Secondary water supply remote monitoring and early warning system based on big data

Abstract

The invention discloses a secondary water supply remote monitoring and early warning system based on big data, relates to the technical field of water supply monitoring, and discloses a diversion node monitoring module, an abnormal node marking module, a primary node marking module, a check node marking module and a water supply early warning determination module. The distribution node monitoring module and the abnormal node marking module are arranged, three-dimensional comprehensive analysis can be conducted on all the distribution nodes of the secondary water supply pipe network, and through multi-dimensional analysis of all water supply monitoring indexes, whether water supply abnormity exists in the distribution nodes or not is efficiently and accurately judged; according to the method, the flow distribution nodes with abnormal water supply are accurately marked, a primary node marking module, a check node marking module and a water supply early warning determining module are arranged, other flow distribution nodes in a secondary water supply pipe network can be rapidly screened and analyzed based on the flow distribution abnormal nodes, and potential abnormal flow distribution nodes are efficiently analyzed; and meanwhile, the water supply early warning level is accurately determined.

Description

Technical Field

[0001] The present invention relates to the field of water supply monitoring technology, and more specifically, to a secondary water supply remote monitoring and early warning system based on big data. Background Art

[0002] With the acceleration of urbanization, the secondary water supply system, as an important part of urban water supply, is directly related to the water safety and quality of life of residents. With the development of big data and artificial intelligence, the monitoring and management of secondary water supply has gradually become intelligent. The current secondary water supply monitoring and management system can periodically monitor the water supply data, and can issue early warnings for the corresponding data after detecting abnormalities. This data monitoring method has the problem of a single data analysis method and limited early warning capabilities. Therefore, how to ensure comprehensive monitoring and analysis of secondary water supply data while ensuring the early warning capabilities of secondary water supply is an urgent problem to be solved. Summary of the invention

[0003] In view of the deficiencies in the prior art, the object of the present invention is to provide a secondary water supply remote monitoring and early warning system based on big data.

[0004] To achieve the above object, the present invention provides the following technical solutions: A secondary water supply remote monitoring and early warning system based on big data, including a diversion node monitoring module, an abnormal node marking module, a primary node marking module, a check node marking module, and a water supply early warning determination module; The diversion node monitoring module obtains the water supply monitoring index of each diversion node in the secondary water supply network based on the duration of each hour; The abnormal node marking module determines whether to mark the diversion node as a diversion abnormal node based on the comparison result of the water supply monitoring index and the water supply monitoring limit index; When an abnormal shunt node appears, the primary node marking module marks a shunt node directly associated with the abnormal shunt node in the secondary water supply network as a primary shunt node; The reselected node marking module is used to determine whether to mark the primary selected branching node as a reselected branching node; The water supply warning determination module is used to determine the water supply warning level of the secondary water supply network.

[0005] Furthermore, the water supply monitoring index is obtained in the following manner: multiple time nodes with equal time intervals are set within a duration of h, and at each time node, the node water supply value of the time node is obtained, a rectangular coordinate system is constructed with the node water supply value as the Y-axis and the duration of h as the X-axis, the node water supply value corresponding to the time node is marked in the rectangular coordinate system as a point, the node water supply values ​​of two adjacent time nodes in the rectangular coordinate system are connected to obtain a water supply monitoring polyline, and the two ends of the water supply monitoring polyline are perpendicular to the X-axis to obtain two water supply start and end line segments, and the water supply monitoring polyline, the two formula start and end line segments and the X-axis form an irregular figure, and the total area of ​​the irregular figure is marked as the water supply monitoring index.

