A management method for a 180-degree return waterfall

Abstract

The present invention relates to a management method for a 180-degree return drop well, and belongs to the technical field of drop wells. The present invention obtains water body estimated characteristic data within a preset time in a current target area through a drop well water body data preference model, obtains real-time water body flow data in the target area, identifies abnormal 180-degree return drop wells according to the water body estimated characteristic data within the preset time in the current target area and the real-time water body flow data in the target area, issues an early warning based on the abnormal 180-degree return drop wells, generates early warning information, and generates a related maintenance plan based on the early warning information. The present invention estimates water body data within a predetermined time by constructing a sensor acquisition network, and constructs a preference model through historical data of the drop wells under various climate environments, thereby more accurately identifying abnormal 180-degree return drop wells than the prior art, and formulating a more reasonable maintenance plan.

Description

Technical Field

[0001] The invention relates to the technical field of drop wells, and in particular to a management method for a 180-degree return drop well. Background Art

[0002] A drop well is a building for adjusting the gradient of concealed pipes. It is generally set up at a place with a large height difference on the ground to connect the upper and lower pipe sections. It is mostly made of concrete or tile pipes. In irrigation channels, drop wells are also used to connect the upper and lower canal sections. In drainage pipes, due to the large drop of the pipe, when the normal pipe slope cannot meet the design requirements, an inspection well with an internal pipe drop is adopted to meet the design plan. Because the water flow in the well falls, such a well is called a drop well. Compared with ordinary manholes, drop wells need to eliminate the energy of the fall, and the size of this energy depends on the flow rate of the water flow and the height of the fall. The structure of the drop well has different designs, which is determined by the energy dissipation measures. The bottom structure of the well is generally stronger than that of ordinary manholes. However, in the process of using the 180-degree return drop well, staff are often sent to detect it only after an abnormality occurs, which leads to abnormal drainage and is not conducive to the daily maintenance and management of the drop well. Summary of the invention

[0003] The invention overcomes the shortcomings of the prior art and provides a management method for a 180-degree return waterfall well.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] A first aspect of the present invention provides a management method for a 180-degree return drop well, comprising:

[0006] By setting sensors in the 180-degree return waterfall well, a sensor acquisition network is constructed, and water body data of the 180-degree return waterfall well is obtained through the sensor acquisition network, and water body characteristic data based on time series under various meteorological characteristics is constructed;

[0007] Based on the water body characteristic data based on the time series under the meteorological characteristics, a water body data preference model for a drop well is constructed, and the estimated water body characteristic data of the current target area within a preset time is obtained through the water body data preference model for the drop well;

[0008] Acquire real-time water flow data of the target area, and identify abnormal 180-degree return waterfalls according to the estimated water characteristic data of the current target area within a preset time and the real-time water flow data of the target area;

[0009] An early warning is issued according to the abnormal 180-degree return drop well, early warning information is generated, and a related maintenance plan is generated based on the early warning information.

[0010] Furthermore, in this method, sensors are set in the 180-degree return drop well to construct a sensor acquisition network, specifically:

[0011] By setting sensors in the 180-degree return drop well and integrating the sensor data into the sensor acquisition network, the layout position of the sensors is initialized, and the communication stability between the sensors and the sensor acquisition network is tested based on the layout position of the sensors;

[0012] Through testing, the communication stability data information between each sensor and the sensor information collection network is obtained, and the communication stability threshold data is set, and it is determined whether the communication stability data information is greater than the communication stability threshold data;

[0013] When the communication stability data information is greater than the communication stability threshold data, the arrangement of sensors corresponding to the communication stability data information being greater than the communication stability threshold data is completed;

[0014] When the communication stability data information is not greater than the communication stability threshold data, the sensors corresponding to the communication stability data information not greater than the communication stability threshold data are rearranged until the communication stability data information of all sensors is greater than the communication stability threshold data, and the sensor acquisition network is constructed.

[0015] Furthermore, in this method, water body data of the 180-degree return waterfall is obtained through the sensor collection network, and water body characteristic data based on time series under various meteorological characteristics are constructed, specifically:

[0016] Constructing a timestamp, obtaining meteorological characteristics of each timestamp, and obtaining water body data of a 180-degree return waterfall through the sensor collection network;

[0017] Based on the meteorological characteristics of each time stamp and the water body data of the 180-degree return waterfall obtained through the sensor collection network, the water body data of the 180-degree return waterfall under each meteorological characteristic is statistically calculated;

[0018] By intercepting the water body data of the 180-degree return waterfall under each meteorological characteristic, the water body characteristic data based on the time series under each meteorological characteristic is obtained, and the water body characteristic data based on the time series under each meteorological characteristic is output.

