Flow monitoring device based on wireless intelligent monitoring ring

Abstract

The invention discloses a flow monitoring device based on a wireless intelligent monitoring ring, relates to the technical field of flow monitoring devices, and aims to realize accurate real-time monitoring of the water yield of a fire-fighting lance. The system comprises a wireless intelligent monitoring ring and a rear-end server, the wireless intelligent monitoring ring is provided with a circular-ring-shaped shell, the circular-ring-shaped shell is provided with an electronic bin and a measuring hole, a sensor is placed in the measuring hole, a microprocessor is placed in the electronic bin, and water flow and environmental parameter data can be measured in real time; the data processing unit compensates and calibrates the data and transmits the data to a rear-end server, and the server calculates the water outlet amount of the water gun and the remaining water amount of the vehicle-mounted water tank according to the data and adjusts the water outlet amount of the water gun; the water volume evaluation accuracy is improved, the fire extinguishing action efficiency is improved, meanwhile, the device design adapts to a complex fire scene, and the adaptability and reliability of equipment are enhanced; in addition, water resource waste can be avoided, the fire extinguishing decision is optimized, and the fire extinguishing success rate is increased.

Description

Technical Field

[0001] The present invention belongs to the technical field of flow monitoring devices. Specifically, it particularly relates to a flow monitoring device based on a wireless intelligent monitoring loop. Background Art

[0002] When a fire truck goes out for a fire, the fire extinguishing strategy will be determined according to the fire situation. For larger or more complex fires, multiple water guns need to be connected to the on-vehicle water tank of the fire truck, and multiple branches are diverted through the main water supply pipeline to supply water to each water gun. During this period, the continuous supply of water source needs to be considered; when the remaining capacity of the on-vehicle water tank is not enough to extinguish the fire, remedial measures should be taken as soon as possible to ensure that there is enough water for the fire extinguishing operation.

[0003] The water output of each branch diverted from the main water supply pipeline is different. To evaluate the remaining water volume in the on-vehicle water tank, it is necessary to monitor the water flow of each branch separately; currently, firefighters estimate the water output of the water guns they control by feeling and experience, and then communicate with each other to estimate the remaining water volume in the on-vehicle water tank; in this way, the efficiency of estimating the remaining water volume in the on-vehicle water tank is low, and the remaining water volume in the on-vehicle water tank cannot be accurately obtained, which affects the subsequent fire extinguishing decision-making. Summary of the Invention

[0004] (I) Technical Problems to be Solved In view of the problems in the related art, the present invention provides a flow monitoring device based on a wireless intelligent monitoring loop. By combining real-time data collection of sensors, data processing of microprocessors, and calculation and analysis of the backend server, the present invention solves the technical problems of inaccurate evaluation and low efficiency of the remaining water volume in the on-vehicle water tank, realizes accurate monitoring of the water output of each water gun and accurate estimation of the remaining water volume in the on-vehicle water tank, thereby improving the scientificity of fire extinguishing decision-making and the effectiveness of resource management.

[0005] (II) Technical Solutions To solve the above technical problems, the present invention is realized through the following technical solutions: A flow monitoring device based on a wireless intelligent monitoring loop, characterized by comprising: a wireless intelligent monitoring loop and a backend server; The wireless intelligent monitoring loop includes: an annular outer shell 2, arranged between the water gun and the water hose, and an electronic compartment 1 and a measuring hole 3 are arranged on the annular outer shell 2; Quick connection devices are provided at both ends of the annular outer shell 2, and sealing rings are equipped at the connection parts; The surface of the outer shell is designed with an impact-resistant protective layer, and the inner wall is provided with a shock-proof structure; The outer shell is designed with heat dissipation holes to ensure that the device can still work normally in a high-temperature environment; A sensor, placed in the measuring hole 3, for real-time measurement of the water flow parameters and environmental parameter data flowing through the water hose and the water gun; A microprocessor is placed in the electronic compartment 1 and includes a signal acquisition unit, a data processing unit, and a water volume calculation unit; The signal acquisition unit is connected to the sensor through a lead wire and is used to obtain water flow data and environmental data near the water gun; The data processing unit is used to compensate and calibrate the water flow data according to the environmental data; The data transmission unit is used to send the compensated and calibrated water flow data to the backend server, The backend server is used to calculate the water output of each water gun and the remaining water volume of the vehicle-mounted water tank according to the compensated and calibrated water flow data; according to the water output of each water gun and the remaining water volume of the vehicle-mounted water tank, adjust the water output of the water gun.

