Water body monitoring method, device, equipment and storage medium based on cloud platform

Abstract

The present invention belongs to the technical field of the Internet of Things, and discloses a water body monitoring method, device, equipment and storage medium based on a cloud platform. The method includes: when a water body anomaly occurs in the water body to be monitored, obtaining a water body anomaly type corresponding to the sensor data, where the sensor data is collected by a sensor for water body detection in the water body to be monitored; determining a target storage partition corresponding to the water body anomaly type in the Internet of Things cloud platform, and the target storage partition stores sensor data corresponding to the water body anomaly type; retrieving the partition data of the target storage partition; determining an abnormal data type according to the partition data; and sending an alarm message to a user according to the abnormal data type. Through the above method, when a water body anomaly occurs, the corresponding abnormal data type can be quickly queried, and only the data related to the water body anomaly type is presented to the user, while removing redundant data, so as to accurately report an alarm message of the water body anomaly type to the user.

Description

Technical Field

[0001] The present invention relates to the technical field of the Internet of Things, and particularly to a water body monitoring method, device, equipment and storage medium based on a cloud platform. Background Art

[0002] Current water conservancy monitoring technologies are all based on sensors to collect data and transmit it to users for analysis and determination. Moreover, the water body data collected by sensors is only presented to users in a point-like and single manner, and cannot perform combined analysis of multiple data by combining various water body problems. In addition, sensor data is often stored in a local server or memory, which is prone to data redundancy and chaos. When a water body problem occurs, it cannot immediately warn the user and accurately display the abnormal data and related information such as the relevant data types to the user.

[0003] The above content is only used to assist in understanding the technical solution of the present invention, and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of the present invention is to provide a water body monitoring method, device, equipment and storage medium based on a cloud platform, aiming to solve the technical problem that the water body monitoring data storage in the prior art is chaotic and cannot intuitively warn the user.

[0005] To achieve the above purpose, the present invention provides a water body monitoring method based on a cloud platform, and the method includes the following steps:

[0006] When a water body anomaly occurs in the water body to be monitored, obtain the water body anomaly type corresponding to the sensor data, where the sensor data is collected by a sensor for water body detection in the water body to be monitored;

[0007] According to the water body anomaly type, determine a target storage partition in the Internet of Things cloud platform corresponding to the water body anomaly type, where the target storage partition stores the sensor data corresponding to the water body anomaly type;

[0008] Retrieve the partition data of the target storage partition;

[0009] Determine the abnormal data type according to the partition data;

[0010] Send an alarm message to the user according to the abnormal data type.

[0011] Optionally, before determining the target storage partition in the Internet of Things cloud platform corresponding to the water body anomaly type according to the water body anomaly type, it further includes:

[0012] Obtain the sensor configuration information of the sensor for water body detection in the water body to be monitored;

[0013] Bind each sensor to the Internet of Things cloud platform according to the sensor configuration information;

[0014] Determine the sensor data type corresponding to each sensor according to the sensor configuration information;

[0015] Correspondingly store the sensor data collected by each sensor into the storage partition on the Internet of Things cloud platform according to the sensor data type.

[0016] Optionally, the correspondingly storing the sensor data collected by each sensor into the storage partition on the Internet of Things cloud platform according to the sensor data type includes:

[0017] Obtain water body anomaly type information;

[0018] Perform storage partitioning for the Internet of Things cloud platform according to the water body anomaly type information to obtain a number of storage partitions;

[0019] Determine the corresponding relationship between each storage partition and each sensor according to the water body anomaly type information and the sensor configuration information;

[0020] Correspondingly store the sensor data collected by each sensor into the corresponding storage partition according to the corresponding relationship.

[0021] Optionally, the performing storage partitioning for the Internet of Things cloud platform according to the water body anomaly type information to obtain a number of storage partitions includes:

[0022] Determine the number of types of water body anomalies according to the water body anomaly type information;

[0023] Determine the number of partitions according to the number of types of water body anomalies;

[0024] Partition the storage space of the Internet of Things cloud platform according to the number of partitions through the shunt scheduling server of the Internet of Things cloud platform to obtain a number of storage partitions.