[0019] Furthermore, in this method, a water body data preference model for a drop well is constructed based on the water body characteristic data based on the time series under the meteorological characteristics, and the estimated water body characteristic data of the current target area within a preset time is obtained through the drop well water body data preference model, specifically:

[0020] A water body data preference model for a drop well is constructed based on LSTM, and the water body characteristic data based on time series under each meteorological characteristic is input into the water body data preference model for training, so as to obtain a trained water body data preference model for a drop well;

[0021] Acquire meteorological characteristic data of the current target area within a preset time, and input the meteorological characteristic data of the current target area within the preset time into the trained waterfall water body data preference model for estimation, so as to acquire the estimated water body characteristic data of the current target area within the preset time.

[0022] Furthermore, in the present method, real-time water flow data of the target area is obtained, and the abnormal 180-degree return drop well is identified according to the estimated water characteristic data of the current target area within a preset time and the real-time water flow data of the target area, specifically including:

[0023] Acquire real-time water flow data of the target area, set an evaluation index range according to the estimated water characteristic data of the current target area within a preset time, and determine whether the real-time water flow data of the target area is within the evaluation index range;

[0024] When the real-time water flow data of the target area is within the evaluation index range, the 180-degree return drop well corresponding to the real-time water flow data of the target area within the evaluation index range is regarded as a normal 180-degree return drop well;

[0025] When the real-time water flow data of the target area is not within the evaluation index range, the 180-degree return waterfall well corresponding to the real-time water flow data of the target area not within the evaluation index range is taken as an abnormal 180-degree return waterfall well.

[0026] Furthermore, in the present method, an early warning is issued according to the abnormal 180-degree return drop well, early warning information is generated, and a related maintenance plan is generated based on the early warning information, specifically including:

[0027] Setting a number of warning levels, and obtaining real-time water body data of the abnormal 180-degree return drop well, dividing the warning levels based on the real-time water body data of the abnormal 180-degree return drop well, and obtaining the warning level of each abnormal 180-degree return drop well;

[0028] Generate warning information based on the warning level of each abnormal 180-degree return drop well, obtain geographical location information of each maintenance worker in the target area, introduce a genetic algorithm, set a genetic algebra based on the genetic algorithm, and initialize the maintenance position of each worker according to the geographical location information where the warning information is generated;

[0029] Setting a travel distance threshold, and obtaining geographical location information where the warning information is generated, and generating a travel distance for each worker according to the geographical location information where the warning information is generated and the maintenance location of each worker;

[0030] When the travel distance of each staff member is greater than the travel distance threshold, genetic iteration is performed based on genetic algebra to re-optimize the maintenance position of each staff member until the travel distance of each staff member is no longer greater than the travel distance threshold, and the maintenance position of each staff member is output to generate a related maintenance plan.

[0031] A second aspect of the present invention provides a management system for a 180-degree return drop well, the system comprising a memory and a processor, the memory comprising a management method program for a 180-degree return drop well, and when the management method program for a 180-degree return drop well is executed by the processor, the following steps are implemented:

[0032] By setting sensors in the 180-degree return waterfall well, a sensor acquisition network is constructed, and water body data of the 180-degree return waterfall well is obtained through the sensor acquisition network, and water body characteristic data based on time series under various meteorological characteristics is constructed;

[0033] Based on the water body characteristic data based on the time series under the meteorological characteristics, a water body data preference model for a drop well is constructed, and the estimated water body characteristic data of the current target area within a preset time is obtained through the water body data preference model for the drop well;

[0034] Acquire real-time water flow data of the target area, and identify abnormal 180-degree return waterfalls according to the estimated water characteristic data of the current target area within a preset time and the real-time water flow data of the target area;

[0035] An early warning is issued according to the abnormal 180-degree return drop well, early warning information is generated, and a related maintenance plan is generated based on the early warning information.

[0036] A third aspect of the present invention provides a computer-readable storage medium, which includes a management method program for a 180-degree turnaround drop well. When the management method program for a 180-degree turnaround drop well is executed by a processor, any step of the management method for a 180-degree turnaround drop well is implemented.

