Smart phone-based irrigation area intelligent inspection system and inspection method

Abstract

The present invention discloses an intelligent inspection system for irrigation districts based on smartphones. It includes a background management system and a mobile terminal inspection App system; the mobile terminal inspection App system communicates data with the background management system via a 4G / 5G wireless network or a VPN dedicated line; the content of the data communication includes the background management system pushing inspection tasks and data information to the mobile terminal and the mobile terminal submitting inspection information to the background management system; the background management system implements functions such as formulating inspection tasks and planning inspection routes; the mobile terminal inspection App system implements functions such as mobile positioning and inspection trajectory map generation. The present invention has the advantages of saving inspection costs, reducing the risk of missed inspections of inspection objects, preventing inspection personnel from being lazy and cheating, improving inspection efficiency, enhancing the risk judgment and predictive analysis of the working conditions of inspection objects, and automatic data synchronization between the mobile terminal and the background. The present invention also discloses an inspection method for an intelligent inspection system for irrigation districts based on smartphones.

Description

Technical Field

[0001] The present invention relates to the field of safe operation management of irrigation districts, and more specifically, to a smart phone-based irrigation district intelligent inspection system. The present invention also relates to an inspection method for the smart phone-based irrigation district intelligent inspection system. Background Art

[0002] Irrigation districts are crucial infrastructure for agricultural and rural economic development and serve as important production bases for agricultural products in my country. With the continued advancement of irrigation district construction, supporting facilities, and renovation projects across my country, irrigation district informatization has become a key component of irrigation district renovation, serving as a crucial measure to improve irrigation district management and efficiency and achieve modernization.

[0003] Irrigation districts are characterized by large irrigation areas, long canal systems, and a wide distribution, variety, and large number of projects. As the foundation of irrigation district informatization, the development of irrigation district perception systems is receiving increasing attention. During the renovation of irrigation district projects, perception systems for water and rainfall conditions, water quality, soil moisture conditions, and project safety have largely achieved automated data collection. Irrigation district project inspections are a crucial complement to the development of these perception systems. They can promptly identify potential risks and are an indispensable tool for the safe operation and management of irrigation district projects. Due to the large number of inspection targets, their diverse scope, and their dispersed distribution, manual inspections remain the primary method of irrigation district project inspections. Because multiple inspection targets must be inspected in a single inspection, inspectors often arrange inspection routes and sequences based on personal preferences. This lack of scientific route planning leads to detours, increased inspection costs, and the risk of missed inspections.

[0004] During field inspections, inspectors perform data processing and analysis tasks such as consulting materials, performing analytical calculations, and making comparative judgments. This workload is arduous and prone to errors. However, if an inspection analysis system could rapidly identify existing risks and generate alerts based on measured operating data and the parameter thresholds for the normal operating conditions of the various characteristic elements of the inspected object, the inspectors' risk analysis workload could be significantly reduced. Furthermore, with the continuous development and widespread application of big data technology, further research is needed to explore how to leverage this technology to predict and analyze the risks of inspected objects based on historical monitoring data, thereby identifying risk trends early on.

[0005] Using mobile smart devices to assist in inspections is an effective means of improving the efficiency and effectiveness of irrigation district inspections. For example, generating inspection trajectory maps through mobile positioning and inspection clocking in is an important technical measure to prevent inspection personnel from missing inspections; reading monitoring data through mobile terminal systems, filling in inspection information on site, and uploading videos, pictures and other data are important means of improving inspection efficiency. Smartphones are the most widely used smart mobile terminals, and inspection terminal systems based on smartphones have the advantages of high popularity, ease of use, and portability. Commonly used apps with positioning functions on smartphones are highly dependent on wireless networks. Some inspection points in irrigation districts are located in remote areas with weak or even no wireless network signals. Therefore, it is necessary to develop mobile positioning function modules to solve the problem of mobile positioning's dependence on wireless network signals, realize offline positioning functions, propose mobile inspection data transmission solutions, and solve the problem of synchronization between mobile inspection data and backend management systems in offline conditions. Summary of the Invention

[0006] The first purpose of the present invention is to provide an intelligent irrigation district inspection system based on a smart phone, which greatly reduces the workload of inspection personnel, improves inspection efficiency, enhances risk judgment and predictive analysis of inspection objects, and has great application value; at the same time, it uses the mobile phone's own GNSS (Global Navigation Satellite System) data to develop a mobile phone positioning function module to realize the positioning function in an offline state, and at the same time realizes the synchronization of mobile terminal inspection data and the background management system in an offline state, which is easy to use; solves the problem of mobile positioning's dependence on wireless network signals, and overcomes the deficiency that the inspection punch-in to generate a trajectory map is restricted by wireless network signals.

[0007] The second purpose of the present invention is to provide an inspection method for the smart phone-based irrigation district intelligent inspection system.

