A water meter intelligent monitoring system and monitoring method

Abstract

The present invention discloses a water meter intelligent monitoring system and monitoring method, which relates to the field of data monitoring; the system includes: a data acquisition module, the data acquisition module includes a CCD camera and a flow rate sensor and a flow sensor built into the intelligent water meter; a monitoring terminal, the monitoring terminal has a built-in data processing module and a data storage module, and is used to receive, process and store the collected data; a communication module, which is used for the communication connection between the data acquisition module and the monitoring terminal, and is used to transmit the data collected by the data acquisition module to the monitoring terminal. The method includes the following steps: water meter installation and initialization, image recognition and data acquisition, wireless transmission, data processing, and alarm. The present invention adopts image recognition technology and sensors to collect data, which greatly improves the accuracy of monitoring compared to simply using sensor recognition. At the same time, the Internet of Things platform processes and analyzes the received data, further improving the accuracy of the data.

Description

Technical Field

[0001] The present invention relates to the field of data monitoring, and in particular to a water meter intelligent monitoring system and a monitoring method. Background Art

[0002] With the development of smart cities, the widespread deployment of wireless IoT monitoring equipment and the popularization of automatic electronic water meter reading have enabled the collected water use data to play a wide role in abnormal monitoring and data mining, realizing abnormal monitoring of water use data, which is beneficial to water use early warning, transportation management, pipeline leak detection and other scenarios.

[0003] After searching, a Chinese patent publication number CN116304962B discloses an intelligent anomaly monitoring method for water meter measurement data, the method comprising: obtaining historical water meter data, determining a first trend factor and a second trend factor of daily historical water meter data; mapping the historical water meter data to an annual data range to obtain mapped water meter data, and determining a first entropy value and a second entropy value based on the mapped water meter data; further determining a first selection weight and a second selection weight, and determining the data weights at different time points based on the first selection weight, the second selection weight, the first trend factor and the second trend factor; constructing an isolated decision tree based on the historical water meter data, calculating a weighted anomaly score, and determining abnormal data from the historical water meter data based on the weighted anomaly score.

[0004] The above patent has the following deficiencies: the means for acquiring water meter data is single, so the acquired data cannot be verified, which results in low acquisition accuracy. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides a water meter intelligent monitoring system, comprising:

[0006] Data acquisition module, the data acquisition module includes a CCD camera and a flow rate sensor and a flow sensor built into the smart water meter;

[0007] Monitoring terminal, the monitoring terminal has a built-in data processing module and a data storage module, and is used to receive, process and store the collected data;

[0008] The communication module is used for communication connection between the data acquisition module and the monitoring terminal, and is used to transmit the data collected by the data acquisition module to the monitoring terminal.

[0009] A water meter intelligence and monitoring method, comprising the following steps:

[0010] S1: Water meter installation and initialization: Install the smart water meter on the water supply pipe and perform initialization settings, including setting the water meter number, installation location, and communication parameters. The smart water meter has a built-in sensor to collect water flow data.

[0011] S2: Image recognition and data acquisition: using machine vision based on image recognition technology, a CCD camera is used to photograph the water meter under test, and the water meter reading is identified through image processing algorithms; at the same time, the built-in sensor of the smart water meter collects water flow and flow rate data in real time;

[0012] S3: Wireless transmission, using multi-threaded transmission mode, the collected data is transmitted to the monitoring terminal;

[0013] S4: Data processing: the monitoring terminal processes the data, analyzes whether there is any abnormal data, and then stores it;

[0014] S5: Alarm, alarm will be issued when there is abnormal data.

[0015] Preferably: in step S2, the method of obtaining the water meter reading using a CCD camera comprises the following steps:

[0016] S21: Image preprocessing, preprocessing the acquired water meter image, including grayscale, denoising, and binarization;

[0017] S22: region positioning, using edge detection algorithm or contour extraction algorithm to locate the position of the water meter in the image; S23: region segmentation, further segmenting the region of the water meter into a digital region and a pointer region;

[0018] S24: Digital recognition, using the deep learning algorithm of convolutional neural network to identify digital areas;

[0019] S25: pointer identification, using template matching or Hough transform to identify the position and direction of the pointer, then calculating the relative position of the pointer and the dial, and then reading the water meter reading in combination with the digital position identified in step S24. Preferably: in the step S3, the data transmission adopts a multi-threaded transmission mode, which includes the following steps: S31: data classification, classifying the data to form multiple independent sub-data tables;

[0020] S32: data sorting and caching, for each independent sub-data table, sorting it and then storing it in the cache;

[0021] S33: Processing thread operations, establishing a corresponding number of communication threads according to the number of sub-data tables;

[0022] S34: Communicate using threads according to the acquired request data, and perform encryption processing during the communication process.