[0025] Optionally, the determining the abnormal data type according to the partition data includes:

[0026] Obtain the partition sensor data corresponding to each sensor according to the partition data;

[0027] Retrieve historical sensor data from the Internet of Things cloud platform;

[0028] Determine the abnormal data type according to the historical sensor data and the partition sensor data.

[0029] Optionally, the determining the abnormal data type according to the historical sensor data and the partition sensor data includes:

[0030] Determine the historical partition sensor data corresponding to each partition sensor data according to the historical sensor data;

[0031] Compare each partition sensor data with the corresponding historical partition sensor data to obtain a comparison result;

[0032] When the comparison result shows that the difference between the partition sensor data and the historical partition sensor data is greater than a preset abnormal threshold, determine that the partition sensor data is abnormal data;

[0033] Determine the abnormal data type according to the abnormal data.

[0034] Optionally, sending an alarm message to the user according to the abnormal data type includes:

[0035] Determine the data abnormal sensor according to the abnormal data type;

[0036] Obtain the historical sensor data of the data abnormal sensor from the Internet of Things cloud platform;

[0037] Generate a water body abnormality report according to the historical sensor data;

[0038] Send an alarm message to the user according to the water body abnormality report.

[0039] In addition, to achieve the above object, the present invention also proposes a water body monitoring device based on a cloud platform, and the water body monitoring device based on the cloud platform includes:

[0040] A type acquisition module, configured to obtain a water body abnormality type corresponding to sensor data when a water body abnormality occurs in the water body to be monitored, and the sensor data is collected by a sensor for water body detection in the water body to be monitored;

[0041] A partition determination module, configured to determine a target storage partition corresponding to the water body abnormality type in the Internet of Things cloud platform according to the water body abnormality type, and the target storage partition stores sensor data corresponding to the water body abnormality type;

[0042] A data retrieval module, configured to retrieve the partition data of the target partition;

[0043] A data locking module, configured to determine an abnormal data type according to the partition data;

[0044] An alarm warning module, configured to send an alarm message to the user according to the abnormal data type.

[0045] In addition, to achieve the above object, the present invention also provides a water body monitoring device based on a cloud platform, where the water body monitoring device based on the cloud platform includes: a memory, a processor, and a water body monitoring program based on the cloud platform stored on the memory and executable on the processor. The water body monitoring program based on the cloud platform is configured to implement the steps of the water body monitoring method based on the cloud platform as described above.

[0046] In addition, to achieve the above object, the present invention also provides a storage medium, on which a water body monitoring program based on the cloud platform is stored. When the water body monitoring program based on the cloud platform is executed by a processor, it implements the steps of the water body monitoring method based on the cloud platform as described above.

[0047] When a water body anomaly occurs in the water body to be monitored in the present invention, the water body anomaly type corresponding to the sensor data is obtained, and the sensor data is collected by a sensor for water body detection in the water body to be monitored; according to the water body anomaly type, a target storage partition corresponding to the water body anomaly type in the Internet of Things cloud platform is determined, and the sensor data corresponding to the water body anomaly type is stored in the target storage partition; the partition data of the target storage partition is retrieved; according to the partition data, the anomaly data type is determined; and an alarm message is sent to the user according to the anomaly data type. In this way, it is realized that the sensor data collected by each sensor is stored in partitions based on the Internet of Things cloud platform. Then, when a water body anomaly occurs, the anomaly data type is determined based on the water body anomaly type, and then an alarm is sent to the user based on the anomaly data type, so that when a water body anomaly occurs, the anomaly data type corresponding to the water body anomaly type can be quickly queried, and only the data related to the water body anomaly type is displayed to the user, while the redundant data is removed, thereby accurately reporting the alarm message of the water body anomaly type to the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a schematic structural diagram of a water body monitoring device based on a cloud platform in a hardware operating environment according to an embodiment of the present invention;

[0049] Figure 2 is a schematic flowchart of a first embodiment of the water body monitoring method based on the cloud platform of the present invention;

[0050] Figure 3 is a schematic flowchart of a second embodiment of the water body monitoring method based on the cloud platform of the present invention;

[0051] Figure 4 is a schematic block diagram of a first embodiment of the water body monitoring device based on the cloud platform of the present invention.

[0052] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation mode

[0053] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0054] Refer to Figure 1 , Figure 1 which is a schematic structural diagram of a water body monitoring device based on a cloud platform for the hardware operating environment involved in the solution of the embodiment of the present invention.

