A data acquisition method, device and gateway

Abstract

The application discloses a data acquisition method and device and a gateway. The method is applied to the gateway and comprises the following steps: detecting that a sensor is accessed; acquiring and configuring a detection range of the sensor based on cloud big data, wherein the cloud big data stores a sensor type, detection range association information and a corresponding relationship of the detection range; and dynamically adjusting a current detection range of the sensor according to an actual operation condition of the sensor. The application is based on big data, comprehensively configures the detection range of the sensor on the gateway side under multiple conditions, and dynamically adjusts the acquisition precision in a targeted manner. Compared with fixed range acquisition, the application is more intelligent, the acquisition precision is higher, the accuracy of data acquisition of the configuration gateway is improved, the reliability and stability of the system are ensured, the engineering configuration speed is also improved, and the problem that the data accuracy of an Internet of Things system in the prior art only depends on one-way data acquisition of terminal sensing equipment and affects the reliability of the system is solved.

Description

Technical Field

[0001] This invention relates to the field of data acquisition technology, and more specifically, to a data acquisition method, apparatus, and gateway. Background Technology

[0002] With the advancement of communication and sensing technologies, the Internet of Things (IoT) has entered an era of rapid development. New-generation information technologies such as data acquisition, sensing and identification, and positioning systems are being widely applied in various fields and industries, including mobile terminals, smart grids, industrial systems, building systems, smart homes, fire protection, and security.

[0003] As a transmission layer device in the Internet of Things (IoT) system, IoT gateways have also experienced explosive growth in recent years. Large and small companies have developed various products, including wired and wireless communication gateways. Most configuration gateways on the market have data acquisition and transmission functions, but the accuracy of the data depends on the end-sensing devices. The gateways themselves do not have data calibration schemes, which means that the gateways do not have an effective way to deal with abnormal data acquisition, thereby reducing the data accuracy of the entire system.

[0004] There is currently no effective solution to the problem that the data accuracy of existing Internet of Things (IoT) systems relies solely on the one-way data collection by end-point sensing devices, which affects system reliability. Summary of the Invention

[0005] This invention provides a data acquisition method, apparatus, and gateway to at least solve the problem in the prior art where the accuracy of data in IoT systems depends solely on the one-way data acquisition by end-sensor devices, thus affecting system reliability.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide a data acquisition method applied to a gateway, comprising:

[0007] A sensor connection has been detected.

[0008] The detection range of the sensor is acquired and configured based on cloud-based big data, wherein the cloud-based big data stores sensor type, detection range association information, and the corresponding relationship of detection range;

[0009] The current detection range of the sensor is dynamically adjusted based on its actual operating conditions.

[0010] Optionally, the detection range of the sensor can be acquired and configured based on big data from the cloud, including:

[0011] Determine the sensor type and detection range association information of the sensor;

[0012] The detection range corresponding to the determined sensor type and detection range association information is obtained from the cloud big data and used as the detection range of the sensor.

[0013] Optionally, the above methods also include:

[0014] If the detection range of the sensor cannot be obtained based on the cloud big data, then data will be collected according to the maximum range of the sensor within a specified time.

[0015] The collected data is uploaded to the cloud in real time, so that the cloud can determine the detection range of the sensor based on the data collected within the specified time period, and the cloud stores the sensor type, detection range association information and the correspondence of detection ranges of the sensor;

[0016] Receive the detection range of the sensor from the cloud for configuration.

[0017] Optionally, the cloud determines the detection range of the sensor based on the data collected within the specified time period, including:

[0018] Determine the minimum and maximum values ​​from the data collected within the specified time period to form a data interval;

[0019] The interval margin value is determined based on the sensor's installation location, climate type, and data collection cycle.

[0020] The detection range of the sensor is obtained based on the data interval and the interval margin value.

[0021] Optionally, the above methods also include:

[0022] Acquire data collected by any connected sensor;

[0023] If the data collected by the sensor exceeds the sensor's detection range, then verification is performed;

[0024] When the verification result indicates that the sensor data is abnormal, an alarm message is output.