[0023] Preferably: in the step S34, the method for data encryption comprises the following steps:

[0024] S341: Divide the data into data blocks with the number of threads doubled; and number each sub-block sequentially

[0025] S342: Encrypt different sub-blocks using different mathematical functions;

[0026] S343: The encrypted sub-blocks are then communicated using different threads.

[0027] Preferably: the step S342 specifically includes the following steps:

[0028] S3421: Establish a mapping function set f i a And the encryption function set f i b ;

[0029] S3422: For the original data, firstly select a corresponding mapping function according to the number of the sub-block, and transform the data through mapping to obtain a result;

[0030] S3423: Select an encryption function corresponding to the number in the encryption function according to the integer part of the obtained result;

[0031] S3424: The result of the mapping function transformation is then transformed twice using an encryption function to obtain a ciphertext.

[0032] Preferably: in S3421, the mapping function is:

[0033] where x min is the minimum value in the data, x max is the maximum value in the data;

[0034] Where e is the base of natural logarithms;

[0035] where x min is the minimum value in the data, x max is the maximum value in the data;

[0036] f a (x) = x p , where p<1;

[0037] in Any combination of .

[0038] Preferably: in the step S4, the method for analyzing whether there is abnormal data includes the following steps:

[0039] S41: Setting the offset ratio threshold p;

[0040] S42: Obtain a normal reading value M based on previous normal reading data of the water meter;

[0041] S43: taking the average of the value obtained by the smart water meter sensor and the value read by the CCD camera to obtain a collection value N;

[0042] S44: If N∈(M-pM, M+pM), the data is determined to be normal, otherwise the data is abnormal.

[0043] Preferably: in the step S42, Among them, M i is the collected data of the normal node i from the current collection node, k i is the weight.

[0044] Preferably: in S42, k i Greater than k i+1 .

[0045] The beneficial effects of the present invention are embodied in:

[0046] 1. The present invention adopts image recognition technology and sensors to collect data, which greatly improves the accuracy of monitoring compared with simply using sensor recognition. At the same time, the Internet of Things platform processes and analyzes the received data, further improving the accuracy of the data.

[0047] 2. The present invention increases data security by utilizing multi-threaded communication and then encrypting the data after dividing it into blocks.

[0048] 3. The present invention adopts a control form of threshold and coefficient for abnormal comparison, so as to realize early warning, and the threshold is calculated comprehensively based on the previous normal value, which increases the accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0050] Figure 1 A flow chart of a water meter intelligent monitoring method proposed by the present invention;

[0051] Figure 2 This is a flow chart of image recognition and data collection of a water meter intelligent monitoring method proposed by the present invention;

[0052] Figure 3 A wireless transmission logic diagram of a water meter intelligent monitoring method proposed by the present invention;

[0053] Figure 4 This is a data processing flow chart of a water meter intelligent monitoring method proposed in the present invention. DETAILED DESCRIPTION

[0054] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0055] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0056] Embodiment 1:

[0057] A water meter intelligent monitoring system, comprising:

[0058] Data acquisition module, the data acquisition module includes a CCD camera and a flow rate sensor and a flow sensor built into the smart water meter;

[0059] Monitoring terminal, the monitoring terminal has a built-in data processing module and a data storage module, and is used to receive, process and store the collected data;

[0060] The communication module is used for communication connection between the data acquisition module and the monitoring terminal, and is used to transmit the data collected by the data acquisition module to the monitoring terminal.

[0061] Embodiment 2:

[0062] A water meter intelligent monitoring method comprises the following steps:

[0063] S1: Water meter installation and initialization: Install the smart water meter on the water supply pipe and perform initialization settings, including setting the water meter number, installation location, and communication parameters. The smart water meter has a built-in sensor to collect water flow data.