[0006] Preferably, the sensor is placed in the measuring hole 3, and the specific steps for real-time measuring the water flow parameters and environmental parameter data flowing through the water hose and the water gun are as follows: S11. Set the water flow parameter set , and set the environmental parameter set , where a i represents the i th water flow parameter in the water flow parameter set, n represents the total number of water flow parameters, b i represents the i th environmental parameter in the environmental parameter set, q represents the total number of environmental parameters; S12. Arrange sensors in the measuring hole 3 of the wireless intelligent monitoring ring according to the parameters in the water flow parameter set and the environmental parameter set; By setting the water flow parameter set and the environmental parameter set and arranging the sensors accordingly in the measuring hole 3 of the wireless intelligent monitoring ring, the water flow parameters and environmental parameter data flowing through the water hose and the water gun can be measured in real time and accurately, effectively improving the accuracy and comprehensiveness of data acquisition.

[0007] Preferably, the specific steps for the signal acquisition unit, which is connected to the sensor through a lead wire and is used to obtain water flow data and environmental data near the water gun, are as follows: S21. Collect the number of real-time water guns, and obtain the real-time number of water guns as m , and connect m wireless intelligent monitoring rings to connect the water gun and the water hose through the quick connection devices at both ends of the circular outer shell 2; Collect the real-time water flow parameter data and real-time environmental parameter data of the water flowing through the water hose and the water gun measured by the sensor through the lead wire, and obtain the real-time monitored water flow parameter data matrix A and the real-time environmental data parameter matrix B , as follows, ; ; Among them, A in represents the real-time water flow parameter data matrix A in the i th water gun's real-time water flow of the n th parameter data, B iq represents the i th water gun's real-time environment of the real-time environment parameter data matrix of the q th parameter; By connecting with the leads of the sensors in the above steps, the real-time water flow parameters and environmental parameters of each water gun can be efficiently collected to form a detailed data matrix; ensuring the real-time and accuracy of data collection, realizing the parallel monitoring of multiple water guns; providing comprehensive and reliable data support for subsequent hydrodynamic model calculation and water volume assessment.

[0008] Preferably, the specific steps for the data processing unit to compensate and calibrate the water flow data according to the environmental data are as follows: S31. Collect the actual water flow parameter data and the water flow parameter data measured by the wireless intelligent monitoring ring in different historical environments to obtain the historical environment and monitoring parameter data matrix and the historical actual water flow parameter data matrix, and use the historical actual water flow parameter data matrix as the label matrix of the historical environment and monitoring parameter data matrix; S32. Build an initial CNN neural network model, set the initial weight of the initial CNN neural network model to c 1 , the initial bias to d 1 , and the initial learning rate to e 1 ; set the training ratio and test ratio of the initial CNN neural network model to f 1 , f 2 ; S33. According to the f 1 , f 2 , divide the historical environment and monitoring parameter data matrix into a historical environment and monitoring parameter data training matrix and a historical environment and monitoring parameter data test matrix; correspondingly divide the historical actual water flow parameter data matrix into a historical actual water flow parameter data training matrix and a historical actual water flow parameter data test matrix; S34. Input the training matrices of the historical environment and monitoring parameter data and the historical actual water flow parameter data into the initial CNN neural network model for training. After the training is completed, obtain the trained CNN neural network model; S35. Input the test matrices of the historical environment and monitoring parameter data and the historical actual water flow parameter data into the trained CNN neural network model for testing. After the test optimization is completed, obtain the final CNN neural network model; S36. Substitute the data in the real-time monitored water flow parameter data matrix A and the real-time environment parameter data matrix B into the final CNN neural network model to obtain the compensated and corrected real-time water flow parameter data matrix C , as follows, ; Among them, Cin represents the i th real-time water flow parameter data after compensation and correction of the n th water gun in the compensated and corrected real-time water flow parameter data matrix; The above steps provide rich learning samples for neural network training by collecting historical data and constructing label matrices, ensuring the effectiveness of the model; the CNN neural network model after training and test optimization can accurately compensate and correct the real-time monitored water flow parameter data, eliminating the influence of environmental factors on the measurement results; the finally generated compensated and corrected real-time water flow parameter data matrix provides more accurate water volume information, which helps to achieve more refined adjustment of the water gun water output and more efficient fire extinguishing strategy formulation.