[0025] Optional, validation may be performed, including:

[0026] If data is acquired from sensors of the same type and location, and this data falls within the detection range of those sensors, the verification result is considered an anomaly in the sensor data acquired; and / or,

[0027] The sensor collects data at the same time the previous day. If the data is within the sensor's detection range, the verification result is that the sensor's collected data is abnormal.

[0028] Optionally, the above methods also include:

[0029] Displays the current detection range and / or adjustment status of each sensor, wherein the adjustment status indicates whether the sensor's detection range has been adjusted.

[0030] Optionally, the detection range association information includes at least: installation location, climate type, and collection cycle, wherein the installation location includes: altitude and latitude and longitude.

[0031] This invention also provides a data acquisition device applied to a gateway, comprising:

[0032] The detection module is used to detect the presence of a sensor.

[0033] The configuration module is used to acquire and configure the detection range of the sensor based on cloud big data, wherein the cloud big data stores sensor type, detection range association information and the correspondence between detection ranges;

[0034] The adjustment module is used to dynamically adjust the current detection range of the sensor according to the actual operating conditions of the sensor.

[0035] This invention also provides a gateway, including the data acquisition device described in this invention.

[0036] This invention also provides a non-volatile computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in this invention.

[0037] By applying the technical solution of this invention, sensor type, detection range association information, and the corresponding relationship of detection ranges are stored in the cloud big data. When the gateway detects that a sensor has connected, it acquires and configures the detection range of the sensor based on the cloud big data, and then dynamically adjusts the current detection range of the sensor according to the actual operation of the sensor. Based on big data, the detection range of the sensor is configured on the gateway side by comprehensively considering multiple factors such as latitude and longitude, altitude, time, and climate (i.e., detection range association information) and sensor type, and the acquisition accuracy is dynamically adjusted accordingly. Compared with fixed-range acquisition, this is more intelligent, has higher acquisition accuracy, improves the accuracy of data acquisition by the configuration gateway, ensures the reliability and stability of the system, and also improves the engineering configuration speed. This solves the problem in the prior art that the data accuracy of IoT systems depends solely on the unidirectional acquisition of data by the end sensor device, which affects the reliability of the system. Attached Figure Description

[0038] Figure 1 This is a flowchart of the data acquisition method provided in Embodiment 1 of the present invention;

[0039] Figure 2This is a schematic diagram of the gateway provided in Embodiment 2 of the present invention;

[0040] Figure 3 This is the gateway-cloud bidirectional association diagram provided in Embodiment 2 of the present invention;

[0041] Figure 4 This is a diagram illustrating the detection range adjustment process provided in Embodiment 2 of the present invention;

[0042] Figure 5 This is a multi-condition comprehensive judgment logic diagram provided in Embodiment 2 of the present invention;

[0043] Figure 6 This is a schematic diagram of sensor anomaly detection provided in Embodiment 2 of the present invention;

[0044] Figure 7 This is a structural block diagram of the data acquisition device provided in Embodiment 3 of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0046] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products or devices.

[0047] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0048] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0049] The optional embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0050] Example 1

[0051] This embodiment provides a data acquisition method applied to a gateway, which can specifically adjust the data acquisition accuracy of the end sensor, thereby ensuring data acquisition accuracy and system reliability.

[0052] Figure 1 This is a flowchart of the data acquisition method provided in Embodiment 1 of the present invention, as follows: Figure 1 As shown, the method includes the following steps:

[0053] S101, A sensor connection has been detected.

[0054] S102, Based on cloud big data, the detection range of the sensor is acquired and configured, wherein the cloud big data stores sensor type, detection range association information and the correspondence between detection ranges.

[0055] S103, dynamically adjust the current detection range of the sensor according to the actual operating conditions of the sensor.