[0064] S2: Image recognition and data acquisition: using machine vision based on image recognition technology, a CCD camera is used to photograph the water meter under test, and the water meter reading is identified through image processing algorithms; at the same time, the built-in sensor of the smart water meter collects water flow and flow rate data in real time;

[0065] S3: Wireless transmission, using multi-threaded transmission mode, the collected data is transmitted to the monitoring terminal;

[0066] S4: Data processing: the monitoring terminal processes the data, analyzes whether there is any abnormal data, and then stores it;

[0067] S5: Alarm, alarm will be issued when there is abnormal data.

[0068] Embodiment 3:

[0069] A water meter intelligent monitoring method comprises the following steps:

[0070] S1: Water meter installation and initialization: Install the smart water meter on the water supply pipe and perform initialization settings, including setting the water meter number, installation location, and communication parameters. The smart water meter has a built-in sensor to collect water flow data.

[0071] S2: Image recognition and data acquisition: using machine vision based on image recognition technology, a CCD camera is used to photograph the water meter under test, and the water meter reading is identified through image processing algorithms; at the same time, the built-in sensor of the smart water meter collects water flow and flow rate data in real time;

[0072] S3: Wireless transmission, using multi-threaded transmission mode, the collected data is transmitted to the monitoring terminal;

[0073] S4: Data processing: the monitoring terminal processes the data, analyzes whether there is any abnormal data, and then stores it;

[0074] S5: Alarm, alarm will be issued when there is abnormal data.

[0075] In step S2, the method for obtaining water meter readings using a CCD camera comprises the following steps:

[0076] S21: Image preprocessing, preprocessing the acquired water meter image, including grayscale, denoising, and binarization;

[0077] S22: region positioning, using edge detection algorithm or contour extraction algorithm to locate the position of the water meter in the image;

[0078] S23: area segmentation, further segmenting the area of ​​the water meter into a digital area and a pointer area;

[0079] S24: Digital recognition, using the deep learning algorithm of convolutional neural network to identify digital areas;

[0080] S25: Pointer recognition, using template matching or Hough transform to recognize the position and direction of the pointer, and then calculating the relative position of the pointer and the dial, and then reading the water meter reading in combination with the digital position recognized in step S24.

[0081] Embodiment 4:

[0082] A water meter intelligent monitoring method comprises the following steps:

[0083] S1: Water meter installation and initialization: Install the smart water meter on the water supply pipe and perform initialization settings, including setting the water meter number, installation location, and communication parameters. The smart water meter has a built-in sensor to collect water flow data.

[0084] S2: Image recognition and data acquisition: using machine vision based on image recognition technology, a CCD camera is used to photograph the water meter under test, and the water meter reading is identified through image processing algorithms; at the same time, the built-in sensor of the smart water meter collects water flow and flow rate data in real time;

[0085] S3: Wireless transmission, using multi-threaded transmission mode, the collected data is transmitted to the monitoring terminal;

[0086] S4: Data processing: the monitoring terminal processes the data, analyzes whether there is any abnormal data, and then stores it;

[0087] S5: Alarm, alarm will be issued when there is abnormal data.

[0088] In step S2, the method for obtaining water meter readings using a CCD camera comprises the following steps:

[0089] S21: Image preprocessing, preprocessing the acquired water meter image, including grayscale, denoising, and binarization;

[0090] S22: region positioning, using edge detection algorithm or contour extraction algorithm to locate the position of the water meter in the image;

[0091] S23: area segmentation, further segmenting the area of ​​the water meter into a digital area and a pointer area;

[0092] S24: Digital recognition, using the deep learning algorithm of convolutional neural network to identify digital areas;

[0093] S25: Pointer recognition, using template matching or Hough transform to recognize the position and direction of the pointer, and then calculating the relative position of the pointer and the dial, and then reading the water meter reading in combination with the digital position recognized in step S24.

