Intelligent water conservancy management system and management method based on Internet of Things

Abstract

The invention provides an intelligent water conservancy management system and management method based on the Internet of Things, belongs to the field of intelligent water conservancy, and is used for solving the problem that the reliability of monitoring equipment of an intelligent water conservancy system in the related technology is difficult to guarantee. On the basis of the pre-constructed machine learning model, the inspection necessity data of each inspection area is calculated and determined, and then the inspection strategy is determined according to the inspection necessity data, so that the inspection frequency of each inspection area is intelligently and reasonably determined on the basis of ensuring the reliability of the intelligent water conservancy system; and the operation and maintenance cost of the intelligent water conservancy system is reduced.

Description

Technical Field

[0001] The present application relates to the field of smart water conservancy, and in particular to a smart water conservancy management system and management method based on the Internet of Things. Background Art

[0002] Smart water conservancy is a system that uses modern information technology, such as the Internet of Things, big data, and cloud computing, to manage water resources intelligently. It collects, analyzes, and processes water conservancy data to achieve efficient use of water resources, protection of the water environment, and early warning of water disasters. In the smart water conservancy system, the number of various monitoring devices at the perception layer is the largest, and a large number of monitoring devices need to be deployed at key water environment monitoring points, causing the monitoring equipment to be greatly affected by natural environmental factors and prone to abnormal failures, making it difficult to ensure the reliability of monitoring data. Summary of the invention

[0003] The present application provides a smart water conservancy management system and management method based on the Internet of Things, which is conducive to ensuring the reliability of monitoring data.

[0004] In a first aspect, the present application provides a smart water management method based on the Internet of Things. The method comprises: Obtaining historical inspection records, wherein the historical inspection records correspond to an inspection area, and the historical inspection records include the inspection start time, the inspection end time, and abnormal fault records of the monitoring equipment; Determine inspection necessity data for each inspection area based on the historical inspection records; Determining inspection strategy information according to the inspection necessity data; The inspection necessity data of each inspection area determined according to the historical inspection records includes: , Where w is the inspection necessity data, n is the number of abnormal fault records in the historical inspection records within the preset time period, It is the time between the inspection end time of the most recent historical inspection record and the current time. It is the time between the inspection start time and the inspection end time of the most recent historical inspection record. is the preset inspection time for the relative inspection area, z is the pre-acquired equipment impact coefficient, is the lower limit of the preset time range, The upper limit of the preset time range.

[0005] By adopting the above technical solution, the inspection strategy can be intelligently determined based on the historical inspection records of the monitoring equipment in the smart water conservancy system, which is beneficial to intelligently and reasonably determine the inspection frequency of each inspection area on the basis of ensuring the reliability of the smart water conservancy system, and is beneficial to reducing the operation and maintenance costs of the smart water conservancy system.

[0006] Furthermore, the method for obtaining the equipment influence coefficient includes: Suppose there are m monitoring devices in the inspection area. Define the monitoring devices that are normal in the most recent historical inspection record as the first type of devices, and the monitoring devices that are abnormal as the second type of devices. Suppose the first type of devices have The second type of equipment has , then , In the formula, represents the position influence factor of the i-th first-category device, where the position influence factor is positively correlated to the distance between the first-category device and the spatial position of each second-category device, represents the type influence factor of the i-th first-category device, where the type influence factor is positively correlated to the similarity between the first-category device and the device type of each second-category device, Indicates the time between the last operation and maintenance of the i-th first-category equipment and the current time.

[0007] Furthermore, the method for obtaining the position influence factor includes: , In the formula, represents the distance between the spatial positions of the i-th first-category device and the j-th second-category device, Indicates taking The sum of the first preset number of smaller values, All are preset constants greater than zero.

[0008] Furthermore, the method for obtaining the type influencing factor includes: , In the formula, represents the similarity between the device types of the i-th first-category device and the j-th second-category device, Indicates taking The sum of the second preset number of larger values, All are preset constants greater than zero.

[0009] Further, the determining the inspection strategy information according to the inspection necessity data includes: Sort the inspection areas from high to low according to the inspection necessity data; Determine the inspection task list based on the preset task duration and the preset inspection duration corresponding to each inspection area; Determining the inspection task list includes: selecting the inspection areas in order from high to low according to the inspection necessity data and placing them into set A, and calculating the sum of the preset inspection durations of all the inspection areas in set A; if the sum of all the preset inspection durations in set A is not greater than the preset task duration, the inspection area is allowed to be placed in set A; otherwise, the inspection area is not allowed to be placed in set A; all inspection areas are traversed to determine the inspection areas in set A as the inspection areas in the inspection task list.