[0037] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0038] The present invention sets a sensor in a 180-degree return drop well, constructs a sensor acquisition network, obtains water body data of the 180-degree return drop well through the sensor acquisition network, constructs water body feature data based on time series under various meteorological characteristics, constructs a drop well water body data preference model based on water body feature data based on time series under various meteorological characteristics, obtains water body estimated feature data of the current target area within a preset time through the drop well water body data preference model, obtains real-time water body flow data of the target area, identifies abnormal 180-degree return drop wells according to the water body estimated feature data of the current target area within the preset time and the real-time water body flow data of the target area, issues an early warning according to the abnormal 180-degree return drop well, generates early warning information, and generates a related maintenance plan based on the early warning information. The present invention estimates water body data within a predetermined time by constructing a sensor acquisition network and constructing a preference model through historical data of the drop well under various climate environments, and more accurately identifies abnormal 180-degree return drop wells compared to the prior art, thereby formulating a more reasonable maintenance plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0040] Figure 1 The overall flow chart of the management method of the 180-degree return waterfall is shown;

[0041] Figure 2 A partial flow chart of a method for managing a 180-degree return waterfall is shown;

[0042] Figure 3 A system block diagram of a management system for a 180-degree return waterfall is shown;

[0043] Figure 4 A schematic diagram of a 180-degree return waterfall is shown. DETAILED DESCRIPTION

[0044] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0046] like Figure 4 As shown in the figure, this is a construction diagram of a 180-degree return drop well. In the drop well of this design, a 0.53-meter-deep energy dissipation pool is set for every 1.47-meter drop depth. The energy dissipation pools at each level are arranged vertically in a staggered and cross-arranged manner as a diversion measure, so that the falling water falls back and forth in the vertical direction of the well and dissipates energy in stages. Compared with a single-stage drop shaft, the ultra-deep drop well with a multi-stage drop composite vertical diversion has a small burial depth, and the bottom plate does not need to be set with a thick concrete to resist scouring; compared with a traditional multi-stage drop well, the ultra-deep drop well with a multi-stage drop composite vertical diversion has a small footprint and a high overall structural stability.

[0047] like Figure 1 As shown, the first aspect of the present invention provides a management method for a 180-degree return drop well, comprising:

[0048] S102: by setting sensors in the 180-degree return waterfall well, constructing a sensor collection network, obtaining water body data of the 180-degree return waterfall well through the sensor collection network, and constructing water body characteristic data based on time series under various meteorological characteristics;

[0049] S104: constructing a water body data preference model for a waterfall based on the water body characteristic data based on the time series under each meteorological characteristic, and obtaining the estimated water body characteristic data of the current target area within a preset time through the water body data preference model for the waterfall;

[0050] S106: Acquire real-time water flow data of the target area, and identify abnormal 180-degree return waterfalls according to the estimated water characteristic data of the current target area within a preset time and the real-time water flow data of the target area;

[0051] S108: issuing an early warning according to the abnormal 180-degree return waterfall, generating early warning information, and generating a related maintenance plan based on the early warning information.

[0052] It should be noted that the present invention constructs a sensor collection network and a preference model through historical data of waterfalls under various climate environments to estimate water body data within a predetermined time. Compared with the existing technology, it can more accurately identify abnormal 180-degree return waterfalls, thereby formulating a more reasonable maintenance plan.

[0053] like Figure 2 As shown, further, in this method, by setting sensors in the 180-degree return drop well, a sensor acquisition network is constructed, specifically:

[0054] S202: by setting sensors in the 180-degree return drop well and integrating the sensor data into the sensor acquisition network, initializing the layout position of the sensors, and testing the communication stability between the sensors and the sensor acquisition network based on the layout position of the sensors;

[0055] S204: obtaining communication stability data information between each sensor and the sensor information collection network through testing, setting communication stability threshold data, and determining whether the communication stability data information is greater than the communication stability threshold data;

[0056] S206: When the communication stability data information is greater than the communication stability threshold data, the sensor corresponding to the communication stability data information greater than the communication stability threshold data is arranged;

[0057] S208: When the communication stability data information is not greater than the communication stability threshold data, the sensors corresponding to the communication stability data information not greater than the communication stability threshold data are rearranged until the communication stability data information of all sensors is greater than the communication stability threshold data, and the sensor acquisition network is constructed.

[0058] It should be noted that the communication stability data information includes the delay data of information collection, the amount of information transmitted per unit time, the number of interruptions when information is transmitted within a certain period of time, etc. This method can improve the rationality of the construction of the sensor collection network.

[0059] Furthermore, in this method, the water body data of the 180-degree return waterfall is obtained through the sensor collection network, and the water body characteristic data based on time series under various meteorological characteristics is constructed, specifically:

[0060] Construct timestamps, obtain meteorological characteristics of each timestamp, and obtain water body data of 180-degree return waterfalls through the sensor collection network;

[0061] Based on the meteorological characteristics of each timestamp and the water body data of the 180-degree return waterfall obtained through the sensor collection network, the water body data of the 180-degree return waterfall under each meteorological characteristic is statistically calculated;

[0062] By intercepting the water body data of the 180-degree return waterfall under each meteorological characteristic, the water body characteristic data based on the time series under each meteorological characteristic is obtained, and the water body characteristic data based on the time series under each meteorological characteristic is output.