[0008] In order to achieve the above-mentioned first object of the present invention, the technical solution of the present invention is: an intelligent inspection system for irrigation areas based on a smart phone, characterized by comprising a background management system and a mobile terminal inspection App system;

[0009] The mobile terminal inspection App system communicates data with the backend management system via 4G / 5G wireless networks or VPN dedicated lines. Data communication includes the backend management system pushing inspection tasks and data information to the mobile terminal inspection App system, and the mobile terminal submitting inspection information to the backend management system.

[0010] The backend management system implements functions such as formulating inspection tasks, planning inspection routes, forecasting and analyzing inspection data, early warning, data reception and management, information query and data synchronization;

[0011] The mobile terminal inspection App system realizes functions such as mobile positioning, inspection trajectory map generation, on-site inspection information reporting, data upload, inspection alarm, information query and display.

[0012] In order to achieve the above-mentioned purpose and the second purpose of the present invention, the technical solution of the present invention is: the inspection method of the irrigation district intelligent inspection system based on smart phones is characterized by comprising the following steps:

[0013] Step 1: Build a comprehensive database for irrigation district inspections;

[0014] Build a comprehensive irrigation district inspection database to support the formulation of inspection tasks, inspection data management, risk analysis and early warning of inspection object working conditions, and inspection information query;

[0015] Step 2: Build an irrigation district inspection map;

[0016] Build an irrigation district inspection map based on the GIS platform to visualize the inspection data of the irrigation district;

[0017] Step 3: Build a risk analysis model library for inspection object working conditions;

[0018] Taking the inspection object as the unit, a working condition risk analysis model library for the inspection object is constructed. The basic information of the inspection object is set, and the parameter threshold value range of each characteristic element of the inspection object under normal working conditions (safe operation) in the standard specifications is preset, as well as the judgment conditions and rules for different working condition risk levels. This provides services for on-site inspection object working condition risk judgment, prediction analysis and alarm warning;

[0019] Step 4: Planning inspection tasks and inspection routes;

[0020] Inspection personnel send inspection task requests to the backend management system through the mobile terminal inspection app system based on the irrigation district they are in. The backend management system automatically extracts all inspection points in the irrigation district based on the irrigation district information, performs route planning, determines the inspection order of each inspection point, generates an inspection route, formulates an inspection task, and pushes the inspection task to the mobile terminal inspection app system. All inspection points and inspection routes included in this inspection task are visually displayed on an irrigation district inspection map.

[0021] Step 5: Check in for inspection and generate an inspection trajectory map;

[0022] When the inspection personnel arrive at each inspection point, they must first scan the QR code of the inspection object to obtain the basic information of the inspection object, which includes the risk analysis model data of the inspection object's operating conditions, the geographical coordinate information of the inspection point, etc. When the inspection personnel scan the QR code, the positioning function module of the mobile terminal App system automatically locates the location of the scanning point (i.e., the scanning location point), obtains the geographical coordinate information of the scanning location point, calculates the straight-line distance L1 between the scanning location point and the inspection object location point, sets the distance threshold ΔL and compares L1 with The size of ΔL, if L1≤ΔL, the inspection punch-in is successful, and the inspection punch-in related information is recorded to generate the inspection track point, including the punch-in person, punch-in time, coordinate information, and inspection object information. If L1>ΔL, the inspection punch-in fails and the inspection track point cannot be generated. The present invention compares the coordinate information with the inspection point itself. When the distance between the two coordinates is less than a given threshold, the inspection punch-in is successful, and the inspection punch-in related information is recorded to generate the inspection track point, including the punch-in person, punch-in time, coordinate information, inspection object information, etc.

[0023] Step 6: Fill in the inspection information on site;

[0024] Inspection personnel fill in inspection information on-site through the mobile terminal inspection app system, including inspection records, observation values ​​of characteristic elements of the inspection objects, photos and videos taken on-site, etc.

[0025] Step 7: Risk warning of working condition of the inspection object;

[0026] According to the inspection object working condition model data and on-site inspection object characteristic element observation data, the mobile terminal inspection App system obtains the inspection object working condition risk analysis model library parameter information and performs analysis and comparison. If the characteristic element F i The observed value S i The working condition risk analysis model parameter P corresponding to this factor i The difference between them is greater than or equal to the given threshold ΔT i , that is: Delt_i ≥ ΔT i , the smart inspection system for irrigation districts based on smartphones generates alarm information and displays it visually in a graph using colors, symbols, etc.; if Delt_i<ΔT i , then it is considered that there is no risk and no warning information is generated;

[0027] Step 8: Risk prediction analysis and early warning of the inspection object's working conditions;

[0028] A risk prediction and analysis model for the operating conditions of inspection objects is constructed. Using big data analysis methods, a risk development trend prediction analysis of the safe operating conditions of the inspection objects is conducted based on the historical inspection data of the inspection objects. The risk development trend prediction value for a given period in the future is obtained. If the prediction value exceeds the set threshold, an early warning message is generated and visualized in a graph; if the prediction value does not exceed the set threshold, no early warning message is generated, indicating that the current operating condition of the inspection object is normal.