[0009] Preferably, the specific steps of S34 are as follows: S341. Set the training error threshold and the maximum number of training iterations; S342. Input the training matrices of the historical environment and monitoring parameter data and the historical actual water flow parameter data into the initial CNN neural network model, and repeatedly train the initial CNN neural network model; S343. Adjust the initial weights, initial biases, and initial learning rates of the initial CNN neural network model according to the training results of each round to obtain the adjusted CNN neural network model, and continue to train the adjusted CNN neural network model. When the training error ≤ the training error threshold or the current number of iterations ≥ the maximum number of training iterations, stop the training to obtain the trained CNN neural network model; The above steps ensure that the model stops training after reaching the preset error threshold or number of iterations through repeated training and parameter adjustment, avoiding the problems of overfitting or underfitting; the trained CNN neural network model has higher generalization ability and prediction accuracy, and can more reliably compensate and calibrate the real-time water flow parameter data, further enhancing the data analysis and decision-making support capabilities of the fire extinguishing system.

[0010] Preferably, the S35 includes the following steps: S351. Set the test accuracy threshold, input the historical environment and monitoring parameter data test matrix into the trained CNN neural network model for testing to obtain the test output result, compare the test output result with the historical actual water flow data test matrix to obtain the test accuracy; S352. When the test accuracy ≥ the test accuracy threshold, use the trained CNN neural network model as the final CNN neural network model; S353. When the test accuracy < the test accuracy threshold, optimize the trained CNN neural network model by combining grid search and cross-validation algorithm. After optimization, obtain the final CNN neural network model; The above steps can objectively evaluate the performance of the model by comparing the test output result with the historical actual data; if the test accuracy reaches or exceeds the threshold, the model is considered qualified and can be directly used in practical applications; if the threshold is not reached, the grid search and cross-validation algorithm are used to further optimize the model until the accuracy requirement is met; improving the prediction accuracy of the model and ensuring the efficiency and accuracy of the CNN neural network model in compensating and calibrating real-time water flow parameter data.

[0011] Preferably, the specific steps of optimizing the trained CNN neural network model by combining grid search and cross-validation algorithm in the S353 and obtaining the final CNN neural network model after optimization are as follows: S3531. Set the parameter search space as g , the maximum number of random searches is h , and set the number of cross-validation folds as X ; S3532. Initialize the optimal parameter combination , initialize the best performance as v ; Randomly select the parameter combination , construct a CNN neural network model according to the selected parameter combination, divide the training data into X folds, and for each fold x , ; Use all the training data except the x th fold to train the CNN neural network model, and use thex Evaluate the model with the folded training data to obtain a set of performance metrics , calculate the mean of the performance metrics for all folds to obtain the average performance u , and the calculation formula is as follows ; If the average performance u > the best performance v , then update the best performance to u , update the optimal parameter combination to , and update the training accuracy; otherwise, keep the original best performance, optimal parameter combination, and training accuracy; S3533. Repeat S3532. When the maximum random search number g is reached or the training accuracy ≥ the training accuracy threshold, stop the iteration, obtain the optimal parameter combination, use the optimal parameter combination as the parameters of the CNN neural network model, and obtain the final CNN neural network model; The above steps ensure finding the optimal solution among a wide range of parameter combinations by setting the parameter search space and the maximum random search number, as well as adopting the number of cross-validation folds, and at the same time maintaining the best performance and parameter combination through iterative updates.