[0055] As Figure 1 shown, the water body monitoring device based on the cloud platform may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless-fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0056] Those skilled in the art can understand that Figure 1 the structure shown in

[0057] As Figure 1 shown, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and a water body monitoring program based on the cloud platform.

[0058] In Figure 1In the water body monitoring device based on the cloud platform shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the device of the present invention can be arranged in the water body monitoring device based on the cloud platform. The water body monitoring device based on the cloud platform calls the water body monitoring program stored in the memory 1005 through the processor 1001 and executes the water body monitoring method provided by the embodiments of the present invention.

[0059] Embodiments of the present invention provide a water body monitoring method based on a cloud platform. Refer to Figure 2 , Figure 2 which is a schematic flow chart of the first embodiment of a water body monitoring method based on a cloud platform of the present invention.

[0060] In this embodiment, the water body monitoring method based on the cloud platform includes the following steps:

[0061] Step S10: When a water body anomaly occurs in the water body to be monitored, obtain the water body anomaly type corresponding to the sensor data, and the sensor data is collected by a sensor for water body detection in the water body to be monitored.

[0062] It should be noted that the execution subject of this embodiment is a processor or a controller in the Internet of Things cloud platform, or other devices that can implement this function. This embodiment does not limit this.

[0063] It should be understood that current water conservancy monitoring technologies are all based on sensors collecting data and directly transmitting it to the user for local storage, and then the user analyzes it in combination with the sensor-collected data. Local storage may lead to redundant and chaotic local data. When there is a problem of water body anomaly and data needs to be extracted for analysis, it often results in overly large and chaotic data, making it difficult to directly view, extract, and give an early warning to the user. However, the solution of this embodiment stores the sensor data collected by each sensor in partitions based on the Internet of Things cloud platform. Then, when a water body anomaly occurs, the anomaly data type is determined based on the water body anomaly type, and then an alarm is given to the user based on the anomaly data type. This enables quick query of the anomaly data type corresponding to the water body anomaly type when a water body anomaly occurs, and only shows the data related to the water body anomaly type to the user, while removing the redundant data, so as to accurately report the alarm information of the water body anomaly type to the user.

[0064] In specific implementation, the water body to be monitored can be any water body equipped with sensors, such as: any river, lake, or reservoir, etc.

[0065] It should be noted that obtaining the water body anomaly type corresponding to the sensor data means that after detecting a water body anomaly, the water body anomaly type corresponding to this water body anomaly is obtained, and each water body anomaly type corresponds to multiple types of sensor data. For example, when the water body anomaly type is too high water level, the corresponding sensor data includes water level data, water flow velocity data, etc. The correspondence between the water body anomaly type and the sensor data can be set by the user. One water body anomaly type can correspond to multiple sensor data, and one sensor data can also correspond to multiple water body anomaly data. This embodiment does not limit this.

[0066] It should be understood that the sensor data is collected by sensors for water body detection in the water body to be monitored means that several sensors for water body monitoring are set in the water body to be detected. For example, water level sensors, water flow velocity sensors, water body component sensors, etc. The data collected by the sensors is used as sensor data.

[0067] Step S20: Determine the target storage partition in the Internet of Things cloud platform corresponding to the water body anomaly type according to the water body anomaly type, and the target storage partition stores the sensor data corresponding to the water body anomaly type.

[0068] In a specific implementation, the Internet of Things cloud platform refers to a cloud platform based on the Internet of Things for storing sensor data, and it can be any form of Internet of Things cloud platform. This embodiment does not limit this.

[0069] It should be noted that the target storage partition refers to the storage partition where the sensor data corresponding to the water body anomaly type is stored. Specifically, the storage space in the Internet of Things cloud platform is partitioned into several storage partitions, and different sensor data is stored in each storage partition.

[0070] Step S30: Retrieve the partition data of the target storage partition.

[0071] It should be understood that the partition data of the target storage partition refers to all the data stored in the target storage partition.

[0072] Step S40: Determine the abnormal data type according to the partition data.

[0073] In a specific implementation, the abnormal data type refers to the type of sensor data that appears abnormal among all the partition data under the water body anomaly type determined according to the partition data.