[0029] In the above technical solution, in step 1, the inspection comprehensive database mainly includes the following data:

[0030] 1) Irrigation district basic data, including basic information data of irrigation districts, basic data of irrigation projects, canal system data, basic data of inspection points (inspection objects), etc.

[0031] 2) Inspection data: Irrigation district inspections include visual inspections and instrument detection; inspection personnel fill in and collect data on-site at each inspection point, including instrument detection data, inspection personnel records, and on-site pictures or videos;

[0032] 3) Inspection task data: Inspection tasks are generated by the background, and inspection task data includes inspection point list, inspection personnel information, inspection route data, etc.

[0033] 4) Model data: Construct a risk analysis model for the operating conditions of the inspection object, which mainly includes basic information of the inspection object, thresholds for normal operating conditions of various characteristic elements under different conditions in the standard specifications, and judgment conditions and rules for different operating condition risk levels. It is mainly used for alarm and early warning analysis of the operating conditions of the inspection object;

[0034] 5) Spatial data; mainly includes spatial distribution data of irrigation area water conservancy projects, road network structure data, canal system data, spatial distribution data of inspection objects, as well as administrative divisions, remote sensing images and other geographic spatial data.

[0035] In the above technical solution, in step 2, the irrigation area inspection data mainly includes the following layers: 1) irrigation area scope and irrigation area segments (surface layer); 2) irrigation area canal system (line layer); 3) sluice gates (including regulating gates, diversion gates, and water discharge gates, point layer); 4) pumping stations (including water lifting pumping stations and drainage pumping stations, point layer); 5) culverts (line layer); 6) inverted siphons (line layer); 7) aqueducts (line layer); 8) dangerous sections of irrigation areas (point layer, line layer); 9) water system (line layer); 10) lakes (surface layer); 11) road network (line layer); 12) urban areas (line layer); 1) town (point layer); 13) administrative division (surface layer); 14) high-definition remote sensing image data of irrigation areas; 15) inspection routes; 16) inspection trajectory map; 17) inspection alarm and warning information distribution map; Spatial data is organized by layers, which can control the display status of each layer, set a unique code for each hydraulic structure / inspection object spatial element, and establish an association with the inspection data. The relevant data of each inspection object can be queried in one map. The relevant data of the inspection object includes the basic data of the inspection object, theoretical thresholds, data collected on-site by inspection personnel, inspection alarm and warning information, etc.

[0036] In the above technical solution, in step 4, the inspection route planning method includes the following steps:

[0037] S41: Set the location of the inspection personnel's work unit (i.e., starting location, also known as office location);

[0038] S42: Set the number of inspection teams and the inspection irrigation areas;

[0039] S43: Initialize data;

[0040] S44: path search;

[0041] S45: Path planning scheme;

[0042] S46: Determine whether the path planning solution meets the constraint conditions;

[0043] The constraints are: (1) Each inspection point can only be inspected by one inspection team; (2) one inspection team can perform inspections on multiple inspection points; (3) the starting point and the end point of each inspection team must be the same office;

[0044] When the constraint conditions are met, jump to step S49;

[0045] When the constraint condition is not met, jump to step S47:

[0046] S47: Compare with the original route;

[0047] S48: Determine whether the new route is better;

[0048] When the new route is better, jump to step S49;

[0049] When the new route is not better, jump to step S46;

[0050] S49: Determine whether the path planning solution obtained in S45 is the optimal solution;

[0051] If it is the optimal solution, jump to step S50;

[0052] If it is not the optimal solution, jump to step S44;

[0053] S50: Output plan;

[0054] S51: Inspection route planning is completed.

[0055] The present invention has the following advantages:

[0056] The present invention uses a path planning algorithm based on the inspection object list, combined with the inspection personnel's travel location and travel time, to carry out scientific and reasonable route planning for the inspection personnel, which not only saves travel costs but also avoids the risk of missed inspections at inspection points; the inspection clock-in to generate an inspection trajectory map is an effective means to prevent inspection personnel from missing inspections. Since the inspection clock-in requires the geographical coordinate information of the inspection personnel's clock-in point, in the wild area where the wireless network signal is weak or even no signal, the positioning function of the smart phone that relies on the network signal cannot be used normally, and clocking in is more difficult. Therefore, the present invention uses the mobile phone's own GNSS (Global Navigation Satellite System, Global Navigation Satellite System) data, develops a positioning function module that does not rely on network signals, overcomes the deficiency that the trajectory map generated by inspection punch-in is limited by wireless network signals; the present invention constructs a working condition risk analysis model for each inspection object, inputs the parameter thresholds of various characteristic elements of the inspection object under normal working conditions in the standard specifications, and when the inspection personnel enter the inspection information on site, the system can quickly determine whether the inspection object has working condition risks. If there is a risk, an alarm message is generated and visualized in the form of text, color, symbols, etc. in a GIS scene of a picture. Furthermore, big data analysis technology is used in the background, combined with historical inspection data to predict and analyze the development trend of the working condition risks of the inspection objects, so as to discover potential risks early.