[0012] Preferably, the backend server is used to calculate the water output of each water gun and the remaining water volume of the vehicle-mounted water tank according to the compensated and calibrated water flow data; the specific steps for adjusting the water output of the water gun according to the water output of each water gun and the remaining water volume of the vehicle-mounted water tank are as follows: S41. The backend server establishes a fluid mechanics model based on the water flow parameter set, substitutes the water flow parameter data of each water gun in the compensated and corrected real-time water flow parameter data matrix into the fluid mechanics model to obtain the water output of each water gun and the remaining water volume of the vehicle-mounted water tank; S42. According to the water output of each water gun and the remaining water volume of the vehicle-mounted water tank, the backend server sends an instruction to adjust the water output of the water gun; The above steps realize the intelligent control and optimization of the fire extinguishing process by establishing a fluid mechanics model, substituting the compensated and corrected real-time water flow parameter data for calculation, and the backend server sending an instruction to adjust the water output of the water gun according to the calculation result; this design not only improves the utilization efficiency of water resources, but also enhances the accuracy and safety of fire extinguishing operations, effectively improving the overall performance and intelligent level of the fire extinguishing system, and providing more scientific and efficient fire extinguishing support for firefighters.

[0013] (III) Beneficial effects The present invention has the following beneficial effects: The present invention realizes the accurate and real-time monitoring of the water output of a fire hose by introducing a flow monitoring device based on a wireless intelligent monitoring ring. Compared with the traditional estimation method relying on the experience and feeling of firefighters, the present invention can significantly improve the accuracy of water volume assessment, reduce errors, and thus improve the efficiency of fire extinguishing operations. Through real-time data collection and processing, fire commanders can more quickly and accurately grasp the remaining water volume in the vehicle-mounted water tank, providing a scientific basis for formulating and adjusting fire extinguishing strategies.

[0014] The monitoring device of the present invention is designed with full consideration of the actual environmental requirements at the fire scene. The circular outer shell is equipped with an impact-resistant protective layer and a shock-proof structure to ensure that the device can still work stably in the complex and changeable fire scene. The design of the heat dissipation holes enables the device to operate normally in a high-temperature environment, while the quick connection device and the sealing ring ensure the convenient and firm connection of the device with the fire hose and the water belt. These design features greatly enhance the adaptability and reliability of the device, providing strong technical support for firefighters.

[0015] The present invention can achieve the accurate regulation of the water output of each fire hose and the real-time update of the remaining water volume in the vehicle-mounted water tank. This not only helps to avoid waste of water resources but also ensures that there is always sufficient water supply during the fire extinguishing process. In addition, based on the accurate water volume data, fire commanders can more reasonably allocate and dispatch resources, optimize fire extinguishing decisions, improve the success rate of fire extinguishing, and minimize the losses caused by the fire.

[0016] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a front view and side view structural schematic diagram of a bayonet-type wireless intelligent monitoring ring in a flow monitoring device based on a wireless intelligent monitoring ring according to the present invention; Figure 2 It is a flow schematic diagram for realizing flow monitoring in a flow monitoring device based on a wireless intelligent monitoring ring according to the present invention; Figure 3 It is an overall structural schematic diagram of a quick-interface-type wireless intelligent monitoring ring in a flow monitoring device based on a wireless intelligent monitoring ring according to the present invention.

[0019] In the figure: 1, electronic compartment; 2, circular outer shell; 3, measuring hole. Detailed implementation manners

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the invention with reference to the accompanying drawings in the embodiments of the invention. Obviously, the described embodiments are only a part of the embodiments of the invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the invention without making creative efforts shall fall within the scope of protection of the invention.

[0021] In the description of the present invention, it should be understood that the terms "open hole", "upper", "lower", "top", "middle", "inner", etc. indicating the orientation or position relationship are only for the convenience of describing the invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the invention.

