Configuration-free edge water meter water quantity identification and data quality control method

Abstract

The invention relates to the technical field of water meter data acquisition, in particular to a configuration-free edge water meter water quantity identification and data quality control method, which comprises the following steps of: acquiring flow information records of N edge water meters in a to-be-controlled water meter community and dynamic change characteristics of a water use state in the to-be-controlled water meter community; screening and defining the flow information record of the jth edge water meter as target user water meter information, calculating a data quality abnormal coefficient of the jth edge water meter according to the distinguishing record, inputting the data quality abnormal coefficient into a network model which is optimized in advance and is used for data quality confidence identification, and circularly outputting different data quality confidence of all the edge water meters; and analyzing the distribution states of the edge water meters with different data quality confidence coefficients in the to-be-controlled water meter community, generating corresponding monitoring schemes for the edge water meters with different data quality confidence coefficients, and sequentially transmitting the monitoring schemes to the corresponding edge water meters. According to the method, the edge water meter data quality in the residential users can be effectively identified, a corresponding monitoring scheme is adopted, and the community water consumption condition is efficiently managed.

Description

Technical Field

[0001] The present application relates to the technical field of water meter data collection, and in particular to a configuration-free edge water meter water volume identification and data quality control method. Background Art

[0002] With the gradual popularization of electronic edge water meters, although the trouble of manual meter reading is avoided, residents pay less and less attention to household water use, and many residents know nothing about their household water use. Among them, many residents cannot read the water meter readings or are unwilling to calculate the water consumption for the month.

[0003] Therefore, in order to facilitate residents to check water usage and carry out targeted water-saving operations, it is urgent to develop a configuration-free edge water meter water volume identification and data quality control solution. At the same time, the water meter water volume information is detailed to each type of household water terminal, which is convenient for relevant staff to read the meter and clearly query the water usage of each type of household water terminal, so that more targeted water-saving measures can be taken. Summary of the invention

[0004] To achieve the above objectives, this application provides the following technical solutions:

[0005] According to a first aspect of the present invention, the present invention claims a configuration-free edge water meter water volume identification and data quality control method, the method comprising:

[0006] S1: Collect flow information records of N edge water meters in the water meter community to be controlled, and based on the flow information records of the N edge water meters, collect dynamic change characteristics of water use status in the water meter community to be controlled;

[0007] S2: Filter and obtain the flow information record of the jth edge water meter, and define the flow information record of the jth edge water meter as the target user water meter information, distinguish and compare the dynamic change characteristics of the water use status and the target user water meter information to collect the difference records of the jth edge water meter, and calculate the data quality anomaly coefficient of the jth edge water meter according to the difference records;

[0008] S3: Input the data quality anomaly coefficient of the j-th edge water meter into the network model optimized in advance for data quality confidence identification to identify the data quality confidence of the j-th edge water meter, and set j=j+1, and return to step S2;

[0009] S4: repeat the above steps S2 to S3 until j=N, and output different data quality confidences of all edge water meters;

[0010] S5: Analyze the distribution status of edge water meters with different data quality confidence levels in the community of water meters to be controlled, generate corresponding monitoring plans for edge water meters with different data quality confidence levels according to the distribution status, and transmit the monitoring plans to the corresponding edge water meters in sequence.

[0011] Furthermore, the flow information record includes five fields, which are the average monthly water flow, instantaneous water flow rate, peak and valley water use cycle, maximum value number and minimum value number;

[0012] The dynamic change characteristics of water usage status in the water meter community to be controlled are collected, including:

[0013] a1: Filter and obtain the jth field in the traffic information record, and analyze all field values ​​of the jth field;

[0014] a2: Divide all field values ​​of the j-th field by interval segmentation to collect P divided intervals;

[0015] a3: Prioritize the P partitions in the order of information size, define the partition with the highest priority as a high priority cluster, calculate the mean of all data in the high priority cluster, and output the high priority value of the jth field;

[0016] a4: Repeat the above steps a1 to a3 until j=5, then end the recursion and output the high priority value of the field; define the high priority value of the field as a dynamic change feature of the water usage status.