[0094] In the step S3, data transmission adopts a multi-threaded transmission mode, which includes the following steps:

[0095] S31: Data classification, classifying the data to form multiple independent sub-data tables;

[0096] S32: data sorting and caching, for each independent sub-data table, sorting it and then storing it in the cache;

[0097] S33: Processing thread operations, establishing a corresponding number of communication threads according to the number of sub-data tables;

[0098] S34: Communicate using threads according to the acquired request data, and perform encryption processing during the communication process.

[0099] Embodiment 5:

[0100] A water meter intelligent monitoring method comprises the following steps:

[0101] S1: Water meter installation and initialization: Install the smart water meter on the water supply pipe and perform initialization settings, including setting the water meter number, installation location, and communication parameters. The smart water meter has a built-in sensor to collect water flow data.

[0102] S2: Image recognition and data acquisition: using machine vision based on image recognition technology, a CCD camera is used to photograph the water meter under test, and the water meter reading is identified through image processing algorithms; at the same time, the built-in sensor of the smart water meter collects water flow and flow rate data in real time;

[0103] S3: Wireless transmission, using multi-threaded transmission mode, the collected data is transmitted to the monitoring terminal;

[0104] S4: Data processing: the monitoring terminal processes the data, analyzes whether there is any abnormal data, and then stores it;

[0105] S5: Alarm, alarm will be issued when there is abnormal data.

[0106] In step S2, the method for obtaining water meter readings using a CCD camera comprises the following steps:

[0107] S21: Image preprocessing, preprocessing the acquired water meter image, including grayscale, denoising, and binarization;

[0108] S22: region positioning, using edge detection algorithm or contour extraction algorithm to locate the position of the water meter in the image;

[0109] S23: area segmentation, further segmenting the area of ​​the water meter into a digital area and a pointer area;

[0110] S24: Digital recognition, using the deep learning algorithm of convolutional neural network to identify digital areas;

[0111] S25: Pointer recognition, using template matching or Hough transform to recognize the position and direction of the pointer, and then calculating the relative position of the pointer and the dial, and then reading the water meter reading in combination with the digital position recognized in step S24.

[0112] In the step S3, data transmission adopts a multi-threaded transmission mode, which includes the following steps:

[0113] S31: Data classification, classifying the data to form multiple independent sub-data tables;

[0114] S32: data sorting and caching, for each independent sub-data table, sorting it and then storing it in the cache;

[0115] S33: Processing thread operations, establishing a corresponding number of communication threads according to the number of sub-data tables;

[0116] S34: Communicate using threads according to the acquired request data, and perform encryption processing during the communication process.

[0117] In the step S34, the data encryption method includes the following steps:

[0118] S341: Divide the data into data blocks with the number of threads doubled; and number each sub-block sequentially

[0119] S342: Encrypt different sub-blocks using different mathematical functions;

[0120] S343: The encrypted sub-blocks are then communicated using different threads.

[0121] The step S342 specifically includes the following steps:

[0122] S3421: Establish a mapping function set f i a And the encryption function set f i b ;

[0123] S3422: For the original data, firstly select a corresponding mapping function according to the number of the sub-block, and transform the data through mapping to obtain a result;

[0124] S3423: Select an encryption function corresponding to the number in the encryption function according to the integer part of the obtained result;

[0125] S3424: The result of the mapping function transformation is then transformed twice using an encryption function to obtain a ciphertext.

[0126] Embodiment 6:

[0127] A water meter intelligent monitoring method comprises the following steps:

[0128] S1: Water meter installation and initialization: Install the smart water meter on the water supply pipe and perform initialization settings, including setting the water meter number, installation location, and communication parameters. The smart water meter has a built-in sensor to collect water flow data.

[0129] S2: Image recognition and data acquisition: using machine vision based on image recognition technology, a CCD camera is used to photograph the water meter under test, and the water meter reading is identified through image processing algorithms; at the same time, the built-in sensor of the smart water meter collects water flow and flow rate data in real time;

[0130] S3: Wireless transmission, using multi-threaded transmission mode, the collected data is transmitted to the monitoring terminal;

[0131] S4: Data processing: the monitoring terminal processes the data, analyzes whether there is any abnormal data, and then stores it;

[0132] S5: Alarm, alarm will be issued when there is abnormal data.