[0074] Furthermore, in order to accurately determine the abnormal data type, step S40 includes: obtaining the partition sensor data corresponding to each sensor according to the partition data; retrieving historical sensor data from the Internet of Things cloud platform; determining the abnormal data type according to the historical sensor data and the partition sensor data.

[0075] It should be noted that obtaining the partition sensor data corresponding to each sensor from the partition data means: extracting the data collected by each sensor from the partition data as the partition sensor data.

[0076] It should be understood that the historical sensor data refers to the historical records of the sensor data collected by each sensor stored in the Internet of Things cloud platform, that is, all the sensor data stored in the Internet of Things cloud platform.

[0077] In a specific implementation, determining the abnormal data type according to the historical sensor data and the partition sensor data means: comparing the partition sensor data of each different type with the corresponding historical partition sensor data to determine the data type with abnormalities.

[0078] In this way, accurate determination of the abnormal data type is achieved through comparison of the historical sensor data.

[0079] Furthermore, in order to determine the abnormal data type from the partition sensor data, the steps of determining the abnormal data type according to the historical sensor data and the partition sensor data include: determining the historical partition sensor data corresponding to each partition sensor data according to the historical sensor data; comparing each partition sensor data with the corresponding historical partition sensor data to obtain a comparison result; when the comparison result shows that the difference between the partition sensor data and the historical partition sensor data is greater than a preset abnormal threshold, determining the partition sensor data as abnormal data; and determining the abnormal data type according to the abnormal data.

[0080] It should be noted that determining the historical partition sensor data corresponding to each partition sensor data according to the historical sensor data means: corresponding each partition sensor data to each historical sensor data so that the partition sensor data and the historical sensor data of the same data type are corresponding.

[0081] It should be understood that comparing each partition sensor data with the corresponding historical partition sensor data to obtain a comparison result means: comparing the corresponding partition sensor data with the historical partition sensor data to obtain a plurality of comparison results.

[0082] In a specific implementation, when the comparison result shows that the difference between the partition sensor data and the historical partition sensor data is greater than a preset abnormal threshold, determining the partition sensor data as abnormal data means: calculating the difference between the average value of each partition sensor data and the historical partition sensor data, and then taking the partition sensor data with the difference greater than the preset abnormal threshold as abnormal data.

[0083] It should be noted that determining the abnormal data type according to the abnormal data means: using the data type corresponding to the abnormal data as the abnormal data type.

[0084] In this way, the accurate calculation of the abnormal data type is achieved, so that the corresponding abnormal data can be accurately displayed to the user, which is more intuitive and fast.

[0085] Step S50: Send an alarm message to the user according to the abnormal data type.

[0086] It should be understood that the alarm message refers to the alarm message generated by combining the abnormal data type, the abnormal data and the water body abnormal type, and is used to display the abnormal data and early warning to the user.

[0087] Further, in order to generate a more detailed water body abnormal report, step S50 includes: determining the data abnormal sensor according to the abnormal data type; obtaining the historical sensor data of the data abnormal sensor from the Internet of Things cloud platform; generating a water body abnormal report according to the historical sensor data; sending an alarm message to the user according to the water body abnormal report.

[0088] In specific implementation, determining the data abnormal sensor according to the abnormal data type means: determining the sensor that collects the corresponding abnormal data according to the abnormal data type as the data abnormal sensor.

[0089] It should be noted that obtaining the historical sensor data of the data abnormal sensor from the Internet of Things cloud platform means: extracting all the data of the data abnormal sensor stored in the Internet of Things cloud platform to obtain the historical sensor data.

[0090] It should be understood that generating a water body abnormal report according to the historical sensor data means: generating a water body abnormal report by combining the historical sensor data with the current abnormal data and abnormal data type.

[0091] In specific implementation, sending an alarm message to the user according to the water body abnormal report means: generating an alarm message according to the water body abnormal report, and the alarm message includes the water body abnormal report and the alarm form. The alarm form can be voice broadcast, information push, or other methods, and this embodiment does not limit this.

[0092] In this way, the accurate generation of the alarm message and the prompt to the user are achieved, so that the user can timely handle the water body abnormality.