[0017] Furthermore, the step of distinguishing and comparing the dynamic change characteristics of the water usage status and the water meter information of the target user includes:

[0018] b1: Filter and obtain the field value of the wth field in the target user's water meter information, and filter and obtain the high priority quantity of the same field in the dynamic change characteristics of the water use status, define the field value of the wth field as the field value to be identified, and define the high priority quantity of the same field as the reference field value;

[0019] b2: Calculate the distance between the field value to be identified and the reference field value, and determine whether the distance between the field value to be identified and the reference field value is within a preset range. If so, set w=w+1 and return to step b1; if not, define the distance between the field value to be identified and the reference field value as the deviation ratio of the wth field, define the wth field as a distinguishing field, set w=w+1, and return to step b1;

[0020] b3: Repeat the above steps b1 to b2 until w=5, end the recursion, output K distinguishing fields and the deviation ratio of each distinguishing field, and define the K distinguishing fields and the deviation ratio of each distinguishing field as the distinguishing record of the jth edge water meter.

[0021] Further, the calculation of the data quality anomaly coefficient of the j-th edge water meter according to the distinguished records includes:

[0022] Screen and obtain the deviation ratio of each difference field in the difference record of the jth edge water meter;

[0023] The deviation ratio of each distinguishing field is input into the preconfigured quality anomaly analysis model, and the data quality anomaly coefficient of the jth edge water meter is output; wherein the formula of the quality anomaly analysis model is as follows: Where: R is the data quality anomaly coefficient of the jth edge water meter, D g is the deviation ratio of the g-th distinguishing field.

[0024] Furthermore, the optimization steps of the network model for data quality confidence identification are as follows:

[0025] Collect historical data quality confidence sample information, and divide the historical data quality confidence sample information into a data quality confidence sample set and a data quality confidence data set to be identified; the historical data quality confidence sample information includes data quality anomaly coefficients of multiple edge water meters and corresponding data quality confidence setting confidence scenarios;

[0026] The data quality confidence setting confidence scenarios include abnormally low confidence scenarios, abnormally medium confidence scenarios, and abnormally high confidence scenarios;

[0027] Configure the quality listener, define the data quality anomaly coefficient in the data quality confidence sample set as the input data of the quality listener, and define the setting confidence scenario in the data quality confidence sample set as the output data of the quality listener, optimize the quality listener, and output the initial monitoring network;

[0028] The initial monitoring network is model verified using the data quality confidence data set to be identified, and the corresponding initial monitoring network outputted with a value greater than or equal to a preset accuracy threshold is defined as a network model for data quality confidence identification.

[0029] Furthermore, the generation logic of setting the confidence scenario in the historical data quality confidence sample information is as follows:

[0030] Setting a data quality anomaly coefficient threshold value, wherein the data quality anomaly coefficient threshold value includes a data quality anomaly coefficient threshold value Lim1 and a data quality anomaly coefficient threshold value Lim2, Lim1>Lim2;

[0031] Compare the data quality anomaly coefficient R with the data quality anomaly coefficient threshold value;

[0032] If R ≥ Lim1, the edge water meter corresponding to the data quality anomaly coefficient is set to the data quality confidence of the abnormally low confidence scenario;

[0033] If R<Lim1 and R>Lim2, the edge water meter corresponding to the data quality anomaly coefficient is set to the data quality confidence of the abnormal medium confidence scenario;

[0034] If R≤Lim1, the edge water meter corresponding to the data quality anomaly coefficient is set to the data quality confidence of the abnormally high confidence scenario.

[0035] Furthermore, the generation of corresponding monitoring schemes for edge water meters with different data quality confidence levels includes:

[0036] Collect W buildings in the water meter community to be controlled and the building ID of each building;

[0037] The number of edge water meters in the abnormally low confidence scene, the number of edge water meters in the abnormally medium confidence scene, and the number of edge water meters in the abnormally high confidence scene in each building are collected in sequence;

[0038] Define the building with the highest priority of edge water meters in the abnormally low confidence scenario as a high-consumption building, filter and obtain the building IDs of the high-consumption buildings, and define the building IDs of the high-consumption buildings as the monitoring solution for edge water meters in the abnormally low confidence scenario;

[0039] Define the building with the highest priority of the number of edge water meters in the abnormal medium confidence scenario as a medium-consumption building, filter and obtain the building ID of the medium-consumption building, and define the building ID of the medium-consumption building as the monitoring plan for the edge water meter in the abnormal medium confidence scenario;

[0040] The building with the highest priority in the number of edge water meters in the abnormally high confidence scene is defined as a low-consumption building, and the building IDs of the low-consumption buildings are obtained by screening, and the building IDs of the low-consumption buildings are defined as the monitoring plan for the edge water meters in the abnormally high confidence scene.