[0057] The smart phone intelligent inspection method and system constructed by the present invention greatly reduces the workload of inspection personnel, improves inspection efficiency, enhances the risk judgment and predictive analysis of the working conditions of inspection objects, and has great application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 Overall structural diagram of the intelligent inspection system for irrigation areas in the present invention.

[0059] Figure 2 Flow chart of the inspection path planning algorithm (ant colony algorithm) in the present invention.

[0060] Figure 3 Flowchart of the calculation of precise single-point positioning of a mobile phone in the present invention.

[0061] Figure 4 Schematic diagram of the process of synchronizing inspection data between the mobile terminal inspection App system and the background system in the present invention.

[0062] Figure 5 Flowchart of risk analysis and prediction of working conditions of inspection objects in the present invention. DETAILED DESCRIPTION

[0063] The following detailed description of the embodiments of the present invention is given in conjunction with the accompanying drawings, which do not limit the present invention but are merely examples. The description makes the advantages of the present invention clearer and easier to understand.

[0064] Referring to the attached drawings, it can be seen that: an intelligent inspection system for irrigation areas based on smart phones includes a background management system and a mobile terminal inspection App system;

[0065] The mobile terminal inspection App system communicates data with the backend management system via 4G / 5G wireless networks or VPN dedicated lines. Data communication includes the backend management system pushing inspection tasks and data information to the mobile terminal, and the mobile terminal submitting inspection information to the backend management system.

[0066] The backend management system implements functions such as formulation of inspection tasks, planning of inspection routes, forecasting and analysis of inspection data and early warning, data reception and management, information query and data synchronization;

[0067] The mobile terminal inspection App system realizes the functions of mobile positioning, inspection trajectory map generation, on-site inspection information reporting, data upload, inspection alarm, information query and display (such as Figure 1 shown).

[0068] Referring to the accompanying drawings, it can be seen that the inspection method of the irrigation district intelligent inspection system based on a smartphone includes the following steps:

[0069] Step 1: Build a comprehensive irrigation district inspection database

[0070] A comprehensive irrigation district inspection database was constructed to store and manage data, completing data organization, entry, and update maintenance. This database primarily includes irrigation district basic information, irrigation district water conservancy project data, basic inspection object data, irrigation district road network data, inspection data, and irrigation district basic geographic spatial data. Irrigation district basic data, basic geographic spatial data, basic inspection object data, and inspection object operating condition model data are static data with low frequency of change and minimal content changes. They can be organized and stored as required. Inspection data is dynamic data and is updated incrementally.

[0071] Step 2: Build an irrigation district inspection map

[0072] A single-map visualization scene is constructed based on the GIS platform. Spatial data for irrigation district inspections is organized by layers, including: 1) irrigation district boundaries and divisions (area layer); 2) irrigation district canal systems (line layer); 3) sluice gates (including regulating gates, diversion gates, and discharge gates, point layer); 4) pumping stations (including lifting and drainage pumping stations, point layer); 5) culverts (line layer); 6) inverted siphons (line layer); 7) aqueducts (line layer); 8) hazardous sections in the irrigation district (point layer, line layer); 9) water systems (line layer); 10) lakes (area layer); 11) road networks (line layer); 12) towns (point layer); 13) administrative divisions (area layer); 14) high-definition remote sensing imagery of the irrigation district; 15) inspection routes; 16) inspection trajectory maps; and 17) inspection alarm and warning information distribution maps. Each layer can control its display status. A coding system is established for irrigation area inspection objects. Each water project structure and inspection object has a unique code, and is associated with inspection data, operating conditions, early warning information, etc. through this code. By selecting the spatial feature object in the layer, the corresponding basic information and inspection data can be queried.

[0073] Step 3: Build a risk analysis model library for inspection object working conditions

[0074] Establish a risk analysis model library for the working conditions of inspection objects. For each inspection object, according to the requirements of relevant standards and specifications, enter the safe operation parameter thresholds of various characteristic elements of the inspection object under normal working conditions in the standards and specifications, set the judgment conditions and rules for different working condition risk levels, and provide services for inspection object working condition risk judgment, prediction analysis and alarm warning;

[0075] Building a risk analysis model library for inspection objects' working conditions is a prerequisite for working condition risk analysis. Suppose a sluice includes the following information: (1) basic information such as the name, code, construction time, and grade of the sluice; (2) basic design parameters of the sluice; (3) various requirement data and threshold data for ensuring the safe operation of the project, such as the maximum flow capacity and the highest water level required in relevant codes and regulations; (4) the risk level of project operation and the judgment conditions for different risk levels; (5) historical / real-time monitoring data of each characteristic element. Organize the above information data effectively, and then set the risk judgment rules, and a working condition risk analysis model is formed, with actual monitoring data as the input and the working condition risk level as the output.