[0010] In the second aspect, the present application provides a smart water management system based on the Internet of Things. The system includes: An acquisition module is used to acquire historical inspection records, wherein the historical inspection records correspond to an inspection area, and the historical inspection records include the inspection start time, the inspection end time, and abnormal fault records of the monitoring equipment; a calculation module, configured to determine inspection necessity data for each inspection area according to the historical inspection records; and A management module, used to determine inspection strategy information according to the inspection necessity data; The computing module is further configured to: , Where w is the inspection necessity data, n is the number of abnormal fault records in the historical inspection records within the preset time period, It is the time between the inspection end time of the most recent historical inspection record and the current time. It is the time between the inspection start time and the inspection end time of the most recent historical inspection record. is the preset inspection time for the relative inspection area, z is the pre-acquired equipment impact coefficient, is the lower limit of the preset time range, The upper limit of the preset time range.

[0011] Furthermore, the calculation module is further configured to: Suppose there are m monitoring devices in the inspection area. Define the monitoring devices that are normal in the most recent historical inspection record as the first type of devices, and the monitoring devices that are abnormal as the second type of devices. Suppose the first type of devices have The second type of equipment has , then , In the formula, represents the position influence factor of the i-th first-category device, where the position influence factor is positively correlated to the distance between the first-category device and the spatial position of each second-category device, represents the type influence factor of the i-th first-category device, where the type influence factor is positively correlated to the similarity between the first-category device and the device type of each second-category device, Indicates the time between the last operation and maintenance of the i-th first-category equipment and the current time.

[0012] Furthermore, the calculation module is further configured to: , In the formula, represents the distance between the spatial positions of the i-th first-category device and the j-th second-category device, Indicates taking The sum of the first preset number of smaller values, All are preset constants greater than zero.

[0013] Furthermore, the calculation module is further configured to: , In the formula, represents the similarity between the device types of the i-th first-category device and the j-th second-category device, Indicates taking The sum of the second preset number of larger values, All are preset constants greater than zero.

[0014] Furthermore, the management module is further configured to: Sort the inspection areas from high to low according to the inspection necessity data; Determine the inspection task list based on the preset task duration and the preset inspection duration corresponding to each inspection area; Determining the inspection task list includes: selecting the inspection areas in order from high to low according to the inspection necessity data and placing them into set A, and calculating the sum of the preset inspection durations of all the inspection areas in set A; if the sum of all the preset inspection durations in set A is not greater than the preset task duration, the inspection area is allowed to be placed in set A; otherwise, the inspection area is not allowed to be placed in set A; all inspection areas are traversed to determine the inspection areas in set A as the inspection areas in the inspection task list.

[0015] In summary, this application at least has the following beneficial effects: Provided are a smart water conservancy management system and a management method based on the Internet of Things, which are conducive to the operation and maintenance of the smart water conservancy system at a relatively low cost and ensure the reliability of the smart water conservancy system.

[0016] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein: Figure 1 A flowchart of a smart water management method based on the Internet of Things in an embodiment of the present application is shown; Figure 2 A block diagram of an Internet of Things-based smart water management system in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0020] The present application provides a smart water conservancy management system and management method based on the Internet of Things, which can reasonably determine the inspection timing of monitoring equipment in each inspection area of ​​the smart water conservancy system, so as to ensure the reliability of the smart water conservancy system at a lower cost.

[0021] In a first aspect, the present application provides a smart water conservancy management method based on the Internet of Things. The method can be executed by a server of a smart water conservancy system.

[0022] Figure 1 A flow chart of a smart water management method based on the Internet of Things in an embodiment of the present application is shown.

[0023] Reference Figure 1 , the method specifically comprises the following steps: S110: Obtain historical inspection records.

[0024] The historical inspection record corresponds to an inspection area, and the historical inspection record includes the inspection start time, the inspection end time and the abnormal fault record of the monitoring equipment.

[0025] Specifically, historical inspection records are records kept by operation and maintenance personnel after they inspect the monitoring equipment of the smart water conservancy system. The inspection work is based on the inspection area. The area of ​​different inspection areas and the types and quantities of monitoring equipment contained therein are different. Operation and maintenance personnel need to record the inspection start time and inspection end time of each inspection area, and need to record the abnormal fault conditions of the monitoring equipment during the inspection, as well as perform maintenance work such as simple adjustment and correction of some monitoring equipment.