[0041] The present application relates to the technical field of water meter data collection, and in particular to a configuration-free edge water meter water volume identification and data quality control method, which collects the flow information records of N edge water meters in the community of water meters to be controlled and the dynamic change characteristics of the water use status in the community of water meters to be controlled; screens and defines the flow information record of the jth edge water meter as the target user water meter information, and calculates the data quality anomaly coefficient of the jth edge water meter based on the difference records and inputs it into the network model optimized in advance for data quality confidence identification, and cyclically outputs the different data quality confidences of all edge water meters; analyzes the distribution status of edge water meters with different data quality confidences in the community of water meters to be controlled, generates corresponding monitoring plans for edge water meters with different data quality confidences, and transmits them to the corresponding edge water meters in sequence. The present application can effectively identify the data quality of edge water meters in residential users, adopt corresponding monitoring plans, and efficiently manage community water use. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a workflow diagram of a configuration-free edge water meter water volume identification and data quality control method claimed in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] The terms "first", "second" and "third" in this application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of the present application (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0045] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0046] See also Figure 1 As shown, this embodiment discloses a configuration-free edge water meter water volume identification and data quality control method, the method comprising:

[0047] S1: Collect flow information records of N edge water meters in the water meter community to be controlled, and based on the flow information records of the N edge water meters, collect dynamic change characteristics of water use status in the water meter community to be controlled;

[0048] It should be noted that: the water meter community to be controlled is selected according to the actual situation, by selecting a high-flow water use scenario in the urban pipeline network where water pipeline accidents or water shortages often occur during peak hours;

[0049] Specifically, the flow information record contains five fields, which are the average monthly water flow, instantaneous water flow rate, peak and valley water use cycle, maximum value number and minimum value number;

[0050] It should be noted that the monthly average water flow, instantaneous water flow rate, peak and valley water use cycle, maximum and minimum times in the flow information record are collected by sensors or collected and output by monitors set in the water meter community to be controlled;

[0051] In implementation, the dynamic change characteristics of water usage status in the community of water meters to be controlled are collected, including:

[0052] a1: Filter and obtain the jth field in the traffic information record, and analyze all field values ​​of the jth field;

[0053] a2: Divide all field values ​​of the j-th field by interval segmentation to collect P divided intervals;

[0054] a3: Prioritize the P partitions in the order of information size, define the partition with the highest priority as a high priority cluster, calculate the mean of all data in the high priority cluster, and output the high priority value of the jth field;

[0055] a4: repeating the above steps a1 to a3 until j=5, ending the recursion and outputting the high priority value of the field; defining the high priority value of the field as a dynamic change feature of the water use status;

[0056] For example, suppose there are 5 edge water meters in the water meter community to be controlled, that is, N=5, which are X1, X2, X3, X4 and X5, respectively. The average monthly water flow of X1 (i.e., the field value of the cubic meter feature) is 9.0, the average monthly water flow of X2 is 8.8, the average monthly water flow of X3 is 6.5, the average monthly water flow of X4 is 11.5, and the average monthly water flow of X5 is 3.0. Assume that after the field value of the cubic meter field is divided, three division intervals are output, which are Y1, Y2 and Y3, respectively. Among them, Y1 contains X1, X2 and X 3 cubic meter field, while Y2 contains the field value of X4 cubic meter field, and Y3 contains the field value of X5 cubic meter field. Among them, Y1 is the partition interval with the first priority configuration, so Y1 is defined as a high priority cluster, and the mean value of all data in the high priority cluster Y1 is calculated, and 8.1 is output, then 8.1 is defined as the high priority quantity of the cubic meter field; similarly, the collection logic of the high priority quantity of other fields is the same, and the details are described above; finally, the high priority quantity of all fields is output, that is, the dynamic change characteristics of water use status;

[0057] It should be understood that the division interval with the first priority configuration often contains the most common patterns or behaviors in the data, that is, it represents most of the characteristic behaviors of water use status in the water meter community to be controlled, and reflects the relatively stable state of water use status (especially in high-flow water use scenarios). By collecting the characteristic quantities of the vast majority of data characteristic behaviors of water use status and defining them as reference benchmarks, the present invention is conducive to providing important data support for the subsequent determination of data quality confidence of different edge water meters in the current time and space.