[0056] Sensor types can be categorized based on the object they detect, such as temperature sensors, humidity sensors, and pressure sensors. The sensor's detection range is the interval less than or equal to its maximum range. Detection range-related information refers to the factors influencing the sensor's detection range, i.e., information affecting the sensor's detection range. This information includes at least: sensor installation location, climate type, and data collection cycle. Installation location includes altitude and latitude / longitude, and data collection cycle includes season and time, such as a yearly collection cycle, the first quarter, June, September 21st, or spring. For example, in cloud-based big data, for temperature sensor 1 installed at location 'a', the detection range corresponding to a yearly collection cycle and the detection range corresponding to a spring collection cycle can be stored.

[0057] For newly connected sensors to the gateway, the sensor's detection range is first configured based on big data. Then, the detection range is dynamically adjusted according to the sensor's actual operating conditions (such as changes in climate and time, actual data collection, and user collection needs). Data collection is based on the sensor's detection range. For example, a temperature sensor with a maximum range of -40 to 120°C and a corresponding output current of 4 to 20 mA might have its detection range adjusted to 0 to 70°C, still corresponding to 4 to 20 mA. This narrower detection range improves detection accuracy.

[0058] This embodiment stores sensor type, detection range association information, and the corresponding relationship of detection ranges in cloud-based big data. When the gateway detects a sensor connection, it acquires and configures the sensor's detection range based on the cloud-based big data. Then, it dynamically adjusts the sensor's current detection range according to the sensor's actual operating conditions. Based on big data, and considering multiple factors such as latitude, longitude, altitude, time, and climate (i.e., detection range association information) and sensor type, the gateway configures the sensor's detection range and dynamically adjusts the acquisition accuracy accordingly. Compared to fixed-range acquisition, this is more intelligent, has higher acquisition accuracy, improves the accuracy of gateway data acquisition, ensures system reliability and stability, and also increases engineering configuration speed. It solves the problem in existing IoT systems where data accuracy relies solely on unidirectional data acquisition from end-point sensors, affecting system reliability.

[0059] Specifically, acquiring and configuring the sensor's detection range based on cloud-based big data includes: determining the sensor type and detection range association information; and acquiring the detection range corresponding to the determined sensor type and detection range association information from the cloud-based big data, using it as the sensor's detection range. This embodiment matches the corresponding detection range in big data based on the sensor type and detection range association information, enabling convenient and rapid configuration of the sensor's detection range.

[0060] If the detection range of the sensor cannot be obtained based on big data in the cloud, data is collected within a specified time according to the maximum range of the sensor; the collected data is uploaded to the cloud in real time so that the cloud can determine the detection range of the sensor based on the data collected within the specified time, and the cloud stores the sensor type, detection range association information and the correspondence of detection ranges of the sensor; the detection range of the sensor is received from the cloud for configuration.

[0061] The specified time period can be determined based on the actual situation; for example, it can be a year or a quarter. A year includes all seasons and various operating conditions, ensuring the comprehensiveness of the data collected within the specified time. Collecting data based on the maximum range also ensures the comprehensiveness of the collected data. Consequently, the sensor detection range configured based on this data is more reasonable.

[0062] Cloud-based big data accumulates gradually. Initially, when cloud-based big data is scarce, sensors connected to the gateway cannot configure their detection range based on cloud-based big data. At this time, by following the steps described above, data is collected within a specified time based on the maximum range, and the cloud determines the sensor's detection range based on this data. This ensures the rationality of the detection range, improves the accuracy of data collection, and stores relevant information in the cloud to form a data model, providing a guarantee for the rapid configuration of other sensors in the future.

[0063] Furthermore, the cloud-based determination of the sensor's detection range based on the data collected within the specified time period includes: determining the minimum and maximum values ​​in the data collected within the specified time period to form a data interval; determining the interval margin value based on the sensor's installation location, climate type, and collection cycle; and obtaining the sensor's detection range based on the data interval and the interval margin value.