[0063] It should be noted that meteorological characteristics include data such as temperature, humidity, sunny days, rainy days, rainstorms, cloudy days, and spring, summer, autumn, and winter season characteristics. In fact, the water body data (such as water flow data) in the 180-degree return waterfall under different meteorological characteristics is inconsistent. By constructing water body characteristic data based on time series under each meteorological characteristic to obtain data under different meteorological characteristics, the recognition accuracy of abnormal waterfalls can be improved.

[0064] Furthermore, in this method, a water body data preference model for a drop well is constructed based on the water body characteristic data based on the time series under each meteorological characteristic, and the estimated water body characteristic data of the current target area within a preset time is obtained through the drop well water body data preference model, specifically:

[0065] A water body data preference model for a waterfall is constructed based on LSTM, and the water body characteristic data based on time series under various meteorological characteristics is input into the water body data preference model for training to obtain the trained water body data preference model for the waterfall;

[0066] Obtain meteorological characteristic data of the current target area within a preset time, and input the meteorological characteristic data of the current target area within the preset time into the trained waterfall water body data preference model for estimation, so as to obtain the estimated water body characteristic data of the current target area within the preset time.

[0067] It should be noted that, in fact, the water data in the 180-degree return waterfall are inconsistent under different meteorological characteristics. In the prior art, this situation is not taken into consideration for the abnormal 180-degree return waterfall. It only sets a threshold to directly judge. In a specific period of time or in a specific climatic environment, the water data in the 180-degree return waterfall will be very different, and this difference does not mean that the 180-degree return waterfall is abnormal. The waterfall water data preference model can accurately identify abnormal waterfalls.

[0068] Furthermore, in the present method, real-time water flow data of the target area is obtained, and the abnormal 180-degree return waterfall is identified according to the estimated characteristic data of the water body in the current target area within a preset time and the real-time water flow data of the target area, specifically including:

[0069] Obtain real-time water flow data of the target area, set the evaluation index range according to the estimated water characteristic data of the current target area within a preset time, and determine whether the real-time water flow data of the target area is within the evaluation index range;

[0070] When the real-time water flow data of the target area is within the evaluation index range, the 180-degree return waterfall well corresponding to the real-time water flow data of the target area within the evaluation index range is regarded as a normal 180-degree return waterfall well;

[0071] When the real-time water flow data of the target area is not within the evaluation index range, the 180-degree return waterfall well corresponding to the real-time water flow data of the target area not within the evaluation index range is regarded as an abnormal 180-degree return waterfall well.

[0072] It should be noted that the present method can accurately identify abnormal drop wells.

[0073] Furthermore, in this method, an early warning is issued according to the abnormal 180-degree return drop well, early warning information is generated, and a related maintenance plan is generated based on the early warning information, specifically including:

[0074] Set several warning levels (such as low warning level, medium warning level, high warning level, etc.), and obtain real-time water body data of abnormal 180-degree return waterfall wells, divide warning levels based on the real-time water body data of abnormal 180-degree return waterfall wells, and obtain the warning level of each abnormal 180-degree return waterfall well;

[0075] Generate warning information based on the warning level of each abnormal 180-degree return drop well, obtain the geographical location information of each maintenance worker in the target area, introduce a genetic algorithm, set the genetic algebra based on the genetic algorithm, and initialize the maintenance position of each worker according to the geographical location information where the warning information is generated;

[0076] Set a travel distance threshold, obtain the geographical location information where the warning information is generated, and generate the travel distance of each staff member according to the geographical location information where the warning information is generated and the maintenance location of each staff member;

[0077] When the travel distance of each staff member is greater than the travel distance threshold, genetic iteration is performed based on genetic algebra to re-optimize the maintenance position of each staff member until the travel distance of each staff member is no longer greater than the travel distance threshold. The maintenance position of each staff member is output and the relevant maintenance plan is generated.

[0078] It should be noted that this method can improve the rationality of maintenance personnel in work allocation and optimize maintenance resource allocation.