[0058] S2: Filter and obtain the flow information record of the jth edge water meter, and define the flow information record of the jth edge water meter as the target user water meter information, distinguish and compare the dynamic change characteristics of the water use status and the target user water meter information to collect the difference records of the jth edge water meter, and calculate the data quality anomaly coefficient of the jth edge water meter according to the difference records;

[0059] In implementation, the dynamic change characteristics of water usage status and target user water meter information are distinguished and compared, including:

[0060] b1: Filter and obtain the field value of the wth field in the target user's water meter information, and filter and obtain the high priority quantity of the same field in the dynamic change characteristics of the water use status, define the field value of the wth field as the field value to be identified, and define the high priority quantity of the same field as the reference field value;

[0061] For example, if the wth field is a cubic meter field (i.e., the average monthly water flow), and the field value of the cubic meter field is 8.8, therefore, when the same field is obtained by screening, the high priority value of the consumption field in the dynamic change characteristics of the water use status is obtained by screening. If the high priority value of the cubic meter field is 8.1, 8.8 is the field value to be identified, and 8.1 is the reference field value;

[0062] b2: Calculate the distance between the field value to be identified and the reference field value, and determine whether the distance between the field value to be identified and the reference field value is within a preset range. If so, set w=w+1 and return to step b1; if not, define the distance between the field value to be identified and the reference field value as the deviation ratio of the wth field, define the wth field as a distinguishing field, set w=w+1, and return to step b1;

[0063] b3: Repeat the above steps b1 to b2 until w=5, then end the recursion, output K distinguishing fields and the deviation ratio of each distinguishing field, and define the K distinguishing fields and the deviation ratio of each distinguishing field as the distinguishing record of the jth edge water meter;

[0064] In implementation, the step of calculating the data quality anomaly coefficient of the j-th edge water meter according to the distinguished records includes:

[0065] Screen and obtain the deviation ratio of each difference field in the difference record of the jth edge water meter;

[0066] The deviation ratio of each distinguishing field is input into the preconfigured quality anomaly analysis model, and the data quality anomaly coefficient of the jth edge water meter is output; wherein the formula of the quality anomaly analysis model is as follows: Where: R is the data quality anomaly coefficient of the jth edge water meter, D g is the deviation ratio of the g-th distinguishing field.

[0067] S3: Input the data quality anomaly coefficient of the j-th edge water meter into the network model optimized in advance for data quality confidence identification to identify the data quality confidence of the j-th edge water meter, and set j=j+1, and return to step S2;

[0068] Specifically, the optimization steps of the network model for data quality confidence identification are as follows:

[0069] Collect historical data quality confidence sample information, and divide the historical data quality confidence sample information into a data quality confidence sample set and a data quality confidence data set to be identified; the historical data quality confidence sample information includes data quality anomaly coefficients of multiple edge water meters and corresponding data quality confidence setting confidence scenarios;

[0070] The data quality confidence setting confidence scenarios include abnormally low confidence scenarios, abnormally medium confidence scenarios, and abnormally high confidence scenarios;

[0071] It should be noted that the data quality anomaly coefficient of the edge water meter in the historical data quality confidence sample information is output after distinguishing and comparing the flow information records of each edge water meter and analyzing the quality anomaly. For details, please refer to the above description and will not be repeated here.