[0133] In step S2, the method for obtaining water meter readings using a CCD camera comprises the following steps:

[0134] S21: Image preprocessing, preprocessing the acquired water meter image, including grayscale, denoising, and binarization;

[0135] S22: region positioning, using edge detection algorithm or contour extraction algorithm to locate the position of the water meter in the image;

[0136] S23: area segmentation, further segmenting the area of ​​the water meter into a digital area and a pointer area;

[0137] S24: Digital recognition, using the deep learning algorithm of convolutional neural network to identify digital areas;

[0138] S25: Pointer recognition, using template matching or Hough transform to recognize the position and direction of the pointer, and then calculating the relative position of the pointer and the dial, and then reading the water meter reading in combination with the digital position recognized in step S24.

[0139] In the step S3, data transmission adopts a multi-threaded transmission mode, which includes the following steps:

[0140] S31: Data classification, classifying the data to form multiple independent sub-data tables;

[0141] S32: data sorting and caching, for each independent sub-data table, sorting it and then storing it in the cache;

[0142] S33: Processing thread operations, establishing a corresponding number of communication threads according to the number of sub-data tables;

[0143] S34: Communicate using threads according to the acquired request data, and perform encryption processing during the communication process.

[0144] In the step S34, the data encryption method includes the following steps:

[0145] S341: Divide the data into data blocks with the number of threads doubled; and number each sub-block sequentially

[0146] S342: Encrypt different sub-blocks using different mathematical functions;

[0147] S343: The encrypted sub-blocks are then communicated using different threads.

[0148] The step S342 specifically includes the following steps:

[0149] S3421: Establish a mapping function set f i a And the encryption function set f i b ;

[0150] S3422: For the original data, firstly select a corresponding mapping function according to the number of the sub-block, and transform the data through mapping to obtain a result;

[0151] S3423: Select an encryption function corresponding to the number in the encryption function according to the integer part of the obtained result;

[0152] S3424: The result of the mapping function transformation is then transformed twice using an encryption function to obtain a ciphertext.

[0153] The mapping function in S3421 is:

[0154] where x min is the minimum value in the data, x max is the maximum value in the data;

[0155] Where e is the base of natural logarithms;

[0156] where x min is the minimum value in the data, x max is the maximum value in the data.

[0157] Embodiment 7:

[0158] A water meter intelligent monitoring method comprises the following steps:

[0159] S1: Water meter installation and initialization: Install the smart water meter on the water supply pipe and perform initialization settings, including setting the water meter number, installation location, and communication parameters. The smart water meter has a built-in sensor to collect water flow data.

[0160] S2: Image recognition and data acquisition: using machine vision based on image recognition technology, a CCD camera is used to photograph the water meter under test, and the water meter reading is identified through image processing algorithms; at the same time, the built-in sensor of the smart water meter collects water flow and flow rate data in real time;

[0161] S3: Wireless transmission, using multi-threaded transmission mode, the collected data is transmitted to the monitoring terminal;

[0162] S4: Data processing: the monitoring terminal processes the data, analyzes whether there is any abnormal data, and then stores it;

[0163] S5: Alarm, alarm will be issued when there is abnormal data.

[0164] In step S2, the method for obtaining water meter readings using a CCD camera comprises the following steps:

[0165] S21: Image preprocessing, preprocessing the acquired water meter image, including grayscale, denoising, and binarization;

[0166] S22: region positioning, using edge detection algorithm or contour extraction algorithm to locate the position of the water meter in the image;

[0167] S23: area segmentation, further segmenting the area of ​​the water meter into a digital area and a pointer area;

[0168] S24: Digital recognition, using the deep learning algorithm of convolutional neural network to identify digital areas;

[0169] S25: Pointer recognition, using template matching or Hough transform to recognize the position and direction of the pointer, and then calculating the relative position of the pointer and the dial, and then reading the water meter reading in combination with the digital position recognized in step S24.

[0170] In the step S3, data transmission adopts a multi-threaded transmission mode, which includes the following steps:

[0171] S31: Data classification, classifying the data to form multiple independent sub-data tables;

[0172] S32: data sorting and caching, for each independent sub-data table, sorting it and then storing it in the cache;

[0173] S33: Processing thread operations, establishing a corresponding number of communication threads according to the number of sub-data tables;

[0174] S34: Communicate using threads according to the acquired request data, and perform encryption processing during the communication process.