[0026] S120: Determine inspection necessity data for each inspection area according to the historical inspection records.

[0027] The method of this step specifically includes: , Where w is the inspection necessity data, n is the number of abnormal fault records in the historical inspection records within the preset time period, It is the time between the inspection end time of the most recent historical inspection record and the current time. It is the time between the inspection start time and the inspection end time of the most recent historical inspection record. is the preset inspection time for the relative inspection area, z is the pre-acquired equipment impact coefficient, is the lower limit of the preset time range, The upper limit of the preset time range.

[0028] The preset inspection duration reflects the duration of normal movement within the inspection area and routine inspection work such as observing, taking pictures, and reading data of monitoring equipment. In actual inspection work, operation and maintenance personnel may be involved in careful and repeated observation of monitoring equipment and necessary adjustments and corrections, etc. Therefore, the actual inspection duration (the time between the start time and the end time of the inspection) is generally longer than the preset inspection duration.

[0029] In the method of this step, the method for obtaining the equipment influence coefficient includes: Suppose there are m monitoring devices in the inspection area. Define the monitoring devices that are normal in the most recent historical inspection record as the first type of devices, and the monitoring devices that are abnormal as the second type of devices. Suppose the first type of devices have The second type of equipment has , then , In the formula, represents the position influence factor of the i-th first-category device, where the position influence factor is positively correlated to the distance between the first-category device and the spatial position of each second-category device, represents the type influence factor of the i-th first-category device, where the type influence factor is positively correlated to the similarity between the first-category device and the device type of each second-category device, Indicates the time between the last operation and maintenance of the i-th first-category equipment and the current time.

[0030] The method for obtaining the position influence factor includes: , In the formula, represents the distance between the spatial positions of the i-th first-category device and the j-th second-category device, Indicates taking The sum of the first preset number of smaller values, All are preset constants greater than zero.

[0031] The method for obtaining the type influencing factor includes: , In the formula, represents the similarity between the device types of the i-th first-category device and the j-th second-category device, Indicates taking The sum of the second preset number of larger values, are all preset constants greater than zero. The similarity between two monitoring devices can be determined by the model of the monitoring device. The model of the monitoring device can generally accurately reflect its brand, type, function, specification and other detailed attributes. The model similarity comparison table can be pre-configured based on data experience and human suggestions to determine the similarity between all monitoring device models. The more similar the two monitoring devices are, the higher the similarity value.

[0032] S130: Determine inspection strategy information according to the inspection necessity data.

[0033] The method of this step specifically includes: sorting the inspection areas from high to low according to the inspection necessity data; determining the inspection task list based on the preset task duration and the preset inspection duration corresponding to each inspection area; determining the inspection task list includes: taking the inspection areas in order from high to low according to the inspection necessity data and placing them into set A, and calculating the sum of the preset inspection durations of all inspection areas in set A, if the sum of all the preset inspection durations in set A is not greater than the preset task duration, the inspection area is allowed to be placed in set A, otherwise the inspection area is not allowed to be placed in set A, traversing all inspection areas, and determining that the inspection areas in set A are the inspection areas in the inspection task list.

[0034] By adopting this method, inspection areas with higher inspection necessity data can be preferentially selected. When inspection areas with higher inspection necessity data cannot be selected due to the total preset task duration, inspection areas with lower inspection necessity data can still be taken backward to see whether they can be selected while meeting the total preset task duration. In this way, the inspection task list determined after traversal can not only include as many inspection areas with higher inspection necessity as possible, but also effectively utilize all the preset task durations.

[0035] It should be understood that the inspection task list generally cannot obtain the inspection area with inspection necessity data of 0, but in order to avoid obtaining it, a condition can also be set so that the inspection task list only selects the inspection area with inspection necessity data not 0. If there is inspection necessity data in the inspection area outside the inspection task list, the value is , you can generate prompt information.

[0036] The method may be executed based on a preset cycle.

[0037] To sum up, this method can realize the intelligent determination of the inspection strategy of the monitoring equipment in the smart water conservancy system, which is conducive to ensuring the reliability of the smart water conservancy system and saving the operation and maintenance cost of the smart water conservancy system as much as possible through timely inspections.