[0006] Furthermore, the node water supply value of the time node is obtained in the following way: obtain the indicator parameters of various water supply monitoring indicators in the diversion node, obtain the water supply monitoring values ​​of various water supply monitoring indicators according to the indicator parameters, sum the water supply monitoring values ​​of various water supply monitoring indicators to obtain the water supply monitoring superposition value gds, set the water supply monitoring threshold, when the water supply monitoring value of the water supply monitoring indicator is greater than or equal to the water supply monitoring threshold, no processing is performed, when the water supply monitoring value of the water supply monitoring indicator is less than the water supply monitoring threshold, the water supply monitoring indicator is marked as an abnormal water supply indicator, all abnormal water supply indicators are grouped together, the water supply monitoring values ​​of the two abnormal water supply indicators in the same group are summed to obtain the abnormal water supply group value, set the abnormal water supply threshold group value, when the abnormal water supply group value is greater than or equal to the abnormal water supply threshold group value, no processing is performed, when the abnormal water supply group value is less than the abnormal water supply threshold group value, the number of abnormal water supply groups is increased by one, and the number of abnormal water supply groups is marked as Esz, and the formula is used. The node water supply value kbg at the time node is obtained, where i1 is the water supply monitoring overlap coefficient and i2 is the coefficient of the number of abnormal water supply groups.

[0007] Furthermore, water supply monitoring values ​​of various water supply monitoring indicators are obtained according to the indicator parameters, specifically: water supply monitoring models of various water supply monitoring indicators are obtained, the indicator parameters of various water supply monitoring indicators are respectively input into the corresponding water supply monitoring models, and the water supply monitoring values ​​of various water supply monitoring indicators are output.

[0008] Furthermore, a water supply monitoring restriction index is set. When the water supply monitoring index is greater than or equal to the water supply monitoring restriction index, the diversion node is marked as an abnormal diversion node. When the water supply monitoring index is less than the water supply monitoring restriction index, no processing is performed.

[0009] Furthermore, determine whether to mark the preliminary diversion node as a re-selected diversion node, specifically: obtain the preliminary warning value of the diversion abnormal node and the preliminary warning value of the preliminary diversion node, calculate the difference between the preliminary warning value of the diversion abnormal node and the preliminary warning value of the preliminary diversion node and take the absolute value to obtain the pre-selected gap value, set the pre-selected gap boundary value, when the pre-selected gap value is greater than or equal to the pre-selected gap boundary value, do not process it, when the pre-selected gap value is less than the pre-selected gap boundary value, mark the preliminary diversion node as a re-selected diversion node.

[0010] Furthermore, the preliminary warning value is obtained in the following way: obtain n water supply monitoring indexes of the diversion node before the current time of the system, sort the n water supply monitoring indexes in the order of acquisition time, calculate the difference between the two adjacent water supply monitoring indexes after sorting and take the absolute value to obtain the continuous water supply floating value, set the continuous water supply floating threshold, when the continuous water supply floating value is greater than or equal to the continuous water supply floating threshold, increase the number of continuous water supply floating times by one, when the continuous water supply floating value is less than the continuous water supply floating threshold, do not process it, mark the number of continuous water supply floating times as SEK, sum the two adjacent water supply monitoring indexes after sorting to obtain the continuous water supply superposition value, set the continuous water supply superposition threshold, when the continuous water supply superposition value is greater than or equal to the continuous water supply superposition threshold, increase the number of continuous water supply superposition times by one, when the continuous water supply superposition value is less than the continuous water supply superposition threshold, do not process it, mark the number of continuous water supply superposition times as TYG, and use the formula The preliminary warning value GLP of the diversion node is obtained, where t1 is the continuous water supply floating times coefficient and t2 is the continuous water supply superposition times coefficient.

[0011] Furthermore, the warning check values ​​of the diversion abnormal node and the check diversion node are obtained, and the warning check boundary value is set. When the warning check value is greater than or equal to the warning check boundary value, no processing is performed. When the warning check value is less than the warning check boundary value, the check diversion node is marked as a synchronous warning node, and the water supply warning level of the secondary water supply network is determined.