[0076] Step 4, Patrol task path planning

[0077] According to the patrol area determined by the patrol task, automatically extract all inspection objects within the irrigation area piece of this patrol, and construct a list of inspection points. According to the starting position where the patrol personnel execute the patrol task, use the path planning algorithm for path planning. Through the patrol task path planning, provide a scientific and reasonable patrol order and patrol path for the patrol personnel, saving patrol time and cost;

[0078] Patrol path planning is a typical VRP problem (Vehicle Routing Problem). The patrol team (one or more groups) starts from the starting position (the starting position is generally set as the usual office location of the patrol personnel), patrols several inspection points within the specified irrigation area piece, and finally returns to the starting position. The core of path planning is to obtain the optimal planning scheme according to the input under the given constraints. For patrol path planning, taking the minimum total patrol cost as the optimal planning scheme, in this invention, the path distance is used as the measurement index of patrol cost, that is, taking the shortest patrol path as the goal.

[0079] Known conditions:

[0080] (1) The number of inspection points is n, and each point is numbered i, where i = 0 represents the starting position of the patrol personnel;

[0081] (2) The number of patrol groups set by the irrigation area management office is m, and the number of each patrol group is k;

[0082] (3) The distance from the starting position to each inspection point and the distance between each inspection point is c ij (i = 0, 1, 2,..., n - 1; j = 1, 2,..., n; i < j; i = 0 represents the starting position of the patrol personnel).

[0083] Constraint conditions:

[0084] (1) Each inspection point can only be patrolled by one patrol group;

[0085] (2) A patrol inspection team can perform patrol inspection tasks on multiple inspection points;

[0086] (3) The starting point and the ending point of each patrol inspection team must be the same location, that is, the starting position.

[0087] The goal of the path planning function is to minimize the total cost of the patrol inspection trip. That is, taking the shortest total travel path as the objective function, a mathematical model is established:

[0088]

[0089] In the formula: c ij represents the distance from the office location to each inspection point and the distance between each inspection point (i = 0, 1,..., n - 1; j = 1, 2,..., n; i < j), where i = 0 represents the starting position; x ijk represents that the patrol inspection team k travels from inspection point i to inspection point j, and takes the value of 1 when the event occurs, otherwise takes the value of 0.

[0090] The constraint conditions are as follows:

[0091]

[0092]

[0093]

[0094] In the formula, y jk represents that the freight task of inspection point i is completed by patrol inspection team k, and takes the value of 1 when the event occurs, otherwise takes the value of 0

[0095] (Equation 1) is the objective function; (Equation 2) represents that each inspection point is inspected by and only by one patrol inspection team; (Equation 3) represents that if inspection point j is inspected by patrol inspection team k, then patrol inspection team k must travel from inspection point i to inspection point j. (Equation 4) represents that if inspection point i is inspected by patrol inspection team k, then after completing the inspection task of this point, patrol inspection team k must reach another inspection point j.

[0096] There are many mature algorithms for VRP path planning. The present invention uses the ant colony search algorithm in the heuristic algorithm for patrol inspection path planning. The algorithm flow is shown in Figure 2 as follows.

[0097] Step 5, perform patrol inspection check-in based on the positioning function module of the smartphone GNSS data, and generate a patrol inspection trajectory map;

[0098] This invention develops a mobile positioning module based on mobile phone GNSS data, enabling mobile phone positioning independent of the mobile network. By using a public data interface to acquire the phone's built-in GNSS observation data and employing the PPP positioning algorithm (Precise Point Positioning), this module is independent of wireless network signals, enabling mobile positioning even in the absence of a network signal.

[0099] Since the inspection points are relatively dispersed in space, the positioning accuracy requirements for mobile phone positioning during inspection and clocking in are not high. Positioning accuracy at the decimeter level or even the meter level can meet the positioning requirements of inspection and clocking in. The present invention adopts the mature PPP positioning algorithm to develop a positioning function module that does not rely on wireless network signals, and uses it to generate inspection trajectory maps for inspection and clocking in.

[0100] PPP positioning uses the precise satellite orbits and satellite clock errors calculated using GPS observation data from several ground tracking stations around the world to perform positioning solutions on the phase and pseudorange observations collected by a single GPS receiver.

[0101] PPP positioning is an absolute positioning technology that uses high-precision carrier phase and pseudorange observations, along with a range of precision products, to achieve high-precision positioning for a single receiver. Precise point positioning utilizes precise orbit, clock, and Earth rotation products provided by the International GNSS Service (IGS) or International GNSS Monitoring and Assessment System (iGMAS), along with mobile carrier and pseudorange observation data, to achieve high-precision positioning results.

[0102] In GNSS measurement, the observation equations of pseudorange and carrier phase are as follows:

[0103]

[0104]

[0105] Where P is the pseudorange observation value; is the carrier observation value; c is the speed of light in vacuum; is the receiver clock error; is the satellite clock error; V ion is the ionospheric delay; V rtop is the tropospheric delay; λ is the wavelength; N is the integer ambiguity; δρ is the effect of satellite ephemeris error on ranging; δρ mul is the multipath error; ε p is the pseudorange observation noise; is the carrier observation noise; ρ is the distance between the satellite and the receiver.