[0093] In this embodiment, when water body anomalies occur in the water body to be monitored, the water body anomaly type corresponding to the sensor data is obtained, and the sensor data is collected by sensors for water body detection in the water body to be monitored; according to the water body anomaly type, the target storage partition corresponding to the water body anomaly type in the Internet of Things cloud platform is determined, and the sensor data corresponding to the water body anomaly type is stored in the target storage partition; the partition data of the target storage partition is retrieved; the anomaly data type is determined according to the partition data; and an alarm message is sent to the user according to the anomaly data type. In this way, it is realized that the sensor data collected by each sensor is stored in partitions based on the Internet of Things cloud platform. Then, when water body anomalies occur, the anomaly data type is determined based on the water body anomaly type, and then an alarm is sent to the user based on the anomaly data type, so that when water body anomalies occur, the anomaly data type corresponding to the water body anomaly type can be quickly queried, and only the data related to the water body anomaly type is displayed to the user, while the redundant data is removed, thereby accurately reporting the alarm message of the water body anomaly type to the user.

[0094] Reference Figure 3 , Figure 3 is a schematic flowchart of the second embodiment of a water body monitoring method based on a cloud platform according to the present invention.

[0095] Based on the above first embodiment, before the step S20 of the water body monitoring method based on a cloud platform in this embodiment, it further includes:

[0096] Step S201: Obtain the sensor configuration information of the sensors for water body detection in the water body to be monitored.

[0097] It should be noted that the sensor configuration information includes, but is not limited to, relevant information such as the data types collected by each sensor and the installation locations.

[0098] Step S202: Bind each sensor to the Internet of Things cloud platform according to the sensor configuration information.

[0099] It should be understood that binding each sensor to the Internet of Things cloud platform according to the sensor configuration information means: uploading the sensor configuration information of each sensor to the Internet of Things cloud platform according to the sensor configuration information, so as to bind each sensor to the Internet of Things cloud platform based on Internet of Things technology, so that the sensor data collected by each sensor is directly uploaded to the Internet of Things cloud platform for storage.

[0100] Step S203: Determine the sensor data type corresponding to each sensor according to the sensor configuration information.

[0101] In a specific implementation, determining the sensor data type according to the sensor configuration information means determining the sensor data type corresponding to each sensor according to the sensor configuration information, that is, the data type corresponding to the data collected by each sensor. For example, the sensor data type of the sensor for collecting the water level height is the water level, and the sensor data type of the sensor for collecting the water body transparency is the transparency, etc. The specific types of sensor data types can be set and allocated by the user, and this embodiment does not limit this.

[0102] Step S204: Corresponding to store the sensor data collected by each sensor into the storage partitions on the Internet of Things cloud platform according to the sensor data type.

[0103] It should be noted that corresponding to store the sensor data collected by each sensor into the storage partitions on the Internet of Things cloud platform according to the sensor data type means: First, partition the storage space on the Internet of Things cloud platform, and then determine the corresponding relationship between each storage partition and the sensor, so as to store the sensor data collected by the sensor into the corresponding storage partition.

[0104] Furthermore, in order to be able to store the sensor data in an organized partitioned manner, the step of corresponding to store the sensor data collected by each sensor into the storage partitions on the Internet of Things cloud platform according to the sensor data type includes: obtaining water body anomaly type information; partitioning the Internet of Things cloud platform according to the water body anomaly type information to obtain a number of storage partitions; determining the corresponding relationship between each storage partition and each sensor according to the water body anomaly type information and the sensor configuration information; and corresponding to store the sensor data collected by each sensor into the corresponding storage partition according to the corresponding relationship.

[0105] It should be understood that the water body anomaly type information refers to the relevant information of the number and types of water body anomaly types configured by the user, such as: water level anomaly, water body transparency anomaly, water flow velocity anomaly, etc.

[0106] It should be noted that partitioning the Internet of Things cloud platform according to the water body anomaly type information to obtain a number of storage partitions means: partitioning according to the number of types of water body anomaly types stored in the water body anomaly type information to obtain a number of storage partitions.

[0107] It should be understood that determining the corresponding relationship between each storage partition and each sensor according to the water body anomaly type information and the sensor configuration information means: determining the water body anomaly type corresponding to each storage partition based on the sensor configuration information and the water body anomaly type information, then determining the sensor data corresponding to the water body anomaly type, and thus determining the sensor corresponding to the water body anomaly type, and finally obtaining the corresponding relationship between the storage partition and the sensor.