[0175] In the step S34, the data encryption method includes the following steps:

[0176] S341: Divide the data into data blocks with the number of threads doubled; and number each sub-block sequentially

[0177] S342: Encrypt different sub-blocks using different mathematical functions;

[0178] S343: The encrypted sub-blocks are then communicated using different threads.

[0179] The step S342 specifically includes the following steps:

[0180] S3421: Establish a mapping function set f i a And the encryption function set f i b ;

[0181] S3422: For the original data, firstly select a corresponding mapping function according to the number of the sub-block, and transform the data through mapping to obtain a result;

[0182] S3423: Select an encryption function corresponding to the number in the encryption function according to the integer part of the obtained result;

[0183] S3424: The result of the mapping function transformation is then transformed twice using an encryption function to obtain a ciphertext.

[0184] The mapping function in S3421 is:

[0185] where x min is the minimum value in the data, x max is the maximum value in the data;

[0186] where x min is the minimum value in the data, x max is the maximum value in the data;

[0187] f a (x) = x p , where p<1;

[0188] in

[0189] Embodiment 8:

[0190] A water meter intelligent monitoring method comprises the following steps:

[0191] S1: Water meter installation and initialization: Install the smart water meter on the water supply pipe and perform initialization settings, including setting the water meter number, installation location, and communication parameters. The smart water meter has a built-in sensor to collect water flow data.

[0192] S2: Image recognition and data acquisition: using machine vision based on image recognition technology, a CCD camera is used to photograph the water meter under test, and the water meter reading is identified through image processing algorithms; at the same time, the built-in sensor of the smart water meter collects water flow and flow rate data in real time;

[0193] S3: Wireless transmission, using multi-threaded transmission mode, the collected data is transmitted to the monitoring terminal;

[0194] S4: Data processing: the monitoring terminal processes the data, analyzes whether there is any abnormal data, and then stores it;

[0195] S5: Alarm, alarm will be issued when there is abnormal data.

[0196] In step S2, the method for obtaining water meter readings using a CCD camera comprises the following steps:

[0197] S21: Image preprocessing, preprocessing the acquired water meter image, including grayscale, denoising, and binarization;

[0198] S22: region positioning, using edge detection algorithm or contour extraction algorithm to locate the position of the water meter in the image;

[0199] S23: area segmentation, further segmenting the area of ​​the water meter into a digital area and a pointer area;

[0200] S24: Digital recognition, using the deep learning algorithm of convolutional neural network to identify digital areas;

[0201] S25: Pointer recognition, using template matching or Hough transform to recognize the position and direction of the pointer, and then calculating the relative position of the pointer and the dial, and then reading the water meter reading in combination with the digital position recognized in step S24.

[0202] In the step S3, data transmission adopts a multi-threaded transmission mode, which includes the following steps:

[0203] S31: Data classification, classifying the data to form multiple independent sub-data tables;

[0204] S32: data sorting and caching, for each independent sub-data table, sorting it and then storing it in the cache;

[0205] S33: Processing thread operations, establishing a corresponding number of communication threads according to the number of sub-data tables;

[0206] S34: Communicate using threads according to the acquired request data, and perform encryption processing during the communication process.

[0207] In the step S34, the data encryption method includes the following steps:

[0208] S341: Divide the data into data blocks with the number of threads doubled; and number each sub-block sequentially

[0209] S342: Encrypt different sub-blocks using different mathematical functions;

[0210] S343: The encrypted sub-blocks are then communicated using different threads.

[0211] The step S342 specifically includes the following steps:

[0212] S3421: Establish a mapping function set f i a And the encryption function set f i b ;

[0213] S3422: For the original data, firstly select a corresponding mapping function according to the number of the sub-block, and transform the data through mapping to obtain a result;

[0214] S3423: Select an encryption function corresponding to the number in the encryption function according to the integer part of the obtained result;

[0215] S3424: The result of the mapping function transformation is then transformed twice using an encryption function to obtain a ciphertext.