[0106]

[0107] (x, y, z) is the receiver position coordinate; (x s ,y s , z s ) are the satellite coordinates.

[0108] Using the precise ephemeris data released by the International Satellite Satellite System (IGS), precise three-dimensional satellite coordinates and satellite clock errors (Equations 5 and 6) can be obtained through processing and calculation. Errors such as Earth rotation, polar motion, and relativistic effects can be corrected using their respective error models. Ionospheric delay can be eliminated by eliminating first-order ionospheric composite observations, and the parameter estimation method uses a Kalman filter model. The error model described is conventional.

[0109] The process of inspection clocking in and generating inspection trajectory map is shown in Figure 3 As shown. Figure 3 As shown, the method for realizing mobile positioning in a state without network signal by using PPP positioning algorithm of the present invention includes data input, data preprocessing and parameter estimation, and finally generates PPP positioning result through data input, data preprocessing and parameter estimation; data input includes obtaining GNSS data from mobile phone chip by using API interface and downloading precise ephemeris product data from IGS; data preprocessing includes error processing, gross error elimination, phase epoch difference and cycle slip detection; parameter estimation includes constant velocity model, Kalman filtering, state prediction and measurement update.

[0110] At each inspection point, the inspection personnel first scan the QR code of the inspection object to obtain the basic information of the inspection object, which includes the inspection object's operating condition risk analysis model data and the inspection point's geographic coordinate information. When the inspection personnel scan the QR code, the mobile terminal's positioning function automatically locates the location of the scanning point (scanning location point), obtains the geographic coordinate information of the scanning location point, calculates the straight-line distance L1 between the scanning location point and the inspection object's location point, sets a distance threshold ΔL and compares L1 with ΔL. If L1 ≤ ΔL, the inspection clock-in is successful, and the inspection clock-in related information is recorded to generate an inspection track point, including the clock-in person, clock-in time, coordinate information, and inspection object information. If L1 > ΔL, the inspection clock-in fails and no inspection track point can be generated.

[0111] Step 6: Inspection information reporting and alarm

[0112] Inspection personnel need to fill in inspection information on site and upload inspection data, including: (1) description and record information of the inspection object's working condition; (2) photos and videos taken on site; (3) observation values ​​of inspection instruments. Since a working condition risk analysis model for the inspection object has been created and the model-related parameters are preset in the QR code, working condition risk analysis can be performed immediately when filling in the inspection data. The working condition risk of the inspection object is divided into three levels: normal, warning, and alarm. The current monitoring value is S_c, the theoretical value under normal working conditions is S_t, and the incremental threshold is given as Δq. The working condition risk level is determined according to Table 1 below.

[0113] Table 1. Judgment of risk level of working condition of inspection objects

[0114] Serial number Status judgment Judgment results 1 S_c≤S_t Normal state 2 S_t<S_c≤S_c+Δq Warning status 3 S_c>S_t+Δq Alert status

[0115] If one or more observation values ​​of the characteristic elements of the inspection object exceed the parameter thresholds preset by the model, the system will generate an alarm message and display it in a graph in the form of text, symbols, colors, etc.; if the observation value does not exceed the parameter thresholds preset by the model, no alarm message will be generated, thus overcoming the problem that the existing irrigation area inspection project uses manual inspections and it is difficult to realize the informatization and automation of on-site processing of inspection data.

[0116] Step 7: Risk prediction analysis and early warning of inspection object working conditions

[0117] The Auto-Regressive and Moving Average model (ARMA model) is selected to construct a risk prediction and analysis model for the inspection object working condition. The ARMA (p, q) model contains p autoregressive terms and q moving average terms. The ARMA (p, q) model can be expressed as:

[0118]

[0119] Where p and q are the autoregressive order and moving average order of the model, and θ are non-zero unknown coefficients, ε t are independent error terms, X t is a stationary, normal, zero-mean time series.

[0120] The model uses the historical monitoring data of the inspection object as input and calculates the predicted value of the characteristic factor for a given period Δt in the future. Assume that the current measured value is S_c, the predicted value calculated by the model is S_f, the theoretical threshold value of the characteristic factor under normal operating conditions is S_t, and the incremental threshold value of the characteristic factor observation value is set to Δq. Then, the operating risk of the inspection object is determined according to Table 2 below.

[0121] Table 2: Working condition risk judgment based on monitoring values ​​and predicted values ​​of inspection objects

[0122]

[0123] The above inspection object working condition analysis results are visualized in a diagram using text, symbols, colors, thematic maps, etc.; the inspection object working condition risk prediction model is used to predict the characteristic factor values, overcoming the defects of the existing technology that cannot perform inspection data prediction and analysis, lacks the development trend prediction analysis of the safety operation risk of the inspection object, and cannot timely discover potential risk hazards. The inspection object working condition risk analysis and prediction flow chart is shown in Figure 5 shown.