[0012] Furthermore, the early warning check values ​​of the diversion abnormal node and the check-selected diversion node are obtained in the following manner: obtain m water supply monitoring indexes of the diversion abnormal node before the current system time, obtain m water supply monitoring indexes of the check-selected diversion node before the current system time, calculate the difference between the two water supply monitoring indexes of the diversion abnormal node and the check-selected diversion node with the same acquisition time, obtain the same-point difference index, sum up all the same-point difference indices and take the average, and obtain the early warning check values ​​of the diversion abnormal node and the check-selected diversion node.

[0013] Furthermore, the water supply warning level of the secondary water supply network is determined by marking the water supply monitoring index of the abnormal diversion node as slk, marking the total number of synchronous warning nodes as bcm, and using the formula Obtain the water supply warning value of the secondary water supply network, set the water supply warning threshold value, when the water supply warning value of the secondary water supply network is greater than or equal to the water supply warning threshold value, determine that the secondary water supply network is at the first-level water supply warning level; when the water supply warning value of the secondary water supply network is less than the water supply warning threshold value, determine that the secondary water supply network is at the second-level water supply warning level.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. Setting up the diversion node monitoring module and the abnormal node marking module can conduct a three-dimensional comprehensive analysis of all diversion nodes in the secondary water supply network. Through multi-dimensional analysis of all water supply monitoring indicators, it can efficiently and accurately determine whether there is abnormal water supply at the diversion node, and accurately mark the diversion nodes with abnormal water supply; 2. Setting up the preliminary node marking module, the check node marking module and the water supply warning determination module can quickly screen and analyze the remaining diversion nodes in the secondary water supply network based on the diversion abnormal nodes, efficiently analyze the potentially abnormal diversion nodes, and accurately determine the water supply warning level, so that regulators can take countermeasures according to the water supply warning level. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is an operation flow chart of a secondary water supply remote monitoring and early warning system based on big data; Figure 2 It is a module block diagram of a secondary water supply remote monitoring and early warning system based on big data; Figure 3 Flow chart for obtaining water supply monitoring index. DETAILED DESCRIPTION

[0016] Reference Figures 1 to 3 A secondary water supply remote monitoring and early warning system based on big data includes a diversion node monitoring module, an abnormal node marking module, a preliminary node marking module, a check node marking module, and a water supply early warning determination module.

[0017] Diversion node monitoring module: every hour, obtain the water supply monitoring index of each diversion node in the secondary water supply network (the secondary water supply network contains multiple diversion nodes, which are the branch points and intersections of the secondary water supply network and can distribute the secondary water supply to different pipes).

[0018] Abnormal node marking module: Set the water supply monitoring limit index (system threshold, modified according to actual needs). When the water supply monitoring index is greater than or equal to the water supply monitoring limit index, the diversion node is marked as a diversion abnormal node. When the water supply monitoring index is less than the water supply monitoring limit index, no processing is performed.

[0019] The water supply monitoring index is obtained in the following way: multiple time nodes with equal time intervals are set within a duration of h (the length of the time interval determines the number of time nodes, and the number of time nodes can be adjusted according to actual needs), and at each time node, the node water supply value of the time node is obtained, and a rectangular coordinate system is constructed with the node water supply value as the Y-axis and the duration of h as the X-axis, and the node water supply value corresponding to the time node is marked in the rectangular coordinate system as a point, and the node water supply values ​​of two adjacent time nodes in the rectangular coordinate system are connected (the node water supply values ​​of the two time nodes are connected together in the form of a line segment), and then a water supply monitoring polyline is obtained, and perpendicular lines are drawn from both ends of the water supply monitoring polyline to the X-axis to obtain two water supply start and end line segments, and an irregular figure is formed by the water supply monitoring polyline, the two formula start and end line segments and the X-axis, and the total area of ​​the irregular figure is marked as the water supply monitoring index.