[0072] The generation logic of setting the confidence scenario in the historical data quality confidence sample information is as follows:

[0073] Setting a data quality anomaly coefficient threshold value, wherein the data quality anomaly coefficient threshold value includes a data quality anomaly coefficient threshold value Lim1 and a data quality anomaly coefficient threshold value Lim2, Lim1>Lim2;

[0074] Compare the data quality anomaly coefficient R with the data quality anomaly coefficient threshold value;

[0075] If R ≥ Lim1, the edge water meter corresponding to the data quality anomaly coefficient is set to the data quality confidence of the abnormally low confidence scenario;

[0076] If R<Lim1 and R>Lim2, the edge water meter corresponding to the data quality anomaly coefficient is set to the data quality confidence of the abnormal medium confidence scenario;

[0077] If R≤Lim1, the edge water meter corresponding to the data quality anomaly coefficient is set to the data quality confidence of the abnormally high confidence scenario.

[0078] It can be understood that: the data quality confidence in the abnormally low confidence scenario indicates that the current data quality behavior of the household owner of the corresponding edge water meter is too aggressive, tending to be higher than the monthly average water flow monitoring; the data quality confidence in the abnormally medium confidence scenario indicates that the current data quality behavior of the household owner of the corresponding edge water meter remains within the speed limit, with a moderate cubic meter, tending to be monitored by the monthly average water flow; and the data quality confidence in the abnormally high confidence scenario indicates that the current data quality behavior of the household owner of the corresponding edge water meter is relatively slow, tending to be lower than the monthly average water flow monitoring;

[0079] Configure the quality listener, define the data quality anomaly coefficient in the data quality confidence sample set as the input data of the quality listener, and define the setting confidence scenario in the data quality confidence sample set as the output data of the quality listener, optimize the quality listener, and output the initial monitoring network;

[0080] The initial monitoring network is model-verified by using the data quality confidence data set to be identified, and the corresponding initial monitoring network outputted with a value greater than or equal to a preset accuracy threshold is defined as a network model for data quality confidence identification;

[0081] S4: repeat the above steps S2 to S3 until j=N, and output different data quality confidences of all edge water meters;

[0082] It can be understood that: by distinguishing and comparing each edge water meter and the data quality anomaly coefficient, and finally inputting the data quality anomaly coefficient corresponding to each edge water meter into the network model that has been optimized in advance for data quality confidence identification, the data quality confidence of each edge water meter and householder in the water meter community to be controlled can be qualitatively determined, which is beneficial to provide important data support for subsequent water diversion.

[0083] S5: Analyze the distribution status of edge water meters with different data quality confidence levels in the community of water meters to be controlled, generate corresponding monitoring plans for edge water meters with different data quality confidence levels according to the distribution status, and transmit the monitoring plans to the corresponding edge water meters in sequence;

[0084] In implementation, the generation of corresponding monitoring schemes for edge water meters with different data quality confidence levels includes:

[0085] Collect W buildings in the water meter community to be controlled and the building ID of each building;

[0086] The number of edge water meters in the abnormally low confidence scene, the number of edge water meters in the abnormally medium confidence scene, and the number of edge water meters in the abnormally high confidence scene in each building are collected in sequence;

[0087] Define the building with the highest priority of edge water meters in the abnormally low confidence scenario as a high-consumption building, filter and obtain the building IDs of the high-consumption buildings, and define the building IDs of the high-consumption buildings as the monitoring solution for edge water meters in the abnormally low confidence scenario;

[0088] Define the building with the highest priority of the number of edge water meters in the abnormal medium confidence scenario as a medium-consumption building, filter and obtain the building ID of the medium-consumption building, and define the building ID of the medium-consumption building as the monitoring plan for the edge water meter in the abnormal medium confidence scenario;

[0089] Define the building with the highest priority for the number of edge water meters in the abnormally high confidence scenario as a low-consumption building, filter and obtain the building IDs of the low-consumption buildings, and define the building IDs of the low-consumption buildings as the monitoring plan for the edge water meters in the abnormally high confidence scenario;