[0038] This method is essentially a machine learning model, which can be trained using human feedback (such as the number or proportion of monitoring devices with abnormal failures in the inspection area compared to the target range) during long-term use. The parameters that can be trained are as follows: , preset duration, preset cycle, etc., this method can more accurately determine the inspection necessity data of each inspection area after long-term execution and feedback training, and then can reasonably determine the inspection timing of each inspection area.

[0039] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0040] On the second aspect, the present application provides a smart water management system based on the Internet of Things.

[0041] Figure 2 A block diagram of an Internet of Things-based smart water management system in an embodiment of the present application is shown.

[0042] Reference Figure 2 , the system comprising: The acquisition module 210 is used to acquire historical inspection records, each of which corresponds to an inspection area, and includes an inspection start time, an inspection end time, and abnormal fault records of the monitoring equipment; A calculation module 220, configured to determine inspection necessity data for each inspection area according to the historical inspection records; and A management module 230, configured to determine inspection strategy information according to the inspection necessity data; The calculation module 220 is further configured to: , Where w is the inspection necessity data, n is the number of abnormal fault records in the historical inspection records within the preset time period, It is the time between the inspection end time of the most recent historical inspection record and the current time. It is the time between the inspection start time and the inspection end time of the most recent historical inspection record. is the preset inspection time for the relative inspection area, z is the pre-acquired equipment impact coefficient, is the lower limit of the preset time range, The upper limit of the preset time range.

[0043] Furthermore, the calculation module 220 is further configured to: Suppose there are m monitoring devices in the inspection area. Define the monitoring devices that are normal in the most recent historical inspection record as the first type of devices, and the monitoring devices that are abnormal as the second type of devices. Suppose the first type of devices have The second type of equipment has , then , In the formula, represents the position influence factor of the i-th first-category device, where the position influence factor is positively correlated to the distance between the first-category device and the spatial position of each second-category device, represents the type influence factor of the i-th first-category device, where the type influence factor is positively correlated to the similarity between the first-category device and the device type of each second-category device, Indicates the time between the last operation and maintenance of the i-th first-category equipment and the current time.

[0044] Furthermore, the calculation module 220 is further configured to: , In the formula, represents the distance between the spatial positions of the i-th first-category device and the j-th second-category device, Indicates taking The sum of the first preset number of smaller values, All are preset constants greater than zero.

[0045] Furthermore, the calculation module 220 is further configured to: , In the formula, represents the similarity between the device types of the i-th first-category device and the j-th second-category device, Indicates taking The sum of the second preset number of larger values, All are preset constants greater than zero.

[0046] Furthermore, the management module 230 is further configured to: Sort the inspection areas from high to low according to the inspection necessity data; Determine the inspection task list based on the preset task duration and the preset inspection duration corresponding to each inspection area; Determining the inspection task list includes: selecting the inspection areas in order from high to low according to the inspection necessity data and placing them into set A, and calculating the sum of the preset inspection durations of all the inspection areas in set A; if the sum of all the preset inspection durations in set A is not greater than the preset task duration, the inspection area is allowed to be placed in set A; otherwise, the inspection area is not allowed to be placed in set A; all inspection areas are traversed to determine the inspection areas in set A as the inspection areas in the inspection task list.

[0047] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0048] In summary, this application at least has the following beneficial effects: Provided are a smart water conservancy management system and a management method based on the Internet of Things, which are conducive to the operation and maintenance of the smart water conservancy system at a relatively low cost and ensure the reliability of the smart water conservancy system.

[0049] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A smart water conservancy management method based on the Internet of Things, characterized in that: include: Obtaining historical inspection records, wherein the historical inspection records correspond to an inspection area, and the historical inspection records include the inspection start time, the inspection end time, and abnormal fault records of the monitoring equipment; Determine inspection necessity data for each inspection area based on the historical inspection records; Determining inspection strategy information according to the inspection necessity data; The inspection necessity data of each inspection area determined according to the historical inspection records includes: ， Where w is the inspection necessity data, n is the number of abnormal fault records in the historical inspection records within the preset time period, It is the time between the inspection end time of the most recent historical inspection record and the current time. It is the time between the inspection start time and the inspection end time of the most recent historical inspection record. is the preset inspection time for the relative inspection area, z is the pre-acquired equipment impact coefficient, is the lower limit of the preset time range, The upper limit of the preset time range.