[0216] The mapping function in S3421 is:

[0217] where x min is the minimum value in the data, x max is the maximum value in the data;

[0218] Where e is the base of natural logarithms;

[0219] where x min is the minimum value in the data, x max is the maximum value in the data;

[0220] in

[0221] Embodiment 9:

[0222] A water meter intelligent monitoring method comprises the following steps:

[0223] S1: Water meter installation and initialization: Install the smart water meter on the water supply pipe and perform initialization settings, including setting the water meter number, installation location, and communication parameters. The smart water meter has a built-in sensor to collect water flow data.

[0224] S2: Image recognition and data acquisition: using machine vision based on image recognition technology, a CCD camera is used to photograph the water meter under test, and the water meter reading is identified through image processing algorithms; at the same time, the built-in sensor of the smart water meter collects water flow and flow rate data in real time;

[0225] S3: Wireless transmission, using multi-threaded transmission mode, the collected data is transmitted to the monitoring terminal;

[0226] S4: Data processing: the monitoring terminal processes the data, analyzes whether there is any abnormal data, and then stores it;

[0227] S5: Alarm, alarm will be issued when there is abnormal data.

[0228] In step S2, the method for obtaining water meter readings using a CCD camera comprises the following steps:

[0229] S21: Image preprocessing, preprocessing the acquired water meter image, including grayscale, denoising, and binarization;

[0230] S22: region positioning, using edge detection algorithm or contour extraction algorithm to locate the position of the water meter in the image;

[0231] S23: area segmentation, further segmenting the area of ​​the water meter into a digital area and a pointer area;

[0232] S24: Digital recognition, using the deep learning algorithm of convolutional neural network to identify digital areas;

[0233] S25: Pointer recognition, using template matching or Hough transform to recognize the position and direction of the pointer, and then calculating the relative position of the pointer and the dial, and then reading the water meter reading in combination with the digital position recognized in step S24.

[0234] In the step S3, data transmission adopts a multi-threaded transmission mode, which includes the following steps:

[0235] S31: Data classification, classifying the data to form multiple independent sub-data tables;

[0236] S32: data sorting and caching, for each independent sub-data table, sorting it and then storing it in the cache;

[0237] S33: Processing thread operations, establishing a corresponding number of communication threads according to the number of sub-data tables;

[0238] S34: Communicate using threads according to the acquired request data, and perform encryption processing during the communication process.

[0239] In the step S34, the data encryption method includes the following steps:

[0240] S341: Divide the data into data blocks with the number of threads doubled; and number each sub-block sequentially

[0241] S342: Encrypt different sub-blocks using different mathematical functions;

[0242] S343: The encrypted sub-blocks are then communicated using different threads.

[0243] The step S342 specifically includes the following steps:

[0244] S3421: Establish a mapping function set f i a And the encryption function set f i b ;

[0245] S3422: For the original data, firstly select a corresponding mapping function according to the number of the sub-block, and transform the data through mapping to obtain a result;

[0246] S3423: Select an encryption function corresponding to the number in the encryption function according to the integer part of the obtained result;

[0247] S3424: The result of the mapping function transformation is then transformed twice using an encryption function to obtain a ciphertext.

[0248] The mapping function in S3421 is:

[0249] where x min is the minimum value in the data, x max is the maximum value in the data;

[0250] Where e is the base of natural logarithms;

[0251] where x min is the minimum value in the data, x max is the maximum value in the data;

[0252] f a (x) = x p , where p<1;

[0253] in

[0254] Any combination of .

[0255] In step S4, the method for analyzing whether there is abnormal data includes the following steps:

[0256] S41: Setting the offset ratio threshold p;

[0257] S42: Obtain a normal reading value M based on previous normal reading data of the water meter;

[0258] S43: taking the average of the value obtained by the smart water meter sensor and the value read by the CCD camera to obtain a collection value N;

[0259] S44: If N∈(M-pM, M+pM), the data is determined to be normal, otherwise the data is abnormal.

[0260] In the step S42, Among them, M i is the collected data of the normal node i from the current collection node, k i is the weight.

[0261] In S42, k i Greater than k i+1 .

[0262] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A water meter intelligent monitoring system, characterized in that: include: Data acquisition module, the data acquisition module includes a CCD camera and a flow rate sensor and a flow sensor built into the smart water meter; Monitoring terminal, the monitoring terminal has a built-in data processing module and a data storage module, and is used to receive, process and store the collected data; The communication module is used for communication connection between the data acquisition module and the monitoring terminal, and is used to transmit the data collected by the data acquisition module to the monitoring terminal.