[0064] The interval margin value provides a safety margin to account for the influence of factors such as location, time, and climate, ensuring the accuracy of data collection. For example, two pressure sensors located at the same latitude and longitude but different altitudes will have different interval margin values. Similarly, when collecting temperature data, a large difference between the maximum and minimum values ​​indicates a significant temperature difference in the region within a specified time period; accordingly, the interval margin value can be larger to ensure coverage of changes in the on-site environment. Specific interval margin values ​​can be determined based on experience or a pre-defined factor-margin correspondence. For example, if the data interval is [a1, a2], the detection range after considering the interval margin value is [a1-x, a2+X].

[0065] This implementation method can accurately and reliably configure the sensor detection range based on the data collected within a specified time period and the interval margin value, thereby improving the accuracy of data collection.

[0066] This embodiment can also infer whether the data collected by the end sensor is abnormal based on the sensor's detection range. Specifically, the above method may further include: acquiring data collected by any connected sensor; if the data collected by the sensor exceeds the sensor's detection range, performing verification; and outputting an alarm message when the verification result indicates that the sensor's collected data is abnormal.

[0067] This implementation method generates an alarm for abnormalities after verification, which can avoid false alarms and promptly notify engineering personnel to inspect and repair the equipment.

[0068] Further, verification is performed, including: acquiring data collected by sensors of the same type and location; if the data is within the detection range of the sensors of the same type and location, the verification result is that the sensor data is abnormal; and / or, acquiring data collected by the sensor at the same time on the previous day; if the data is within the detection range of the sensor, the verification result is that the sensor data is abnormal.

[0069] In theory, multiple sensors of the same type and location have similar detection ranges, and the data they collect should be equal or similar. If sensor A collects data outside its detection range, but sensor B of the same type and location collects data within its detection range, it indicates that sensor B is functioning correctly, and therefore sensor A's data is abnormal. Effective verification can be achieved using data collected from sensors of the same type and location.

[0070] If there is no significant change in the sensor's detection range, the data collected by the sensor now should theoretically be similar to the data collected at the same time the previous day. If the data collected by the sensor at the same time the previous day is within the sensor's detection range, it indicates that the currently collected data is abnormal. Therefore, using data collected by the same sensor at the same time can achieve effective verification.

[0071] This implementation method can further confirm abnormal data conditions through verification, thus avoiding false alarms.

[0072] In an optional implementation, the current detection range and / or adjustment status of each sensor can also be displayed, wherein the adjustment status indicates whether the sensor's detection range has been adjusted. This facilitates viewing by relevant personnel. Specifically, this can be displayed via indicator lights, digital tubes, or a display screen. Furthermore, the number of adjustments can be recorded to ensure successful adjustment.

[0073] Example 2

[0074] The data acquisition method described above will be illustrated below with reference to a specific embodiment. However, it is worth noting that this specific embodiment is only for better illustration of this application and does not constitute an improper limitation of this application. The same or corresponding terminology used in the above embodiment will not be repeated in this embodiment.

[0075] like Figure 2 The diagram shows a gateway, which includes: I / O interface 1, bus interface 2, wireless communication component 3, and Ethernet interface 4. I / O interface 1 and bus interface 2 are used to receive data collected by the end sensors. However, the gateway is not limited to these two types of interfaces; it can also use other interfaces to communicate with the end sensors. The gateway has wireless communication capabilities. Wireless communication component 3 is used for wireless communication with cloud-based big data platforms and servers, while Ethernet interface 4 is used for wired communication with host or slave computers.

[0076] Initially, when cloud data was scarce, the gateway collected sensor values. Based on the collected data, the sensor detection range was determined. The cloud stored sensor type, detection range correlation information, and the corresponding relationships between detection ranges, forming a data model. Subsequently, based on the large amount of data in the cloud, the detection range of relevant sensors could be adjusted downwards. For example... Figure 3The diagram shows a bidirectional connection between the gateway and the cloud. The gateway is configured to connect to different types of end sensors (sensor A to sensor N). In the initial stage, the gateway collects data for each sensor within a certain time based on the sensor's maximum range. After the basic data collection, the detection range of the sensor is adjusted. The detection ranges of sensors A to N are A1 to A2, B1 to B2, C1 to C2...N1 to N2, respectively, to improve the gateway's detection accuracy by utilizing big data.