[0022] Embodiment 1: Please refer to Figure 1 , Figure 2 , Figure 3 , the present invention discloses a flow monitoring device based on a wireless intelligent monitoring ring, including: a wireless intelligent monitoring ring and a backend server; the caliber types of the wireless intelligent monitoring ring include 65 caliber, 80 caliber, etc.; the types of the wireless intelligent monitoring ring include bayonet type, quick interface type, etc.; The wireless intelligent monitoring ring includes: a circular outer shell 2, arranged between the water gun and the water hose, and an electronic compartment 1 and a measuring hole 3 are arranged on the circular outer shell 2; Both ends of the circular outer shell 2 are provided with quick connection devices, and sealing rings are equipped at the connection parts; The surface of the outer shell is designed with an impact-resistant protective layer, and the inner wall is provided with a shock-proof structure; The outer shell is designed with heat dissipation holes to ensure that the device can still work normally in a high-temperature environment; A sensor is placed in the measuring hole 3 for real-time measurement of the water flow parameters and environmental parameter data flowing through the water hose and the water gun; A microprocessor is placed in the electronic compartment 1, including a signal acquisition unit, a data processing unit and a water volume calculation unit; The signal acquisition unit is connected to the sensor through a lead for obtaining water flow data and environmental data near the water gun; The data processing unit is used for compensating and calibrating the water flow data according to the environmental data; The data transmission unit is used for sending the compensated and calibrated water flow data to the backend server, The backend server is used for calculating the water output of each water gun and the remaining water volume of the vehicle-mounted water tank according to the compensated and calibrated water flow data; adjusting the water output of the water gun according to the water output of each water gun and the remaining water volume of the vehicle-mounted water tank.

[0023] The sensor is placed in the measurement hole 3, and the specific steps for measuring the water flow parameters and environmental parameter data flowing through the water hose and the water gun in real time are as follows: S11. Set the water flow parameter set , and set the environmental parameter set , where a i represents the i th water flow parameter in the water flow parameter set, n represents the total number of water flow parameters, b i represents the i th environmental parameter in the environmental parameter set, q represents the total number of environmental parameters; the water flow parameters are such as flow velocity, flow rate, water pressure, etc., and the environmental parameters are such as temperature, humidity, etc.; S12. Arrange sensors in the measurement hole 3 of the wireless intelligent monitoring ring according to the parameters in the water flow parameter set and the environmental parameter set; The signal acquisition unit is connected to the sensor through a lead wire, and the specific steps for obtaining the water flow data and the environmental data near the water gun are as follows: S21. Collect the number of real-time water guns, and obtain that the number of real-time water guns is m , and connect m wireless intelligent monitoring rings to connect the water gun and the water hose through the quick connection devices at both ends of the circular outer shell 2; the quick connection devices are such as buckles or threaded interfaces; Collect the real-time water flow parameter data and real-time environmental parameter data of the water flowing through the water hose and the water gun measured by the sensor through the lead wire, and obtain the real-time monitored water flow parameter data matrix A and the real-time environmental data parameter matrix B , as follows, ; ; where, A in represents the A th real-time water flow parameter data of the i th water gun in the real-time water flow parameter data matrix, n represents the B iq th parameter of the real-time environment of the i th water gun in the real-time environmental parameter data matrix, q ; The data processing unit is used to compensate and calibrate the water flow data according to the environmental data, and the specific steps are as follows: S31. Collect the actual water flow parameter data in different historical environments and the water flow parameter data measured by the wireless intelligent monitoring ring to obtain the historical environment and monitoring parameter data matrix and the historical actual water flow parameter data matrix. Use the historical actual water flow parameter data matrix as the label matrix of the historical environment and monitoring parameter data matrix; S32. Construct an initial CNN neural network model, and set the initial weight of the initial CNN neural network model to c 1 , the initial bias to d 1 , and the initial learning rate to e 1 ; Set the training ratio and test ratio of the initial CNN neural network model to f 1 , f 2 ; S33. According to the f 1 , f 2 , divide the historical environment and monitoring parameter data matrix into a historical environment and monitoring parameter data training matrix and a historical environment and monitoring parameter data test matrix; correspondingly, divide the historical actual water flow parameter data matrix into a historical actual water flow parameter data training matrix and a historical actual water flow parameter data test matrix; S34. Input the historical environment and monitoring parameter data training matrix and the historical actual water flow parameter data training matrix into the initial CNN neural network model for training. After the training is completed, obtain the trained CNN neural network model; The specific steps of S34 are as follows: S341. Set the training error threshold and the maximum number of training iterations; S342. Input the historical environment and monitoring parameter data training matrix and the historical actual water flow parameter data training matrix into the initial CNN neural network model, and perform repeated training on the initial CNN neural network model; S343. Adjust the initial weight, initial bias, and initial learning rate of the initial CNN neural network model according to the training results of each round to obtain the adjusted CNN neural network model, and continue to train the adjusted CNN neural network model. When the training error ≤ the training error threshold or the current iteration number ≥ the maximum number of training iterations, stop training to obtain the trained CNN neural network model.