[0079] In addition, the method may further comprise the following steps:

[0080] Introducing a Markov model, and obtaining water body estimated characteristic data for each timestamp based on the water body estimated characteristic data of the current target area within a preset time, and inputting the water body estimated characteristic data for each timestamp into the Markov model;

[0081] Taking the estimated characteristic data of the water body at each timestamp as a state value in the Markov model, counting each state value, constructing a state value transfer matrix, and calculating the transition probability value of each state value in the state value transfer matrix turning into another state value;

[0082] Preset a transition probability threshold, and determine whether the transition probability value is greater than the transition probability threshold, and when the transition probability value is not greater than the transition probability threshold, maintain the current state value and output the current state value;

[0083] When the transition probability value is greater than the transition probability threshold, the other state value is used as the state value corresponding to the timestamp, and the current state value is updated according to the other state value, and the corresponding water body estimation characteristic data is output.

[0084] It should be noted that the Markov Chain describes a state sequence, each state value of which depends on the previous finite state. The Markov Chain is a sequence of random variables X1, X2, X3... with Markov properties. The range of these variables, that is, the set of all their possible values, is called the "state space", and the value of Xn is the state at time n. When the transition probability value is not greater than the transition probability threshold, it means that no transition has occurred. Otherwise, a transition has occurred, that is, from one state value to another state value. This method can improve the prediction accuracy of water body estimation characteristic data.

[0085] In addition, obtaining real-time water flow data of the target area may also include the following steps:

[0086] Acquire real-time water flow data of a target area through a sensor collection network, and acquire communication delay data of the real-time water flow data of the target area when the real-time water flow data is collected, introduce a decision tree model, and input the communication delay data of the real-time water flow data of the target area when the real-time water flow data is collected into the decision tree model for initialization classification;

[0087] Acquire a number of leaf nodes after the initialization classification, introduce a Dice coefficient measurement method, calculate Dice coefficient values ​​between the number of leaf nodes after the initialization classification based on the Dice coefficient measurement method, and preset a Dice coefficient value threshold;

[0088] Obtain several Dice coefficient values, determine whether a Dice coefficient value greater than a Dice coefficient value threshold value appears in the leaf node, and when a Dice coefficient value greater than the Dice coefficient value threshold value appears, reclassify the leaf node until a Dice coefficient value greater than the Dice coefficient value threshold value no longer appears, and output the membership degree corresponding to the leaf node;

[0089] When the Dice coefficient value is no longer greater than the Dice coefficient value threshold, the membership corresponding to the leaf node is output. When the membership corresponding to the leaf node is greater than the preset membership, the real-time water flow data of the current target area is regarded as abnormal data and unreliable data. When the membership corresponding to the leaf node is not greater than the preset membership, it is regarded as normal data and credible data.

[0090] It should be noted that the communication delay data of the real-time water flow data of the target area during collection is input into the decision tree model for initial classification, thereby obtaining the membership of the communication delay, wherein the membership of the communication delay includes no delay, low delay, medium delay, and high delay. Since the decision tree model is prone to the phenomenon of local optimal solution, the Dice coefficient measurement method is introduced to calculate the Dice coefficient value between several leaf nodes after the initial classification. When the Dice coefficient value is no longer greater than the Dice coefficient value threshold, it means that the classification is normal, otherwise it is abnormal. The introduction of the Dice coefficient measurement method can improve the accuracy of data classification. When the communication delay exceeds a certain threshold, the data cannot be used as the data of the current timestamp. This method can determine whether the real-time water flow data of the target area is credible data, thereby improving the accuracy of data acquisition.

[0091] like Figure 3 As shown, the second aspect of the present invention provides a management system 4 for a 180-degree return drop well, the system 4 includes a memory 41 and a processor 42, the memory 41 includes a management method program for a 180-degree return drop well, and when the management method program for a 180-degree return drop well is executed by the processor 42, the following steps are implemented:

[0092] By setting sensors in the 180-degree return waterfall well, a sensor collection network is constructed, and water body data of the 180-degree return waterfall well is obtained through the sensor collection network, and water body characteristic data based on time series under various meteorological characteristics are constructed;

[0093] Based on the water body characteristic data based on the time series under various meteorological characteristics, a water body data preference model for the waterfall is constructed, and the estimated water body characteristic data of the current target area within the preset time is obtained through the water body data preference model for the waterfall;

[0094] Obtain real-time water flow data of the target area, and identify abnormal 180-degree return waterfalls based on the estimated water feature data of the current target area within a preset time and the real-time water flow data of the target area;

[0095] According to the abnormal 180-degree return drop well, an early warning is issued, early warning information is generated, and a related maintenance plan is generated based on the early warning information.