[0077] Specifically, the process of adjusting the sensor's detection range through basic data acquisition, as described above, is... Figure 4 As shown, for end sensor A, data is collected on a one-year cycle based on the maximum range of sensor A. A certain amount of data is collected in the cloud, forming a data range a1 to a2. After intervention by factors such as time cycle, installation location, and climate type, the range margin is adjusted a1±x to a2±X to obtain the detection range A1 to A2. The port detection range of the corresponding sensor on the configuration gateway is adapted and adjusted. The gateway then adjusts the sensor detection accuracy downwards, and other sensors are adjusted in the same way. For example, the temperature sensor has a range of -10 to 100℃, corresponding to 4 to 20mA. After the gateway adjusts the sensor downwards, the detection range becomes 0 to 70℃, still corresponding to 4 to 20mA. The detection range is reduced, but the detection accuracy is improved.

[0078] Once a certain amount of data has accumulated in the cloud, the detection range of newly connected sensors can be directly configured based on the big data in the cloud. For example... Figure 5 The diagram illustrates a multi-condition integrated judgment logic. The cloud database stores sensor type, detection range association information, and the corresponding relationships between detection ranges, along with factors such as altitude, latitude and longitude, climate, season, and other related influencing factors. Combined with sensor type and other conditions, a dynamic accuracy range can be assigned to the gateway. For example, if a temperature and humidity sensor is installed in Zhuhai, Guangdong, with an annual collection cycle, the collected temperature range can be assigned as 0℃~45℃. With a seasonal collection cycle, taking summer as an example, the collected temperature range could be 20℃~45℃. The gateway adjusts the accuracy of the end-sensor. The dynamic adjustment time period can be selected as year, quarter, month, day, etc. Other sensor types follow the same principle.

[0079] For example, if the latitude and longitude are 23.5° North latitude and 113° East longitude, the altitude is 50m, the current month is June, and the climate is subtropical monsoon climate. Taking a temperature and humidity sensor as an example, the current temperature of the area is a°C and the humidity is b%RH, then the temperature detection range of the temperature and humidity sensor is adjusted to a1~a2°C (a1<a<a2), and the humidity detection range is adjusted to b1~b2%RH (b1<b<b2). The difference a2-a1 is equal to the daily temperature variation value plus a margin value, and the difference b2-b1 is equal to the daily humidity variation value plus a margin value. For example, if the daily temperature variation value is c and the margin value is d, then the temperature range a2-a1=c+d, and the value of d is dynamically adjusted as the value of c changes. For example, a larger c leads to a larger d, so as to ensure that the detection range covers the variation value of the on-site environment. When the values of a and b change with climate and time, the values of a1, a2, b1 and b2 also change accordingly to realize dynamic regulation of acquisition accuracy. Where a2-a1=x and b2-b1=y, it is preferable to keep the values of x and y unchanged to ensure data acquisition with the highest accuracy. Of course, the values of x and y can also be changed according to actual accuracy requirements.

[0080] The adjustment status can be displayed in forms such as indicator lights, digital tubes, and display screens. The adjustment status is used to indicate whether the detection range of the sensor has been adjusted. For example, if the sensor accuracy is not adjusted, the sensor indicator light is red, and it is displayed green after initial adjustment, and the number of adjustments is accumulated and counted to ensure successful adjustment, and so on. For another example, the digital tube uses a status code to indicate whether the accuracy of the sensor has been adjusted, where 1 represents the original range and 2 represents that the adjustment has been completed. If the adjustment status is displayed through a display screen, the current detection range can also be displayed.

[0081] Based on big data, fast and accurate matching of the detection ranges of sensors with similar conditions (such as similar sensor types, installation positions, acquisition cycles, etc.) can be achieved.