2. The method according to claim 1, characterized in that The method for obtaining the equipment influence coefficient includes: Suppose there are m monitoring devices in the inspection area. Define the monitoring devices that are normal in the most recent historical inspection record as the first type of devices, and the monitoring devices that are abnormal as the second type of devices. Suppose the first type of devices have The second type of equipment has , then ， In the formula, represents the position influence factor of the i-th first-category device, where the position influence factor is positively correlated to the distance between the first-category device and the spatial position of each second-category device, represents the type influence factor of the i-th first-category device, where the type influence factor is positively correlated to the similarity between the first-category device and the device type of each second-category device, Indicates the time between the last operation and maintenance of the i-th first-category equipment and the current time.

3. The method according to claim 2, characterized in that The method for obtaining the position influence factor includes: ， In the formula, represents the distance between the spatial positions of the i-th first-category device and the j-th second-category device, Indicates taking The sum of the first preset number of smaller values, All are preset constants greater than zero.

4. The method according to claim 2, characterized in that: The method for obtaining the type influencing factor includes: ， In the formula, represents the similarity between the device types of the i-th first-category device and the j-th second-category device, Indicates taking The sum of the second preset number of larger values, All are preset constants greater than zero.

5. The method according to claim 1, characterized in that The determining of inspection strategy information according to the inspection necessity data includes: Sort the inspection areas from high to low according to the inspection necessity data; Determine the inspection task list based on the preset task duration and the preset inspection duration corresponding to each inspection area; Determining the inspection task list includes: selecting the inspection areas in order from high to low according to the inspection necessity data and placing them into set A, and calculating the sum of the preset inspection durations of all the inspection areas in set A; if the sum of all the preset inspection durations in set A is not greater than the preset task duration, the inspection area is allowed to be placed in set A; otherwise, the inspection area is not allowed to be placed in set A; all inspection areas are traversed to determine the inspection areas in set A as the inspection areas in the inspection task list.

6. A smart water management system based on the Internet of Things, characterized in that: include: An acquisition module (210) is used to acquire a historical inspection record, wherein the historical inspection record corresponds to an inspection area, and the historical inspection record includes an inspection start time, an inspection end time, and an abnormal fault record of a monitoring device; A calculation module (220), configured to determine inspection necessity data for each inspection area according to the historical inspection records; as well as A management module (230), configured to determine inspection strategy information according to the inspection necessity data; The calculation module (220) is further configured to: ， Where w is the inspection necessity data, n is the number of abnormal fault records in the historical inspection records within the preset time period, It is the time between the inspection end time of the most recent historical inspection record and the current time. It is the time between the inspection start time and the inspection end time of the most recent historical inspection record. is the preset inspection time for the relative inspection area, z is the pre-acquired equipment impact coefficient, is the lower limit of the preset time range, The upper limit of the preset time range.

7. The system according to claim 6, characterized in that The calculation module (220) is further configured to: Suppose there are m monitoring devices in the inspection area. Define the monitoring devices that are normal in the most recent historical inspection record as the first type of devices, and the monitoring devices that are abnormal as the second type of devices. Suppose the first type of devices have The second type of equipment has , then ， In the formula, represents the position influence factor of the i-th first-category device, where the position influence factor is positively correlated to the distance between the first-category device and the spatial position of each second-category device, represents the type influence factor of the i-th first-category device, where the type influence factor is positively correlated to the similarity between the first-category device and the device type of each second-category device, Indicates the time between the last operation and maintenance of the i-th first-category equipment and the current time.

8. The system according to claim 7, characterized in that The calculation module (220) is further configured to: ， In the formula, represents the distance between the spatial positions of the i-th first-category device and the j-th second-category device, Indicates taking The sum of the first preset number of smaller values, All are preset constants greater than zero.

9. The system according to claim 7, characterized in that The calculation module (220) is further configured to: ， In the formula, represents the similarity between the device types of the i-th first-category device and the j-th second-category device, Indicates taking The sum of the second preset number of larger values, All are preset constants greater than zero.

10. The system according to claim 6, characterized in that The management module (230) is further configured to: Sort the inspection areas from high to low according to the inspection necessity data; Determine the inspection task list based on the preset task duration and the preset inspection duration corresponding to each inspection area; Determining the inspection task list includes: selecting the inspection areas in order from high to low according to the inspection necessity data and placing them into set A, and calculating the sum of the preset inspection durations of all the inspection areas in set A; if the sum of all the preset inspection durations in set A is not greater than the preset task duration, the inspection area is allowed to be placed in set A; otherwise, the inspection area is not allowed to be placed in set A; all inspection areas are traversed to determine the inspection areas in set A as the inspection areas in the inspection task list.

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