[0090] Exemplarily, it is assumed that there are 4 buildings in the water meter community to be controlled, that is, W=4, and the building IDs of the 4 buildings are T1 (first from the left), T2 (second from the left), T3 (first from the right) and T4 (second from the right) in sequence. It is further assumed that there are 20 edge water meters in the water meter community to be controlled, and there are 8 edge water meters with abnormally low confidence scenes in T2 (second from the left) (edge ​​water meters with abnormally low confidence scenes account for the largest proportion), and there are 5 edge water meters with abnormally medium confidence scenes in T3 (first from the right) (edge ​​water meters with abnormally medium confidence scenes account for the largest proportion), and there are 4 edge water meters with abnormally high confidence scenes in T4 (second from the right) (edge ​​water meters with abnormally high confidence scenes account for the largest proportion). Therefore, T2 (second from the left) is defined as a monitoring scheme for edge water meters with abnormally low confidence scenes, T3 (first from the right) is defined as a monitoring scheme for edge water meters with abnormally medium confidence scenes, and T4 (second from the right) is defined as a monitoring scheme for edge water meters with abnormally high confidence scenes;

[0091] By evaluating the data quality anomaly coefficients of different edge water meters, determining the data quality confidences of different edge water meters according to the data quality anomaly coefficients, and finally providing monitoring solutions for edge water meters with different data quality confidences, the present invention can accurately evaluate the data quality confidences of different edge water meters and data quality personnel in the current time and space, and the evaluation is more objective. Furthermore, by providing different data quality solutions for edge water meters with different data quality confidences; the present invention can play a role in indirectly intervening in or controlling the water use status, which is beneficial to alleviating the water shortage status in high-flow water use scenarios, ensuring the stability of the water use status, and improving the smoothness of the water use status in high-flow water use scenarios.

[0092] Since the electronic device introduced in this embodiment is an electronic device used to implement the configuration-free edge water meter water volume identification and data quality control method in the embodiment of the present application, based on the configuration-free edge water meter water volume identification and data quality control method introduced in the embodiment of the present application, the technical personnel of this field can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of the present application will not be described in detail here. As long as the technical personnel of this field implement the electronic device used by the configuration-free edge water meter water volume identification and data quality control method in the embodiment of the present application, it is within the scope of protection of this application.

[0093] 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 units is only a logical function division. There may be other division methods in actual implementation. For example, 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0094] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. All equivalent structures or equivalent process transformations made through the description and drawings of this application, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

[0095] The specific implementation methods of the invention are described in detail above, but they are only defined as examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A configuration-free edge water meter water volume identification and data quality control method, characterized in that: The method comprises: S1: Collect flow information records of N edge water meters in the water meter community to be controlled, and based on the flow information records of the N edge water meters, collect dynamic change characteristics of water use status in the water meter community to be controlled; S2: Filter and obtain the flow information record of the jth edge water meter, and define the flow information record of the jth edge water meter as the target user water meter information, distinguish and compare the dynamic change characteristics of the water use status and the target user water meter information to collect the difference records of the jth edge water meter, and calculate the data quality anomaly coefficient of the jth edge water meter according to the difference records; S3: Input the data quality anomaly coefficient of the j-th edge water meter into the network model optimized in advance for data quality confidence identification to identify the data quality confidence of the j-th edge water meter, and set j=j+1, and return to step S2; S4: repeat the above steps S2 to S3 until j=N, and output different data quality confidences of all edge water meters; S5: Analyze the distribution status of edge water meters with different data quality confidence levels in the community of water meters to be controlled, generate corresponding monitoring plans for edge water meters with different data quality confidence levels according to the distribution status, and transmit the monitoring plans to the corresponding edge water meters in sequence.

2. The configuration-free edge water meter water volume identification and data quality control method according to claim 1, characterized in that: The flow information record contains five fields, which are the average monthly water flow, instantaneous water flow rate, peak and valley water use cycle, maximum value number and minimum value number; The dynamic change characteristics of water usage status in the water meter community to be controlled are collected, including: a1: Filter and obtain the jth field in the traffic information record, and analyze all field values ​​of the jth field; a2: Divide all field values ​​of the j-th field by interval segmentation to collect P divided intervals; a3: Prioritize the P partitions in the order of information size, define the partition with the highest priority as a high priority cluster, calculate the mean of all data in the high priority cluster, and output the high priority value of the jth field; a4: Repeat the above steps a1 to a3 until j=5, then end the recursion and output the high priority value of the field; define the high priority value of the field as a dynamic change feature of the water usage status.