[0020] The node water supply value of the time node is obtained in the following way: obtain the indicator parameters of various water supply monitoring indicators in the diversion node (the diversion node is a key position in the secondary water supply network, and its monitoring is very important for ensuring the stable operation of the secondary water supply system. The types of water supply monitoring indicators include but are not limited to water supply flow indicators, water supply pressure indicators, and water supply quality indicators), obtain the water supply monitoring values ​​of various water supply monitoring indicators according to the indicator parameters, sum the water supply monitoring values ​​of various water supply monitoring indicators to obtain the water supply monitoring superposition value gds, and set the water supply monitoring threshold (all water supply monitoring indicators use a unified water supply monitoring threshold, and the water supply monitoring threshold can be modified according to actual needs ), when the water supply monitoring value of the water supply monitoring indicator is greater than or equal to the water supply monitoring threshold, no processing is performed; when the water supply monitoring value of the water supply monitoring indicator is less than the water supply monitoring threshold, the water supply monitoring indicator is marked as an abnormal water supply indicator, all abnormal water supply indicators are grouped together, the water supply monitoring values ​​of the two abnormal water supply indicators in the same group are summed up to obtain the abnormal water supply group value, and the abnormal water supply threshold group value is set (system threshold, modified according to actual needs). When the abnormal water supply group value is greater than or equal to the abnormal water supply threshold group value, no processing is performed; when the abnormal water supply group value is less than the abnormal water supply threshold group value, the number of abnormal water supply groups is increased by one, and the number of abnormal water supply groups is marked as Esz. Using the formula The node water supply value kbg at the time node is obtained, where i1 is the water supply monitoring overlap coefficient, i2 is the coefficient of the number of abnormal water supply groups, the value of i1 is 1.38, and the value of i2 is 1.81.

[0021] The water supply monitoring values ​​of various water supply monitoring indicators are obtained according to the indicator parameters, specifically: the water supply monitoring models of various water supply monitoring indicators are obtained, the indicator parameters of various water supply monitoring indicators are input into the corresponding water supply monitoring models respectively, and the water supply monitoring values ​​of various water supply monitoring indicators are obtained as output.

[0022] In the secondary water supply network, different water supply monitoring indicators correspond to different water supply monitoring models. In order to facilitate the analysis of different water supply monitoring indicators, different water supply monitoring models use the same training method. The only difference is that the training data in the training process is replaced with the indicator parameters of the corresponding water supply monitoring indicators. The following will introduce the construction method of the water supply monitoring model of water supply quality indicators: collect the indicator parameters of g groups of water supply monitoring indicators, and the indicator parameters of each group of water supply monitoring indicators are collected in the form of a data set. The data set of the indicator parameters of the water supply monitoring indicators is {turbidity, pH value, dissolved oxygen, conductivity, heavy metal content, organic matter content}. The indicator parameters contained in the data set include but are not limited to the above. The indicator parameters of the g groups of water supply monitoring indicators are used as the training data of the deep learning model, and the training data are assigned water supply monitoring values. The training data is divided into a training set and a validation set according to a set ratio of 1:2. The deep learning model is trained through the training set and the validation set to obtain the water supply monitoring model of water supply quality indicators. The value range of the water supply monitoring value is (1~3). The closer the water supply monitoring value is to 3, the more the indicator parameter conforms to the water supply quality indicator. The closer the water supply monitoring value is to 1, the less the indicator parameter conforms to the water supply quality indicator. The value range of the water supply monitoring value assigned by different water supply monitoring models is consistent.

[0023] By setting up a diversion node monitoring module and an abnormal node marking module, a three-dimensional comprehensive analysis of all diversion nodes in the secondary water supply network can be carried out. Through multi-dimensional analysis of all water supply monitoring indicators, it is possible to efficiently and accurately determine whether there are water supply abnormalities at the diversion nodes, and accurately mark the diversion nodes with water supply abnormalities.

[0024] Preliminary node marking module: When an abnormal diversion node appears, the diversion node directly associated with the abnormal diversion node in the secondary water supply network is marked as a preliminary diversion node (direct association means that there is a physical connection between the diversion nodes. For example, when there is a direct pipeline connection between two diversion nodes, there is a direct association between the two diversion nodes).