[0124] In the risk prediction analysis of the working conditions of the inspection objects of the present invention, a comprehensive analysis is performed by combining real-time monitoring data with historical monitoring data. At the same time, after the alarm information of step 6 is generated and the relevant requirements of the technical regulations are digitized and modeled, the observed values ​​are compared with the thresholds specified in the technical regulations. If the thresholds are exceeded, an alarm is issued. This is simple, direct and efficient, which reduces the workload of inspection personnel in consulting technical regulations, simple calculations and conversions.

[0125] Step 8: Synchronize the inspection data on the mobile terminal with the backend management system

[0126] The present invention develops a data synchronization service between the mobile terminal and the background management system. The inspection database table structure identical to that of the background service is created in the mobile terminal inspection App system. Inspection personnel fill in the inspection data record on site at the inspection point, and when the wireless network signal is good, directly transmit it to the background management system through the VPN network to achieve data synchronization; if there is no wireless network signal, it is first saved to the inspection App system locally, and when there is a network signal, it is automatically synchronized to the background management system; it overcomes the problem of the prior art that the inspection data cannot be synchronized when the smartphone has no signal in the wild, and the defect that the on-site inspection data cannot be transmitted to the background server in the offline state; the data synchronization flow chart of the inspection App system and the background management system is shown in Figure 4 shown.

[0127] Other parts not described belong to the prior art.

Claims

1. A patrol method for an irrigation district intelligent patrol system based on a smartphone, characterized by: The smart phone-based irrigation district intelligent inspection system includes a background management system and a mobile terminal inspection App system; The mobile terminal inspection App system communicates data with the backend management system via 4G / 5G wireless networks or VPN dedicated lines. Data communication includes the backend management system pushing inspection tasks and data information to the mobile terminal inspection App system, and the mobile terminal submitting inspection information to the backend management system. The backend management system implements the functions of formulating inspection tasks, planning inspection routes, forecasting and analyzing inspection data and early warning, receiving and managing data, information query and data synchronization; The mobile terminal inspection App system realizes the functions of mobile positioning, inspection trajectory map generation, on-site inspection information reporting, data upload, inspection alarm, information query and display; The inspection method comprises the following steps: Step 1: Build a comprehensive database for irrigation district inspections; Build a comprehensive irrigation district inspection database to support the formulation of inspection tasks, inspection data management, inspection risk analysis and early warning, and inspection information query; Step 2: Build an irrigation district inspection map; Build an irrigation district inspection map based on the GIS platform to visualize the inspection data of the irrigation district; Step 3: Build a risk analysis model library for the operating conditions of the inspection objects; Taking the inspection object as the unit, a risk analysis model library for the operating conditions of the inspection object is constructed. The basic information of the inspection object, the parameter value range of each characteristic element under the safe operation condition, the risk level of different working conditions and the judgment rules are set to provide services for the risk judgment, prediction analysis and alarm warning of the inspection object working conditions; Step 4: Planning inspection tasks and inspection routes; Inspection personnel send inspection task requests to the backend management system through the mobile terminal inspection app system based on the irrigation district they are in. The backend management system automatically extracts all inspection points in the irrigation district based on the irrigation district information, performs route planning, determines the inspection order of each inspection point, generates an inspection route, formulates an inspection task, and pushes the inspection task to the mobile terminal inspection app system. All inspection points and inspection routes included in this inspection task are visually displayed on an irrigation district inspection map. The PPP positioning algorithm is used to achieve mobile positioning in the absence of network signals. When the wireless network signal is good, the data is directly transmitted to the background management system through the VPN network to achieve data synchronization. If there is no wireless network signal, it is first saved to the inspection App system locally, and automatically synchronized to the background management system when there is a network signal. The goal of path planning is to minimize the total cost of inspection trips, that is, to take the shortest total trip path as the objective function and establish a mathematical model: (Formula 1) Where: represents the distances from the office location to each inspection point and the distances between each inspection point (i = 0, 1, …, n - 1; j = 1, 2, …, n; i < j), where i = 0 represents the starting position; represents that the inspection team k travels from inspection point i to inspection point j, taking the value of 1 when the event occurs and 0 otherwise; Step 5: Check in for inspection and generate an inspection trajectory map; At each inspection point, the inspection personnel first scan the QR code of the inspection object to obtain the basic information of the inspection object, which includes the risk analysis model data of the inspection object's operating conditions and the geographical coordinate information of the inspection point. When the inspection personnel scan the QR code, the positioning function of the mobile terminal App system automatically locates the location of the scanning point, obtains the geographical coordinate information of the scanning location point, calculates the straight-line distance L1 between the scanning location point and the inspection object location point, and sets the distance threshold. And compare L1 with If the size , then the inspection punch-in is successful, and the inspection punch-in related information is recorded to generate the inspection track point, including the punch-in person, punch-in time, coordinate information, and inspection object information. If , then the inspection clock-in fails and the inspection track point cannot be generated; Step 6: Fill in the inspection information on site; Inspection personnel fill in inspection information on-site through the mobile terminal inspection app system, including inspection records, observation values ​​of characteristic elements of the inspection objects, and photos and videos taken on-site; Step 7: Risk warning of working condition of the inspection object; According to the inspection object working condition model data and on-site inspection object characteristic element observation data, the mobile terminal inspection App system obtains the inspection object working condition risk analysis model library parameter information and performs analysis and comparison. If the characteristic element Observed values The working condition risk analysis model parameters corresponding to this factor The difference between Greater than or equal to a given threshold ,Right now: ,The smart inspection system for irrigation districts based on smartphones generates alarm information and ,visualizes it in a picture using colors and symbols; , then it is considered that there is no risk and no warning information is generated; Step 8: Risk prediction analysis and early warning of the inspection object's working conditions; The autoregressive moving average model is selected to construct the inspection object working condition risk prediction analysis model. The ARMA (p, q) model contains p autoregressive terms and q moving average terms. The ARMA (p, q) model is expressed as: (Equation 8) Where p and q are the autoregressive order and moving average order of the model, and is a non-zero undetermined coefficient, are independent error terms, is a stationary, normal, zero-mean time series; The historical monitoring data of the inspection object is used as the input data of the model, and the characteristic elements are calculated based on the historical monitoring data in a given period in the future. The predicted value of The predicted value calculated by the model is , the theoretical threshold value of this characteristic factor of the inspection object under normal working conditions is , set the incremental threshold of the characteristic element observation value to , then, the working condition risk of the inspection object is judged according to Table 2 below; Table 2: Working condition risk judgment based on monitoring values ​​and predicted values ​​of inspection objects The above-mentioned inspection object working condition analysis results are visualized in a single diagram using text, symbols, colors, and thematic maps. The inspection object working condition risk prediction model is used to predict the characteristic factor values ​​and obtain the predicted value of the risk development trend in a given period in the future. If the predicted value exceeds the set threshold, an early warning message is generated and visualized in a single diagram. If the predicted value does not exceed the set threshold, no warning information is generated, indicating that the current working condition of the inspection object is normal.