2. A water meter intelligent monitoring method, applied to the monitoring system of the water meter intelligent monitoring system according to claim 1, characterized in that: It includes the following steps: S1: Water meter installation and initialization: Install the smart water meter on the water supply pipe and perform initialization settings, including setting the water meter number, installation location, and communication parameters. The smart water meter has a built-in sensor to collect water flow data. S2: Image recognition and data acquisition: using machine vision based on image recognition technology, a CCD camera is used to photograph the water meter under test, and the water meter reading is identified through image processing algorithms; at the same time, the built-in sensor of the smart water meter collects water flow and flow rate data in real time; S3: Wireless transmission, using multi-threaded transmission mode, the collected data is transmitted to the monitoring terminal; S4: Data processing: the monitoring terminal processes the data, analyzes whether there is any abnormal data, and then stores it; S5: Alarm, alarm will be issued when there is abnormal data.

3. A water meter intelligent monitoring method according to claim 2, characterized in that: In step S2, the method for obtaining water meter readings using a CCD camera comprises the following steps: S21: Image preprocessing, preprocessing the acquired water meter image, including grayscale, denoising, and binarization; S22: region positioning, using edge detection algorithm or contour extraction algorithm to locate the position of the water meter in the image; S23: area segmentation, further segmenting the area of ​​the water meter into a digital area and a pointer area; S24: Digital recognition, using the deep learning algorithm of convolutional neural network to identify digital areas; S25: Pointer recognition, using template matching or Hough transform to recognize the position and direction of the pointer, and then calculating the relative position of the pointer and the dial, and then reading the water meter reading in combination with the digital position recognized in step S24.

4. A water meter intelligent monitoring method according to claim 2, characterized in that: In the step S3, data transmission adopts a multi-threaded transmission mode, which includes the following steps: S31: Data classification, classifying the data to form multiple independent sub-data tables; S32: data sorting and caching, for each independent sub-data table, sorting it and then storing it in the cache; S33: Processing thread operations, establishing a corresponding number of communication threads according to the number of sub-data tables; S34: Communicate using threads according to the acquired request data, and perform encryption processing during the communication process.

5. A water meter intelligent monitoring method according to claim 4, characterized in that: In the step S34, the data encryption method includes the following steps: S341: Divide the data into data blocks with the number of threads doubled; and number each sub-block sequentially S342: Encrypt different sub-blocks using different mathematical functions; S343: The encrypted sub-blocks are then communicated using different threads.

6. A water meter intelligent monitoring method according to claim 5, characterized in that: The step S342 specifically includes the following steps: S3421: Establish a mapping function set f i a And the encryption function set f i b ; S3422: For the original data, firstly select a corresponding mapping function according to the number of the sub-block, and transform the data through mapping to obtain a result; S3423: Select an encryption function corresponding to the number in the encryption function according to the integer part of the obtained result; S3424: The result of the mapping function transformation is then transformed twice using an encryption function to obtain a ciphertext.

7. A water meter intelligent monitoring method according to claim 6, characterized in that: The mapping function in S3421 is: where x min is the minimum value in the data, x max is the maximum value in the data; Where e is the base of natural logarithms; where x min is the minimum value in the data, x max is the maximum value in the data; f a (x) = x p , where p<1; in Any combination of .

8. A water meter intelligent monitoring method according to claim 2, characterized in that: In step S4, the method for analyzing whether there is abnormal data includes the following steps: S41: Setting the offset ratio threshold p; S42: Obtain a normal reading value M based on previous normal reading data of the water meter; S43: taking the average of the value obtained by the smart water meter sensor and the value read by the CCD camera to obtain a collection value N; S44: If N∈(M-pM, M+pM), the data is determined to be normal, otherwise the data is abnormal.

9. A water meter intelligent monitoring method according to claim 8, characterized in that: In the step S42, Among them, M i is the collected data of the normal node i from the current collection node, k i is the weight.

10. A water meter intelligent monitoring method according to claim 9, characterized in that: In S42, k i Greater than k i+1 .

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