[0025] Check node marking module: obtain the preliminary warning value of the abnormal diversion node and the preliminary warning value of the preliminary diversion node, calculate the difference between the preliminary warning value of the abnormal diversion node and the preliminary warning value of the preliminary diversion node and take the absolute value to obtain the pre-selected gap value, set the pre-selected gap threshold (system threshold, modified according to actual needs), when the pre-selected gap value is greater than or equal to the pre-selected gap threshold, no processing is performed, when the pre-selected gap value is less than the pre-selected gap threshold, the preliminary diversion node is marked as a check diversion node.

[0026] The preliminary warning value is obtained in the following way: obtain n water supply monitoring indexes of the diversion node before the current time of the system, sort the n water supply monitoring indexes in the order of acquisition time, calculate the difference between the two adjacent water supply monitoring indexes after sorting and take the absolute value to obtain the continuous water supply floating value, set the continuous water supply floating threshold (system threshold, modified according to actual needs), when the continuous water supply floating value is greater than or equal to the continuous water supply floating threshold, increase the number of continuous water supply floating times by one, when the continuous water supply floating value is less than the continuous water supply floating threshold, no processing is performed, and the number of continuous water supply floating times is marked as SEK, sum the two adjacent water supply monitoring indexes after sorting to obtain the continuous water supply superposition value, set the continuous water supply superposition threshold (system threshold, modified according to actual needs), when the continuous water supply superposition value is greater than or equal to the continuous water supply superposition threshold, increase the number of continuous water supply superposition times by one, when the continuous water supply superposition value is less than the continuous water supply superposition threshold, no processing is performed, and the number of continuous water supply superposition times is marked as TYG, using the formula The preliminary warning value GLP of the diversion node is obtained, where t1 is the continuous water supply floating times coefficient, t2 is the continuous water supply superposition times coefficient, the value of t1 is 0.98, and the value of t2 is 0.81.

[0027] Water supply warning determination module: obtain the warning check value of the diversion abnormal node and the check diversion node, set the warning check boundary value (system threshold, modified according to actual needs), when the warning check value is greater than or equal to the warning check boundary value, no processing is performed, when the warning check value is less than the warning check boundary value, the check diversion node is marked as a synchronous warning node, and then the water supply warning level of the secondary water supply network is determined.

[0028] The early warning check value of the diversion abnormal node and the check diversion node is obtained in the following way: obtain m water supply monitoring indexes of the diversion abnormal node before the current system time, obtain m water supply monitoring indexes of the check diversion node before the current system time, calculate the difference between the two water supply monitoring indexes of the diversion abnormal node and the check diversion node with the same acquisition time, obtain the same-point difference index, sum up all the same-point difference indices and take the average, and obtain the early warning check value of the diversion abnormal node and the check diversion node.

[0029] Determine the water supply warning level of the secondary water supply network, specifically: mark the water supply monitoring index of the diversion abnormal node as slk, mark the total number of synchronous warning nodes as bcm, and use the formula The water supply warning value of the secondary water supply network is obtained, where u1 is the water supply monitoring index coefficient, u2 is the total number coefficient of synchronous warning nodes, the value of u1 is 0.54, and the value of u2 is 0.47. The water supply warning threshold (system threshold, modified according to actual needs) is set. When the water supply warning value of the secondary water supply network is greater than or equal to the water supply warning threshold, the secondary water supply network is determined to be a first-level water supply warning level. When the water supply warning value of the secondary water supply network is less than the water supply warning threshold, the secondary water supply network is determined to be a second-level water supply warning level. The warning level of the first-level water supply warning level is higher than that of the second-level water supply warning level, indicating that the water supply problem and problem scope of the first-level water supply warning level are greater than those of the second-level water supply warning level. Supervisors can take different response measures according to different levels.