[0024] S35. Input the historical environment and monitoring parameter data test matrix and the historical actual water flow parameter data test matrix into the trained CNN neural network model for testing. After the test optimization is completed, obtain the final CNN neural network model; The specific steps of S35 are as follows: S351. Set the test accuracy threshold, input the historical environment and monitoring parameter data test matrix into the trained CNN neural network model for testing to obtain the test output result, compare the test output result with the historical actual water flow data test matrix to obtain the test accuracy; S352. When the test accuracy ≥ the test accuracy threshold, use the trained CNN neural network model as the final CNN neural network model; S353. When the test accuracy < the test accuracy threshold, optimize the trained CNN neural network model by combining grid search and cross - validation algorithm. After optimization, obtain the final CNN neural network model; The specific steps of S353 are as follows: S3531. Set the parameter search space as g , the maximum number of random searches as h , and set the number of cross - validation folds as X ; S3532. Initialize the optimal parameter combination , initialize the best performance as v ; Randomly select a parameter combination , construct a CNN neural network model according to the selected parameter combination, divide the training data into X folds. For each fold x , ; Use all the training data except the x rd fold to train the CNN neural network model, use the training data of the x rd fold to evaluate the model to obtain the performance metric set , calculate the mean of the performance metrics of all folds to obtain the average performance u , and the calculation formula is as follows, ; If the average performance u > the best performance v , then update the best performance to u , update the optimal parameter combination to , and update the training accuracy; otherwise, keep the original best performance, optimal parameter combination, and training accuracy; S3533. Repeat S3532. When the maximum number of random searches g is reached or the training accuracy ≥ the training accuracy threshold, stop the iteration to obtain the optimal parameter combination, use the optimal parameter combination as the parameters of the CNN neural network model, and obtain the final CNN neural network model; S36. Input the real - time monitored water flow parameter data matrixA and the real-time environmental parameter data matrix B Substitute the data in the final CNN neural network model to obtain the compensated and corrected real-time water flow parameter data matrix C , as follows, ; Wherein, Cin represents the i th water gun in the compensated and corrected real-time water flow parameter data matrix, and the n th real-time water flow parameter data after compensation and correction; The backend server is used to calculate the water output of each water gun and the remaining water volume of the vehicle-mounted water tank according to the compensated and calibrated water flow data; the specific steps of adjusting the water output of the water gun according to the water output of each water gun and the remaining water volume of the vehicle-mounted water tank are as follows: S41. The backend server establishes a hydrodynamics model based on the water flow parameter set, and substitutes the water flow parameter data of each water gun in the compensated and corrected real-time water flow parameter data matrix into the hydrodynamics model to obtain the water output of each water gun and the remaining water volume of the vehicle-mounted water tank; S42. According to the water output of each water gun and the remaining water volume of the vehicle-mounted water tank, the backend server sends an instruction to adjust the water output of the water gun.

[0025] In the description of this specification, the descriptions referring to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0026] The above-disclosed preferred embodiments of the invention are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principle and practical application of the invention, so that those skilled in the art can well understand and utilize the invention.