[0108] In a specific implementation, after determining the correspondence relationship, the sensor data is stored in the corresponding storage partition, so that every time the sensor collects sensor data, it is automatically transmitted to the corresponding storage partition.

[0109] In this way, the sensor data is accurately stored in each storage partition correspondingly, making subsequent calls and views more organized, more convenient, and improving the user experience.

[0110] Further, in order to divide storage partitions in the Internet of Things cloud platform, the steps of performing storage partitioning on the Internet of Things cloud platform according to the water body anomaly type information to obtain a number of storage partitions include: determining the number of types of water body anomalies according to the water body anomaly type information; determining the number of partitions according to the number of types of water body anomalies; partitioning the storage space of the Internet of Things cloud platform according to the number of partitions through the shunt scheduling server of the Internet of Things cloud platform to obtain a number of storage partitions.

[0111] It should be noted that the number of types refers to the number of types of water body anomalies pre-configured, and the specific number of types is subject to user configuration, and this embodiment does not limit this.

[0112] It should be understood that determining the number of partitions according to the number of types of water body anomalies means that after determining the number of types, taking the number of types as the number of partitions.

[0113] In a specific implementation, partitioning the storage space of the Internet of Things cloud platform according to the number of partitions through the shunt scheduling server of the Internet of Things cloud platform to obtain a number of storage partitions means that after determining the number of partitions, partitioning the storage space through the shunt scheduling server on the Internet of Things cloud platform to obtain the number of storage partitions equal to the number of partitions.

[0114] In this way, the storage space of the Internet of Things cloud platform is reasonably partitioned, so that each type of water body anomaly can correspond to a storage partition, making the call and extraction of data more convenient.

[0115] In this embodiment, the sensor configuration information of the sensors for water body detection in the water body to be monitored is obtained; each sensor is bound to the Internet of Things cloud platform according to the sensor configuration information; the sensor data types corresponding to each sensor are determined according to the sensor configuration information; and the sensor data collected by each sensor is correspondingly stored in the storage partition on the Internet of Things cloud platform according to the sensor data type. In this way, partitioned storage of the Internet of Things cloud platform is implemented based on the sensor configuration information of the sensors, making the storage of sensor data more organized and the call and view more convenient and fast.

[0116] In addition, an embodiment of the present invention further provides a storage medium, on which a water body monitoring program based on a cloud platform is stored. When the water body monitoring program based on the cloud platform is executed by a processor, the steps of the water body monitoring method based on the cloud platform as described above are implemented.

[0117] Since this storage medium adopts all the technical solutions of the above-mentioned all embodiments, it has at least all the beneficial effects brought by the technical solutions of the above-mentioned embodiments, which will not be elaborated one by one here.

[0118] Refer to Figure 4 , Figure 4 which is a structural block diagram of the first embodiment of the water body monitoring device based on the cloud platform of the present invention.

[0119] As Figure 4 shown, the water body monitoring device based on the cloud platform proposed by the embodiment of the present invention includes:

[0120] A type acquisition module 10, configured to acquire a water body anomaly type corresponding to sensor data when a water body anomaly occurs in the water body to be monitored, where the sensor data is collected by a sensor for water body detection in the water body to be monitored.

[0121] A partition determination module 20, configured to determine a target storage partition corresponding to the water body anomaly type in the Internet of Things cloud platform according to the water body anomaly type, and the target storage partition stores sensor data corresponding to the water body anomaly type.

[0122] A data retrieval module 30, configured to retrieve the partition data of the target partition.

[0123] A data locking module 40, configured to determine an abnormal data type according to the partition data.

[0124] An alarm warning module 50, configured to send an alarm message to a user according to the abnormal data type.

[0125] It should be understood that the above is only for illustration and does not constitute any limitation to the technical solutions of the present invention. In specific applications, those skilled in the art can set according to needs, and the present invention does not make any restrictions on this.