[0096] Furthermore, in this system, sensors are set in the 180-degree return drop well to construct a sensor collection network, specifically:

[0097] S202: by setting sensors in the 180-degree return drop well and integrating the sensor data into the sensor acquisition network, initializing the layout position of the sensors, and testing the communication stability between the sensors and the sensor acquisition network based on the layout position of the sensors;

[0098] S204: obtaining communication stability data information between each sensor and the sensor information collection network through testing, setting communication stability threshold data, and determining whether the communication stability data information is greater than the communication stability threshold data;

[0099] S206: When the communication stability data information is greater than the communication stability threshold data, the sensor corresponding to the communication stability data information greater than the communication stability threshold data is arranged;

[0100] S208: When the communication stability data information is not greater than the communication stability threshold data, the sensors corresponding to the communication stability data information not greater than the communication stability threshold data are rearranged until the communication stability data information of all sensors is greater than the communication stability threshold data, and the sensor acquisition network is constructed.

[0101] It should be noted that the communication stability data information includes the delay data of information collection, the amount of information transmitted per unit time, the number of interruptions when information is transmitted within a certain period of time, etc. This method can improve the rationality of the construction of the sensor collection network.

[0102] Furthermore, in this system, the water body data of the 180-degree return waterfall is obtained through the sensor collection network, and the water body characteristic data based on time series under various meteorological characteristics are constructed, specifically:

[0103] Construct timestamps, obtain meteorological characteristics of each timestamp, and obtain water body data of 180-degree return waterfalls through the sensor collection network;

[0104] Based on the meteorological characteristics of each timestamp and the water body data of the 180-degree return waterfall obtained through the sensor collection network, the water body data of the 180-degree return waterfall under each meteorological characteristic is statistically calculated;

[0105] By intercepting the water body data of the 180-degree return waterfall under each meteorological characteristic, the water body characteristic data based on the time series under each meteorological characteristic is obtained, and the water body characteristic data based on the time series under each meteorological characteristic is output.

[0106] It should be noted that meteorological characteristics include data such as temperature, humidity, sunny days, rainy days, rainstorms, cloudy days, and spring, summer, autumn, and winter season characteristics. In fact, the water body data (such as water flow data) in the 180-degree return waterfall under different meteorological characteristics is inconsistent. By constructing water body characteristic data based on time series under each meteorological characteristic to obtain data under different meteorological characteristics, the recognition accuracy of abnormal waterfalls can be improved.

[0107] Furthermore, in this system, a water body data preference model of a drop well is constructed based on the water body characteristic data based on the time series under each meteorological characteristic, and the estimated water body characteristic data of the current target area within a preset time is obtained through the drop well water body data preference model, specifically:

[0108] A water body data preference model for a waterfall is constructed based on LSTM, and the water body characteristic data based on time series under various meteorological characteristics is input into the water body data preference model for training to obtain the trained water body data preference model for the waterfall;

[0109] Obtain meteorological characteristic data of the current target area within a preset time, and input the meteorological characteristic data of the current target area within the preset time into the trained waterfall water body data preference model for estimation, so as to obtain the estimated water body characteristic data of the current target area within the preset time.

[0110] It should be noted that, in fact, the water data in the 180-degree return waterfall are inconsistent under different meteorological characteristics. In the prior art, this situation is not taken into consideration for the abnormal 180-degree return waterfall. It only sets a threshold to directly judge. In a specific period of time or in a specific climatic environment, the water data in the 180-degree return waterfall will be very different, and this difference does not mean that the 180-degree return waterfall is abnormal. The waterfall water data preference model can accurately identify abnormal waterfalls.

[0111] Furthermore, in this system, real-time water flow data of the target area is obtained, and abnormal 180-degree return waterfalls are identified according to the estimated water characteristic data of the current target area within a preset time and the real-time water flow data of the target area, specifically including:

[0112] Obtain real-time water flow data of the target area, set the evaluation index range according to the estimated water characteristic data of the current target area within a preset time, and determine whether the real-time water flow data of the target area is within the evaluation index range;

[0113] When the real-time water flow data of the target area is within the evaluation index range, the 180-degree return waterfall well corresponding to the real-time water flow data of the target area within the evaluation index range is regarded as a normal 180-degree return waterfall well;

[0114] When the real-time water flow data of the target area is not within the evaluation index range, the 180-degree return waterfall well corresponding to the real-time water flow data of the target area not within the evaluation index range is regarded as an abnormal 180-degree return waterfall well.

[0115] It should be noted that the present method can accurately identify abnormal drop wells.