[0030] By setting up a preliminary node marking module, a check node marking module and a water supply warning determination module, the remaining diversion nodes in the secondary water supply network can be quickly screened and analyzed based on the diversion abnormal nodes, and the potentially abnormal diversion nodes can be efficiently analyzed. At the same time, the water supply warning level can be accurately determined, which is convenient for regulators to take countermeasures according to the water supply warning level.

[0031] The above formulas are all dimensionless and numerical calculations are performed, and the preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0032] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0033] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0034] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0035] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0036] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0037] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0038] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A remote monitoring and early warning system for secondary water supply based on big data, characterized in that: It includes a diversion node monitoring module, an abnormal node marking module, a preliminary node marking module, a check node marking module, and a water supply early warning determination module; The diversion node monitoring module obtains the water supply monitoring index of each diversion node in the secondary water supply network based on the duration of each hour; The abnormal node marking module determines whether to mark the diversion node as a diversion abnormal node based on the comparison result of the water supply monitoring index and the water supply monitoring limit index; When an abnormal shunt node appears, the primary node marking module marks a shunt node directly associated with the abnormal shunt node in the secondary water supply network as a primary shunt node; The reselected node marking module is used to determine whether to mark the primary selected branching node as a reselected branching node; The water supply warning determination module is used to determine the water supply warning level of the secondary water supply network.

2. According to the big data-based secondary water supply remote monitoring and early warning system of claim 1, it is characterized in that: The water supply monitoring index is obtained in the following way: multiple time nodes with equal time intervals are set within a duration of h, and at each time node, the node water supply value of the time node is obtained, a rectangular coordinate system is constructed with the node water supply value as the Y-axis and the duration of h as the X-axis, the node water supply value corresponding to the time node is marked in the rectangular coordinate system as a point, the node water supply values ​​of two adjacent time nodes in the rectangular coordinate system are connected to obtain a water supply monitoring polyline, and the two ends of the water supply monitoring polyline are perpendicular to the X-axis to obtain two water supply start and end line segments, and the water supply monitoring polyline, the two formula start and end line segments and the X-axis form an irregular figure, and the total area of ​​the irregular figure is marked as the water supply monitoring index.

3. According to the big data-based secondary water supply remote monitoring and early warning system of claim 2, it is characterized in that: The node water supply value of the time node is obtained in the following way: obtain the indicator parameters of various water supply monitoring indicators in the diversion node, obtain the water supply monitoring values ​​of various water supply monitoring indicators according to the indicator parameters, sum the water supply monitoring values ​​of various water supply monitoring indicators to obtain the water supply monitoring superposition value gds, set the water supply monitoring threshold, when the water supply monitoring value of the water supply monitoring indicator is greater than or equal to the water supply monitoring threshold, no processing is performed, when the water supply monitoring value of the water supply monitoring indicator is less than the water supply monitoring threshold, the water supply monitoring indicator is marked as an abnormal water supply indicator, all abnormal water supply indicators are grouped together, the water supply monitoring values ​​of the two abnormal water supply indicators in the same group are summed to obtain the abnormal water supply group value, set the abnormal water supply threshold group value, when the abnormal water supply group value is greater than or equal to the abnormal water supply threshold group value, no processing is performed, when the abnormal water supply group value is less than the abnormal water supply threshold group value, the number of abnormal water supply groups is increased by one, and the number of abnormal water supply groups is marked as Esz, and the formula is used. The node water supply value kbg at the time node is obtained, where i1 is the water supply monitoring overlap coefficient and i2 is the coefficient of the number of abnormal water supply groups.

4. According to the big data-based secondary water supply remote monitoring and early warning system of claim 3, it is characterized in that: The water supply monitoring values ​​of various water supply monitoring indicators are obtained according to the indicator parameters, specifically: the water supply monitoring models of various water supply monitoring indicators are obtained, the indicator parameters of various water supply monitoring indicators are input into the corresponding water supply monitoring models respectively, and the water supply monitoring values ​​of various water supply monitoring indicators are obtained as output.