[0082] After adjusting the detection range of the sensor based on big data, it is also possible to reversely infer whether the data collected by the end sensor is abnormal, such as Figure 6 shown, which is a schematic diagram of sensor abnormality judgment. The detection range of sensor 1 is a1~a2, and the daily collected values are all within this range. When the value of sensor 1 collected by the gateway is <a1 or >a2, the value can be compared and verified with the value collected by sensor 2 of the same type and at the same position, and / or verified with the value collected by sensor 1 at the same time on the previous day. If all verification results show that the data collected by sensor 1 is abnormal, the gateway will issue an abnormal alarm to notify engineering personnel to detect and maintain the equipment.

[0083] This invention, based on the bidirectional correlation and matching debugging of cloud-based big data and edge-side data acquisition, automatically and accurately configures the sensor detection range, adjusts the sensor acquisition accuracy, effectively adjusts gateway performance, and improves engineering configuration speed and system stability. By combining the dual judgment logic of acquired data and gateway self-verification, it improves the accuracy of gateway data acquisition configuration and enhances system reliability.

[0084] Example 3

[0085] Based on the same inventive concept, this embodiment provides a data acquisition device applied to a gateway, which can be used to implement the data acquisition method described in the above embodiments. This data acquisition device can be implemented through software and / or hardware.

[0086] Figure 7 This is a structural block diagram of the data acquisition device provided in Embodiment 3 of the present invention, as shown below. Figure 7 As shown, the data acquisition device includes:

[0087] Detection module 71 is used to detect the presence of a sensor.

[0088] Configuration module 72 is used to acquire and configure the detection range of the sensor based on cloud big data, wherein the cloud big data stores sensor type, detection range association information and the correspondence between detection ranges;

[0089] The adjustment module 73 is used to dynamically adjust the current detection range of the sensor according to the actual operating conditions of the sensor.

[0090] Optionally, configuration module 72 includes:

[0091] A determining unit is used to determine the sensor type and detection range association information of the sensor;

[0092] The acquisition unit is used to acquire the detection range corresponding to the determined sensor type and detection range association information from the cloud big data, and use it as the detection range of the sensor.

[0093] Optionally, the above data acquisition device further includes:

[0094] The data acquisition module is used to acquire data within a specified time according to the maximum range of the sensor if the detection range of the sensor cannot be obtained based on the big data in the cloud.

[0095] The upload module is used to upload the collected data to the cloud in real time, so that the cloud can determine the detection range of the sensor based on the data collected within the specified time period, and the cloud stores the sensor type, detection range association information and the correspondence of detection ranges of the sensor;

[0096] The receiving module is used to receive the detection range of the sensor sent from the cloud for configuration.

[0097] Optionally, the cloud is specifically used for: determining the minimum and maximum values ​​in the data collected within the specified time period to form a data interval; determining the interval margin value according to the sensor's installation location, climate type, and collection cycle; and obtaining the sensor's detection range based on the data interval and the interval margin value.

[0098] Optionally, the above data acquisition device further includes:

[0099] The acquisition module is used to acquire data collected by any connected sensor;

[0100] The verification module is used to verify the sensor if the data it collects exceeds its detection range.

[0101] The alarm module is used to output alarm information when the verification result indicates that the sensor data is abnormal.

[0102] Optionally, the verification module is specifically used for:

[0103] If data is acquired from sensors of the same type and location, and this data falls within the detection range of those sensors, the verification result is considered an anomaly in the sensor data acquired; and / or,

[0104] The sensor collects data at the same time the previous day. If the data is within the sensor's detection range, the verification result is that the sensor's collected data is abnormal.

[0105] Optionally, the above data acquisition device further includes:

[0106] The display module is used to display the current detection range and / or adjustment status of each sensor, wherein the adjustment status is used to indicate whether the detection range of the sensor has been adjusted.

[0107] Optionally, the detection range association information includes at least: installation location, climate type, and collection cycle, wherein the installation location includes: altitude and latitude and longitude.