Claims

1. A flow monitoring device based on a wireless intelligent monitoring ring, characterized in that: include: Wireless intelligent monitoring ring and backend server; The wireless intelligent monitoring ring comprises: a circular ring-shaped shell, which is arranged between the water gun and the water hose, and an electronic compartment and a measuring hole are arranged on the circular ring-shaped shell; Both ends of the annular housing are provided with quick connection devices, and the connection parts are equipped with sealing rings; The outer shell surface is designed with an impact-resistant protective layer, and the inner wall is equipped with a shock-proof structure; The shell is designed with heat dissipation holes; Sensors are placed in the measuring holes to measure the water flow parameters and environmental parameter data flowing through the hose and water gun in real time; A microprocessor, placed in the electronic compartment, includes a signal acquisition unit, a data processing unit and a water volume calculation unit; A signal acquisition unit is connected to the sensor through a lead wire and is used to obtain water flow data and environmental data near the water gun; A data processing unit, used for compensating and calibrating water flow data according to environmental data; The data transmission unit is used to send the compensated and calibrated water flow data to the back-end server. The back-end server is used to calculate the water output of each water gun and the remaining water volume in the vehicle-mounted water tank according to the water flow data after compensation calibration; and adjust the water output of the water gun according to the water output of each water gun and the remaining water volume in the vehicle-mounted water tank.

2. A flow monitoring device based on a wireless intelligent monitoring ring according to claim 1, characterized in that: The specific steps of placing the sensor in the measuring hole to measure the water flow parameters and environmental parameter data flowing through the water hose and water gun in real time are as follows: S11. Set water flow parameter set , set the environment parameter set ,in a i Indicates the first i Water flow parameters, n Represents the total number of water flow parameters, b i Indicates the first i environmental parameters, q Indicates the total number of environmental parameters; S12. Arrange sensors in the measuring holes of the wireless intelligent monitoring ring according to the parameters in the water flow parameter set and the environmental parameter set.

3. A flow monitoring device based on a wireless intelligent monitoring ring according to claim 1, characterized in that: The signal acquisition unit is connected to the sensor through a lead wire, and the specific steps for obtaining water flow data and environmental data near the water gun are as follows: S21. Collect the number of real-time water guns and obtain the number of real-time water guns: m ,Will m A wireless intelligent monitoring ring connects the water gun to the water hose through the quick connection devices at both ends of the circular shell; The real-time water flow parameter data and real-time environmental parameter data measured by the sensor flowing through the water hose and water gun are collected through the lead to obtain the real-time monitoring water flow parameter data matrix A and real-time environmental data parameter matrix B ,as follows, ； ； in, A in Represents real-time water flow parameter data matrix A Middle i The real-time water flow of the water gun n parameter data, B iq Represents the first i The real-time environment of the water gun q parameters.

4. A flow monitoring device based on a wireless intelligent monitoring ring according to claim 1, characterized in that: The specific steps of the data processing unit for compensating and calibrating the water flow data according to the environmental data are as follows: S31, collecting actual water flow parameter data under different historical environments and water flow parameter data measured by the wireless intelligent monitoring ring, obtaining a historical environment and monitoring parameter data matrix and a historical actual water flow parameter data matrix, and using the historical actual water flow parameter data matrix as a label matrix of the historical environment and monitoring parameter data matrix; S32, construct an initial CNN neural network model, and set the initial weights of the initial CNN neural network model to c 1. The initial bias is d 1. The initial learning rate is e 1; Set the training ratio and test ratio of the initial CNN neural network model to f 1. f 2; S33, according to f 1. f 2. Divide the historical environment and monitoring parameter data matrix into a historical environment and monitoring parameter data training matrix and a historical environment and monitoring parameter data test matrix; correspondingly divide the historical actual water flow parameter data matrix into a historical actual water flow parameter data training matrix and a historical actual water flow parameter data test matrix; S34, inputting the historical environment and monitoring parameter data training matrix and the historical actual water flow parameter data training matrix into the initial CNN neural network model for training, and after the training is completed, obtaining the trained CNN neural network model; S35, inputting the historical environment and monitoring parameter data test matrix and the historical actual water flow parameter data test matrix into the trained CNN neural network model for testing, and after the test optimization is completed, obtaining the final CNN neural network model; S36, real-time monitoring of water flow parameter data matrix A and real-time environmental parameter data matrix B The data in is substituted into the final CNN neural network model to obtain the compensation and correction real-time water flow parameter data matrix C ,as follows, ； in, Cin Indicates the compensation correction of the real-time water flow parameter data matrix i After the compensation calibration of the first water gun n Real-time water flow parameter data.