[0126] In this embodiment, when water body anomalies occur in the water body to be monitored, the water body anomaly type corresponding to the sensor data is obtained, and the sensor data is collected by sensors for water body detection in the water body to be monitored; the target storage partition corresponding to the water body anomaly type in the Internet of Things cloud platform is determined according to the water body anomaly type, and the sensor data corresponding to the water body anomaly type is stored in the target storage partition; the partition data of the target storage partition is retrieved; the abnormal data type is determined according to the partition data; and an alarm message is sent to the user according to the abnormal data type. In this way, the sensor data collected by each sensor is stored in partitions based on the Internet of Things cloud platform. Then, when water body anomalies occur, the abnormal data type is determined based on the water body anomaly type, and then an alarm is sent to the user based on the abnormal data type, so that when water body anomalies occur, the abnormal data type corresponding to the water body anomaly type can be quickly queried, and only the data related to the water body anomaly type is displayed to the user, while the redundant data is removed, thereby accurately reporting the alarm message of the water body anomaly type to the user.

[0127] In one embodiment, the partition determination module 20 is further configured to obtain the sensor configuration information of the sensors for water body detection in the water body to be monitored; bind each sensor to the Internet of Things cloud platform according to the sensor configuration information; determine the sensor data type corresponding to each sensor according to the sensor configuration information; and store the sensor data collected by each sensor in the storage partition on the Internet of Things cloud platform corresponding to the sensor data type.

[0128] In one embodiment, the partition determination module 20 is further configured to obtain water body anomaly type information; perform storage partitioning on the Internet of Things cloud platform according to the water body anomaly type information to obtain a plurality of storage partitions; determine the corresponding relationship between each storage partition and each sensor according to the water body anomaly type information and the sensor configuration information; and store the sensor data collected by each sensor in the corresponding storage partition according to the corresponding relationship.

[0129] In one embodiment, the partition determination module 20 is further configured to determine the number of types of water body anomalies according to the water body anomaly type information; determine the number of partitions according to the number of types of water body anomalies; and partition the storage space of the Internet of Things cloud platform into a plurality of storage partitions according to the number of partitions through the shunt scheduling server of the Internet of Things cloud platform.

[0130] In one embodiment, the data locking module 40 is further configured to obtain the partition sensor data corresponding to each sensor according to the partition data; retrieve historical sensor data from the Internet of Things cloud platform; and determine the abnormal data type according to the historical sensor data and the partition sensor data.

[0131] In one embodiment, the data locking module 40 is further configured to determine the historical partition sensor data corresponding to each partition sensor data according to the historical sensor data; compare each partition sensor data with the corresponding historical partition sensor data to obtain a comparison result; when the comparison result shows that the difference between the partition sensor data and the historical partition sensor data is greater than a preset abnormal threshold, determine that the partition sensor data is abnormal data; and determine the abnormal data type according to the abnormal data.

[0132] In one embodiment, the alarm warning module 50 is further configured to determine the data abnormal sensor according to the abnormal data type; obtain the historical sensor data of the data abnormal sensor from the Internet of Things cloud platform; generate a water body abnormal report according to the historical sensor data; and send an alarm message to the user according to the water body abnormal report.

[0133] Since this device adopts all the technical solutions of all the above embodiments, it at least has all the beneficial effects brought by the technical solutions of the above embodiments, which will not be elaborated here one by one.

[0134] It should be noted that the above-described work process is only illustrative and does not limit the protection scope of the present invention. In actual applications, those skilled in the art can select some or all of them according to actual needs to achieve the purpose of the solution of this embodiment, and no limitation is made here.

[0135] In addition, for the technical details not described in detail in this embodiment, reference can be made to the water body monitoring method based on the cloud platform provided in any embodiment of the present invention, which will not be elaborated here.

[0136] In addition, it should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or system including that element.

[0137] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0138] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, 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 is stored in a storage medium (such as a read-only memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0139] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. All equivalent structural or equivalent process transformations made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, are equally included in the patent protection scope of the present invention.