5. According to the big data-based secondary water supply remote monitoring and early warning system of claim 1, it is characterized in that: Set the water supply monitoring limit index. When the water supply monitoring index is greater than or equal to the water supply monitoring limit index, the diversion node is marked as an abnormal diversion node. When the water supply monitoring index is less than the water supply monitoring limit index, no processing is performed.

6. According to the big data-based secondary water supply remote monitoring and early warning system of claim 1, it is characterized in that: Determine whether to mark the preliminary diversion node as a re-selected diversion node, specifically: obtain the preliminary warning value of the diversion abnormal node and the preliminary warning value of the preliminary diversion node, calculate the difference between the preliminary warning value of the diversion abnormal node and the preliminary warning value of the preliminary diversion node and take the absolute value to obtain the pre-selected gap value, set the pre-selected gap boundary value, when the pre-selected gap value is greater than or equal to the pre-selected gap boundary value, do not process it, when the pre-selected gap value is less than the pre-selected gap boundary value, mark the preliminary diversion node as a re-selected diversion node.

7. A secondary water supply remote monitoring and early warning system based on big data according to claim 6, characterized in that: The preliminary warning value is obtained in the following way: obtain n water supply monitoring indexes of the diversion node before the current time of the system, sort the n water supply monitoring indexes in the order of acquisition time, calculate the difference between the two adjacent water supply monitoring indexes after sorting and take the absolute value to obtain the continuous water supply floating value, set the continuous water supply floating threshold, when the continuous water supply floating value is greater than or equal to the continuous water supply floating threshold, increase the number of continuous water supply floating times by one, when the continuous water supply floating value is less than the continuous water supply floating threshold, do not process it, mark the number of continuous water supply floating times as SEK, sum the two adjacent water supply monitoring indexes after sorting to obtain the continuous water supply superposition value, set the continuous water supply superposition threshold, when the continuous water supply superposition value is greater than or equal to the continuous water supply superposition threshold, increase the number of continuous water supply superposition times by one, when the continuous water supply superposition value is less than the continuous water supply superposition threshold, do not process it, mark the number of continuous water supply superposition times as TYG, and use the formula The preliminary warning value GLP of the diversion node is obtained, where t1 is the continuous water supply floating times coefficient and t2 is the continuous water supply superposition times coefficient.

8. The secondary water supply remote monitoring and early warning system based on big data according to claim 1 is characterized in that: Get the warning check value of the diversion abnormal node and the check diversion node, set the warning check boundary value, when the warning check value is greater than or equal to the warning check boundary value, no processing is done, when the warning check value is less than the warning check boundary value, mark the check diversion node as a synchronous warning node, and then determine the water supply warning level of the secondary water supply network.

9. The secondary water supply remote monitoring and early warning system based on big data according to claim 8 is characterized in that: The early warning check value of the diversion abnormal node and the check diversion node is obtained in the following way: obtain m water supply monitoring indexes of the diversion abnormal node before the current system time, obtain m water supply monitoring indexes of the check diversion node before the current system time, calculate the difference between the two water supply monitoring indexes of the diversion abnormal node and the check diversion node with the same acquisition time, obtain the same-point difference index, sum up all the same-point difference indices and take the average, and obtain the early warning check value of the diversion abnormal node and the check diversion node.

10. A secondary water supply remote monitoring and early warning system based on big data according to claim 9, characterized in that: Determine the water supply warning level of the secondary water supply network, specifically: mark the water supply monitoring index of the diversion abnormal node as slk, mark the total number of synchronous warning nodes as bcm, and use the formula The water supply warning value Jcw of the secondary water supply network is obtained, where u1 is the water supply monitoring index coefficient, u2 is the total number coefficient of synchronous warning nodes, and the water supply warning threshold is set. When the water supply warning value of the secondary water supply network is greater than or equal to the water supply warning threshold, the secondary water supply network is determined to be at the first-level water supply warning level; when the water supply warning value of the secondary water supply network is less than the water supply warning threshold, the secondary water supply network is determined to be at the second-level water supply warning level.

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