5. A flow monitoring device based on a wireless intelligent monitoring ring according to claim 4, characterized in that: The specific steps of S34 are as follows: S341, setting a training error threshold and a maximum number of training iterations; S342, inputting the historical environment and monitoring parameter data training matrix and the historical actual water flow parameter data training matrix into the initial CNN neural network model, and repeatedly training the initial CNN neural network model; S343. According to the training results of each round, the initial weights, initial biases and initial learning rates of the initial CNN neural network model are adjusted to obtain an adjusted CNN neural network model. The adjusted CNN neural network model is continued to be trained. When the training error is ≤ the training error threshold or the current number of iterations is ≥ the maximum number of training iterations, the training is stopped to obtain a trained CNN neural network model.

6. A flow monitoring device based on a wireless intelligent monitoring ring according to claim 4, characterized in that: The specific steps of S35 are as follows: S351, setting a test accuracy threshold, inputting the historical environment and monitoring parameter data test matrix into the trained CNN neural network model for testing, obtaining a test output result, and comparing the test output result with the historical actual water flow data test matrix to obtain a test accuracy; S352, when the test accuracy is greater than or equal to the test accuracy threshold, using the trained CNN neural network model as the final CNN neural network model; S353. When the test accuracy is less than the test accuracy threshold, the trained CNN neural network model is optimized by combining the grid search with the cross-validation algorithm. After the optimization is completed, the final CNN neural network model is obtained.

7. A flow monitoring device based on a wireless intelligent monitoring ring according to claim 6, characterized in that: In S353, the trained CNN neural network model is optimized by combining grid search with cross-validation algorithm. After the optimization is completed, the specific steps of obtaining the final CNN neural network model are as follows: S3531, set the parameter search space to g , the maximum number of random searches is h , set the cross validation fold to X ; S3532, Initialize the optimal parameter combination , the initialization optimal performance is v ; Randomly select parameter combinations , build a CNN neural network model based on the selected parameter combination, and divide the training data into X Fold, for each fold x , ; Use except x All training data outside the fold are used to train the CNN neural network model. x The training data of the fold is used to evaluate the model and obtain the performance indicator set , calculate the mean of the performance indicators of all folds and get the average performance u , the calculation formula is as follows, ； If the average performance u > Best performance v , then the updated best performance is u , update the optimal parameter combination to , and update the training accuracy; otherwise, keep the original best performance, optimal parameter combination and training accuracy; S3533, repeat S3532, when the maximum number of random searches is reached g Or when the training accuracy is ≥ the training accuracy threshold, the iteration is stopped to obtain the optimal parameter combination, and the optimal parameter combination is used as the parameters of the CNN neural network model to obtain the final CNN neural network model.

8. A flow monitoring device based on a wireless intelligent monitoring ring according to claim 1, characterized in that: The back-end server is used to calculate the water output of each water gun and the remaining water volume in the vehicle-mounted water tank according to the water flow data after compensation calibration; the specific steps of adjusting the water output of the water gun according to the water output of each water gun and the remaining water volume in the vehicle-mounted water tank are as follows: S41, the back-end server establishes a fluid mechanics model according to the water flow parameter set, substitutes the water flow parameter data of each water gun in the compensation and correction real-time water flow parameter data matrix into the fluid mechanics model, and obtains the water output of each water gun and the remaining water volume of the vehicle-mounted water tank; S42. According to the water output of each water gun and the remaining water volume in the vehicle-mounted water tank, the back-end server sends an instruction to adjust the water output of the water gun.

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