Claims

1. A water body monitoring method based on a cloud platform, characterized in that The water body monitoring method of the cloud platform includes: When water body anomalies occur in the water body to be monitored, obtain the water body anomaly type corresponding to the sensor data, where the sensor data is collected by sensors for water body detection in the water body to be monitored; Obtain the sensor configuration information of the sensors for water body detection in the water body to be monitored; Bind each sensor to the Internet of Things cloud platform according to the sensor configuration information; Determine the sensor data types corresponding to each sensor according to the sensor configuration information; Obtain water body anomaly type information; Perform storage partitioning on the Internet of Things cloud platform according to the water body anomaly type information to obtain a number of storage partitions; Determine the corresponding relationship between each storage partition and each sensor according to the water body anomaly type information and the sensor configuration information; Correspondingly store the sensor data collected by each sensor into the corresponding storage partition according to the corresponding relationship; Determine the target storage partition corresponding to the water body anomaly type in the Internet of Things cloud platform according to the water body anomaly type, and the target storage partition stores the sensor data corresponding to the water body anomaly type; Retrieve the partition data of the target storage partition; Determine the abnormal data type according to the partition data; Send an alarm message to the user according to the abnormal data type.

2. The method according to claim 1, characterized in that, The performing storage partitioning on the Internet of Things cloud platform according to the water body anomaly type information to obtain a number of storage partitions includes: Determine the number of types of water body anomalies according to the water body anomaly type information; Determine the number of partitions according to the number of types of water body anomalies; Partition the storage space of the Internet of Things cloud platform into the number of partitions according to the number of partitions through the shunt scheduling server of the Internet of Things cloud platform to obtain a number of storage partitions.

3. The method according to claim 1, wherein The determining the abnormal data type according to the partition data includes: Obtain the partition sensor data corresponding to each sensor according to the partition data; Retrieve historical sensor data from the Internet of Things cloud platform; Determine the abnormal data type according to the historical sensor data and the partition sensor data.

4. The method according to claim 3, characterized in that, The determining the abnormal data type according to the historical sensor data and the partition sensor data includes: Determine the historical partition sensor data corresponding to each partition sensor data according to the historical sensor data; Compare each partition sensor data with the corresponding historical partition sensor data to obtain a comparison result; When the comparison result is that the difference between the partition sensor data and the historical partition sensor data is greater than a preset abnormal threshold, determine the partition sensor data as abnormal data; Determine the abnormal data type according to the abnormal data.

5. The method according to any one of claims 1 to 4, characterized in that The sending an alarm message to the user according to the abnormal data type includes: Determine the data abnormal sensor according to the abnormal data type; Obtain the historical sensor data of the data abnormal sensor from the Internet of Things cloud platform; Generate a water body anomaly report according to the historical sensor data; Send an alarm message to the user according to the water body anomaly report.

6. A water body monitoring device based on a cloud platform, characterized in that, The water body monitoring device based on the cloud platform includes: A type acquisition module, configured to acquire a water body anomaly type corresponding to sensor data when a water body anomaly occurs in the water body to be monitored, where the sensor data is collected by a sensor for water body detection in the water body to be monitored; A partition determination module, configured to determine a target storage partition corresponding to the water body anomaly type in the Internet of Things cloud platform according to the water body anomaly type, where the target storage partition stores sensor data corresponding to the water body anomaly type; A data retrieval module, configured to retrieve partition data of the target partition; A data locking module, configured to determine an anomaly data type according to the partition data; An alarm warning module, configured to send an alarm message to a user according to the anomaly data type; The partition determination module is further configured to acquire sensor configuration information of a sensor for water body detection in the water body to be monitored; bind each sensor to the Internet of Things cloud platform according to the sensor configuration information; determine a sensor data type corresponding to each sensor according to the sensor configuration information; acquire water body anomaly type information; perform storage partitioning for the Internet of Things cloud platform according to the water body anomaly type information to obtain a plurality of storage partitions; determine a corresponding relationship between each storage partition and each sensor according to the water body anomaly type information and the sensor configuration information; and store the sensor data collected by each sensor into the corresponding storage partition according to the corresponding relationship.

7. A water body monitoring device based on a cloud platform, characterized in that The device includes: a memory, a processor, and a cloud platform-based water body monitoring program stored on the memory and executable on the processor, where the cloud platform-based water body monitoring program is configured to implement the cloud platform-based water body monitoring method according to any one of claims 1 to 5.

8. A storage medium, characterized in that, A cloud platform-based water body monitoring program is stored on the storage medium, and when the cloud platform-based water body monitoring program is executed by a processor, it implements the cloud platform-based water body monitoring method according to any one of claims 1 to 5.

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