[0116] Furthermore, in this system, an early warning is issued according to the abnormal 180-degree return drop well, early warning information is generated, and a related maintenance plan is generated based on the early warning information, specifically including:

[0117] Set several warning levels (such as low warning level, medium warning level, high warning level, etc.), and obtain real-time water body data of abnormal 180-degree return waterfall wells, divide warning levels based on the real-time water body data of abnormal 180-degree return waterfall wells, and obtain the warning level of each abnormal 180-degree return waterfall well;

[0118] Generate warning information based on the warning level of each abnormal 180-degree return drop well, obtain the geographical location information of each maintenance worker in the target area, introduce a genetic algorithm, set the genetic algebra based on the genetic algorithm, and initialize the maintenance position of each worker according to the geographical location information where the warning information is generated;

[0119] Set a travel distance threshold, obtain the geographical location information where the warning information is generated, and generate the travel distance of each staff member according to the geographical location information where the warning information is generated and the maintenance location of each staff member;

[0120] When the travel distance of each staff member is greater than the travel distance threshold, genetic iteration is performed based on genetic algebra to re-optimize the maintenance position of each staff member until the travel distance of each staff member is no longer greater than the travel distance threshold. The maintenance position of each staff member is output and the relevant maintenance plan is generated.

[0121] It should be noted that this method can improve the rationality of maintenance personnel in work allocation and optimize maintenance resource allocation.

[0122] A third aspect of the present invention provides a computer-readable storage medium, which includes a management method program for a 180-degree turnaround drop well. When the management method program for a 180-degree turnaround drop well is executed by a processor, any step of the management method for a 180-degree turnaround drop well is implemented.

[0123] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0124] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0125] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0126] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0127] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0128] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A management method for a 180-degree return waterfall, characterized in that: include: By setting sensors in the 180-degree return waterfall well, a sensor acquisition network is constructed, and water body data of the 180-degree return waterfall well is obtained through the sensor acquisition network, and water body characteristic data based on time series under various meteorological characteristics is constructed; Based on the water body characteristic data based on the time series under the meteorological characteristics, a water body data preference model for a drop well is constructed, and the estimated water body characteristic data of the current target area within a preset time is obtained through the water body data preference model for the drop well; Acquire real-time water flow data of the target area, and identify abnormal 180-degree return waterfalls according to the estimated water characteristic data of the current target area within a preset time and the real-time water flow data of the target area; Produce an early warning based on the abnormal 180-degree return drop well, generate early warning information, and generate a related maintenance plan based on the early warning information; The water body data of the 180-degree return waterfall is obtained through the sensor collection network, and the water body characteristic data based on time series under various meteorological characteristics are constructed, specifically: Constructing a timestamp, obtaining meteorological characteristics of each timestamp, and obtaining water body data of a 180-degree return waterfall through the sensor collection network; Based on the meteorological characteristics of each time stamp and the water body data of the 180-degree return waterfall obtained through the sensor collection network, the water body data of the 180-degree return waterfall under each meteorological characteristic is statistically calculated; By intercepting the water body data of the 180-degree return waterfall under each meteorological feature, the water body feature data based on the time series under each meteorological feature is obtained, and the water body feature data based on the time series under each meteorological feature is output; Based on the water body characteristic data based on the time series under the meteorological characteristics, a water body data preference model for a drop well is constructed, and the estimated water body characteristic data of the current target area within a preset time is obtained through the water body data preference model for the drop well, specifically: A water body data preference model for a drop well is constructed based on LSTM, and the water body characteristic data based on time series under each meteorological characteristic is input into the water body data preference model for training, so as to obtain a trained water body data preference model for a drop well; Acquire meteorological characteristic data of the current target area within a preset time, and input the meteorological characteristic data of the current target area within the preset time into the trained drop well water body data preference model for estimation, so as to acquire estimated water body characteristic data of the current target area within the preset time; Meteorological characteristics include temperature, humidity, sunny days, rainy days, rainstorms, cloudy days, and seasons of spring, summer, autumn and winter.

2. A management method for a 180-degree return drop well according to claim 1, characterized in that: By setting sensors in the 180-degree return waterfall, a sensor collection network is constructed, specifically: By setting sensors in the 180-degree return drop well and integrating the sensor data into the sensor acquisition network, the layout position of the sensors is initialized, and the communication stability between the sensors and the sensor acquisition network is tested based on the layout position of the sensors; Through testing, the communication stability data information between each sensor and the sensor information collection network is obtained, and the communication stability threshold data is set, and it is determined whether the communication stability data information is greater than the communication stability threshold data; When the communication stability data information is greater than the communication stability threshold data, the arrangement of sensors corresponding to the communication stability data information being greater than the communication stability threshold data is completed; When the communication stability data information is not greater than the communication stability threshold data, the sensors corresponding to the communication stability data information not greater than the communication stability threshold data are rearranged until the communication stability data information of all sensors is greater than the communication stability threshold data, and the sensor acquisition network is constructed.