2. The inspection method of the smart phone-based irrigation district intelligent inspection system according to claim 1, characterized in that: In step 1, the inspection comprehensive database includes the following data: 1) Irrigation district basic data, including basic information data of the irrigation district, basic data of irrigation projects in the irrigation district, canal system data, and basic data of inspection points; 2) Inspection data; Irrigation area inspections include visual inspections and instrumental explorations; The data reported and collected by inspection personnel at each inspection point include instrument detection data, inspection records, and on-site pictures or video data; 3) Inspection task data; Inspection tasks are generated by the backend, and inspection task data includes inspection point list, inspection personnel information, and inspection route data; 4) Model data: Construct a safe operating condition model for each inspection object, including theoretical threshold data for each inspection object's operating condition, for use in alarm and early warning analysis of the inspection object's operating condition; 5) Spatial data; including spatial distribution data of water conservancy projects, road network structure data, canal system data, spatial distribution data of inspection objects, administrative divisions, and remote sensing image geospatial data.

3. The inspection method of the smart phone-based irrigation district intelligent inspection system according to claim 1 or 2, characterized in that: In step 2, the irrigation district inspection data includes the following layers: irrigation district scope and irrigation district subdivisions; irrigation district canal system; sluice gates; pumping stations; culverts; inverted siphons; aqueducts; dangerous sections of irrigation districts; water systems; lakes; road networks; towns; administrative divisions; high-definition remote sensing image data of irrigation districts; inspection routes; Inspection trajectory map; Inspection, alarm and warning information distribution map; spatial data is organized by layers to control the display status of each layer; unique codes are set for spatial feature objects, and associations are established with inspection data. The relevant data of each inspection object can be queried in one map. The relevant data of the inspection object includes the basic data of the inspection object, theoretical thresholds, data collected on-site by inspection personnel, and inspection, alarm and warning information.

4. The inspection method of the smart phone-based irrigation district intelligent inspection system according to claim 3 is characterized by: In step 4, the inspection route planning method includes the following steps: S41: Set the location of the inspection personnel’s work unit; S42: Set the number of inspection teams and the inspection irrigation areas; S43: Initialize data; S44: path search; S45: Path planning scheme; S46: Determine whether the path planning solution meets the constraint conditions; When the constraint conditions are met, jump to step S49; When the constraint condition is not met, jump to step S47: S47: Compare with the original route; S48: Determine whether the new route is better; When the new route is better, jump to step S49; When the new route is not better, jump to step S46; S49: Determine whether the path planning solution obtained in S45 is the optimal solution; If it is the optimal solution, jump to step S50; If it is not the optimal solution, jump to step S44; S50: Output plan; S51: Inspection route planning is completed.

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