[0108] The data acquisition device described above can execute the data acquisition method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in this embodiment can be found in the data acquisition method provided in the embodiments of the present invention.

[0109] Example 4

[0110] This embodiment provides a gateway, including the data acquisition device described in the above embodiment.

[0111] Example 5

[0112] This embodiment provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method described in the above embodiment.

[0113] Example 6

[0114] This embodiment provides a non-volatile computer-readable storage medium on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the above embodiment.

[0115] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0116] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data acquisition method applied to a gateway, characterized in that, include: A sensor connection has been detected. The detection range of the sensor is acquired and configured based on cloud-based big data. The cloud-based big data stores sensor type, detection range association information, and the corresponding relationship of detection ranges. The detection range association information includes at least: installation location, climate type, and data collection cycle. The installation location includes: altitude and latitude and longitude. The current detection range of the sensor is dynamically adjusted based on its actual operating conditions. If the detection range of the sensor cannot be obtained based on the big data in the cloud, data is collected within a specified time according to the maximum range of the sensor; the collected data is uploaded to the cloud in real time so that the cloud can determine the detection range of the sensor based on the data collected within the specified time, and the cloud stores the sensor type, detection range association information and the correspondence of detection ranges of the sensor; the detection range of the sensor is received from the cloud for configuration. The cloud platform determines the detection range of the sensor based on the data collected within the specified time period, including: determining the minimum and maximum values ​​in the data collected within the specified time period to form a data interval; determining the interval margin value based on the sensor's installation location, climate type, and collection cycle; and obtaining the sensor's detection range based on the data interval and the interval margin value.

2. The method according to claim 1, characterized in that, The detection range of the sensor is acquired and configured based on big data from the cloud, including: Determine the sensor type and detection range association information of the sensor; The detection range corresponding to the determined sensor type and detection range association information is obtained from the cloud big data and used as the detection range of the sensor.

3. The method according to any one of claims 1 to 2, characterized in that, Also includes: Acquire data collected by any connected sensor; If the data collected by the sensor exceeds the sensor's detection range, then verification is performed; When the verification result indicates that the sensor data is abnormal, an alarm message is output.

4. The method according to claim 3, characterized in that, The verification process includes: If data is acquired from sensors of the same type and location, and this data falls within the detection range of those sensors, the verification result is considered an anomaly in the sensor data acquired; and / or, The sensor collects data at the same time the previous day. If the data is within the sensor's detection range, the verification result is that the sensor's collected data is abnormal.

5. The method according to any one of claims 1 to 2, characterized in that, Also includes: Displays the current detection range and / or adjustment status of each sensor, wherein the adjustment status indicates whether the sensor's detection range has been adjusted.

6. A data acquisition device applied to a gateway, characterized in that, include: The detection module is used to detect the presence of a sensor. The configuration module is used to acquire and configure the detection range of the sensor based on cloud-based big data. The cloud-based big data stores sensor type, detection range association information, and the corresponding relationship of detection ranges. The detection range association information includes at least: installation location, climate type, and data collection cycle. The installation location includes: altitude and latitude and longitude. An adjustment module is used to dynamically adjust the current detection range of the sensor based on the actual operating conditions of the sensor; The data acquisition module is used to acquire data within a specified time according to the maximum range of the sensor if the detection range of the sensor cannot be obtained based on the big data in the cloud. The upload module is used to upload the collected data to the cloud in real time, so that the cloud can determine the detection range of the sensor based on the data collected within the specified time period, and the cloud stores the sensor type, detection range association information and the correspondence of detection ranges of the sensor; A receiving module is used to receive the detection range of the sensor sent from the cloud for configuration. Specifically, the cloud is used to: determine the minimum and maximum values ​​in the data collected within the specified time period to form a data interval; determine the interval margin value according to the sensor's installation location, climate type, and collection cycle; and obtain the sensor's detection range based on the data interval and the interval margin value.

7. A gateway, characterized in that, include: The data acquisition device according to claim 6.

8. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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