3. A management method for a 180-degree return waterfall according to claim 1, characterized in that: Acquiring real-time water flow data of the target area, and identifying abnormal 180-degree return waterfalls according to the estimated water characteristic data of the current target area within a preset time and the real-time water flow data of the target area, specifically including: Acquire real-time water flow data of the target area, set an evaluation index range according to the estimated water characteristic data of the current target area within a preset time, and determine whether the real-time water flow data of the target area is within the evaluation index range; When the real-time water flow data of the target area is within the evaluation index range, the 180-degree return drop well corresponding to the real-time water flow data of the target area within the evaluation index range is regarded as a normal 180-degree return drop well; When the real-time water flow data of the target area is not within the evaluation index range, the 180-degree return waterfall well corresponding to the real-time water flow data of the target area not within the evaluation index range is taken as an abnormal 180-degree return waterfall well.

4. A management method for a 180-degree return waterfall according to claim 1, characterized in that: According to the abnormal 180-degree return drop well, an early warning is issued, early warning information is generated, and a relevant maintenance plan is generated based on the early warning information, specifically including: Setting a number of warning levels, and obtaining real-time water body data of the abnormal 180-degree return drop well, dividing the warning levels based on the real-time water body data of the abnormal 180-degree return drop well, and obtaining the warning level of each abnormal 180-degree return drop well; Generate warning information based on the warning level of each abnormal 180-degree return drop well, obtain geographical location information of each maintenance worker in the target area, introduce a genetic algorithm, set a genetic algebra based on the genetic algorithm, and initialize the maintenance position of each worker according to the geographical location information where the warning information is generated; Setting a travel distance threshold, and obtaining geographical location information where the warning information is generated, and generating a travel distance for each worker according to the geographical location information where the warning information is generated and the maintenance location of each worker; When the travel distance of each staff member is greater than the travel distance threshold, genetic iteration is performed based on genetic algebra to re-optimize the maintenance position of each staff member until the travel distance of each staff member is no longer greater than the travel distance threshold, and the maintenance position of each staff member is output to generate a related maintenance plan.

5. A management system for a 180-degree return waterfall, characterized in that: The system includes a memory and a processor, wherein the memory includes a management method program for a 180-degree return drop well, and when the management method program for the 180-degree return drop well is executed by the processor, the following steps are implemented: By setting sensors in the 180-degree return waterfall well, a sensor acquisition network is constructed, and water body data of the 180-degree return waterfall well is obtained through the sensor acquisition network, and water body characteristic data based on time series under various meteorological characteristics is constructed; Based on the water body characteristic data based on the time series under the meteorological characteristics, a water body data preference model for a drop well is constructed, and the estimated water body characteristic data of the current target area within a preset time is obtained through the water body data preference model for the drop well; Acquire real-time water flow data of the target area, and identify abnormal 180-degree return waterfalls according to the estimated water characteristic data of the current target area within a preset time and the real-time water flow data of the target area; Produce an early warning based on the abnormal 180-degree return drop well, generate early warning information, and generate a related maintenance plan based on the early warning information; The water body data of the 180-degree return waterfall is obtained through the sensor collection network, and the water body characteristic data based on time series under various meteorological characteristics are constructed, specifically: Constructing a timestamp, obtaining meteorological characteristics of each timestamp, and obtaining water body data of a 180-degree return waterfall through the sensor collection network; Based on the meteorological characteristics of each time stamp and the water body data of the 180-degree return waterfall obtained through the sensor collection network, the water body data of the 180-degree return waterfall under each meteorological characteristic is statistically calculated; By intercepting the water body data of the 180-degree return waterfall under each meteorological feature, the water body feature data based on the time series under each meteorological feature is obtained, and the water body feature data based on the time series under each meteorological feature is output; Based on the water body characteristic data based on the time series under the meteorological characteristics, a water body data preference model for a drop well is constructed, and the estimated water body characteristic data of the current target area within a preset time is obtained through the water body data preference model for the drop well, specifically: A water body data preference model for a drop well is constructed based on LSTM, and the water body characteristic data based on time series under each meteorological characteristic is input into the water body data preference model for training, so as to obtain a trained water body data preference model for a drop well; Acquire meteorological characteristic data of the current target area within a preset time, and input the meteorological characteristic data of the current target area within the preset time into the trained drop well water body data preference model for estimation, so as to acquire estimated water body characteristic data of the current target area within the preset time; Meteorological characteristics include temperature, humidity, sunny days, rainy days, rainstorms, cloudy days, and seasons of spring, summer, autumn and winter.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a management method program for a 180-degree return drop well. When the management method program for a 180-degree return drop well is executed by a processor, the steps of the management method for a 180-degree return drop well as described in any one of claims 1-4 are implemented.

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