3. The configuration-free edge water meter water volume identification and data quality control method as claimed in claim 2, characterized in that: The step of distinguishing and comparing the dynamic change characteristics of water use status and water meter information of target users includes: b1: Filter and obtain the field value of the wth field in the target user's water meter information, and filter and obtain the high priority quantity of the same field in the dynamic change characteristics of the water use status, define the field value of the wth field as the field value to be identified, and define the high priority quantity of the same field as the reference field value; b2: Calculate the distance between the field value to be identified and the reference field value, and determine whether the distance between the field value to be identified and the reference field value is within a preset range. If so, set w=w+1 and return to step b1; if not, define the distance between the field value to be identified and the reference field value as the deviation ratio of the wth field, define the wth field as a distinguishing field, set w=w+1, and return to step b1; b3: Repeat the above steps b1 to b2 until w=5, end the recursion, output K distinguishing fields and the deviation ratio of each distinguishing field, and define the K distinguishing fields and the deviation ratio of each distinguishing field as the distinguishing record of the jth edge water meter.

4. The configuration-free edge water meter water volume identification and data quality control method as claimed in claim 3, characterized in that: The step of calculating the data quality anomaly coefficient of the j-th edge water meter according to the distinguished records includes: Screen and obtain the deviation ratio of each difference field in the difference record of the jth edge water meter; The deviation ratio of each distinguishing field is input into the preconfigured quality anomaly analysis model, and the data quality anomaly coefficient of the jth edge water meter is output; wherein the formula of the quality anomaly analysis model is as follows: Where: R is the data quality anomaly coefficient of the jth edge water meter, D g is the deviation ratio of the g-th distinguishing field.

5. The configuration-free edge water meter water volume identification and data quality control method according to claim 4, characterized in that: The optimization steps of the network model for data quality confidence identification are as follows: Collect historical data quality confidence sample information, and divide the historical data quality confidence sample information into a data quality confidence sample set and a data quality confidence data set to be identified; the historical data quality confidence sample information includes data quality anomaly coefficients of multiple edge water meters and corresponding data quality confidence setting confidence scenarios; The data quality confidence setting confidence scenarios include abnormally low confidence scenarios, abnormally medium confidence scenarios, and abnormally high confidence scenarios; Configure the quality listener, define the data quality anomaly coefficient in the data quality confidence sample set as the input data of the quality listener, and define the setting confidence scenario in the data quality confidence sample set as the output data of the quality listener, optimize the quality listener, and output the initial monitoring network; The initial monitoring network is model verified using the data quality confidence data set to be identified, and the corresponding initial monitoring network outputted with a value greater than or equal to a preset accuracy threshold is defined as a network model for data quality confidence identification.

6. The configuration-free edge water meter water volume identification and data quality control method according to claim 5, characterized in that: The generation logic of setting the confidence scenario in the historical data quality confidence sample information is as follows: Setting a data quality anomaly coefficient threshold value, wherein the data quality anomaly coefficient threshold value includes a data quality anomaly coefficient threshold value Lim1 and a data quality anomaly coefficient threshold value Lim2, Lim1>Lim2; Compare the data quality anomaly coefficient R with the data quality anomaly coefficient threshold value; If R ≥ Lim1, the edge water meter corresponding to the data quality anomaly coefficient is set to the data quality confidence of the abnormally low confidence scenario; If R<Lim1 and R>Lim2, the edge water meter corresponding to the data quality anomaly coefficient is set to the data quality confidence of the abnormal medium confidence scenario; If R≤Lim1, the edge water meter corresponding to the data quality anomaly coefficient is set to the data quality confidence of the abnormally high confidence scenario.

7. The configuration-free edge water meter water volume identification and data quality control method according to claim 6, characterized in that: The generating of corresponding monitoring schemes for edge water meters with different data quality confidence levels includes: Collect W buildings in the water meter community to be controlled and the building ID of each building; The number of edge water meters in the abnormally low confidence scenes, the number of edge water meters in the abnormally medium confidence scenes, and the number of edge water meters in the abnormally high confidence scenes in each building are collected in sequence; Define the building with the highest priority of edge water meters in the abnormally low confidence scenario as a high-consumption building, filter and obtain the building IDs of the high-consumption buildings, and define the building IDs of the high-consumption buildings as the monitoring solution for edge water meters in the abnormally low confidence scenario; Define the building with the highest priority of the number of edge water meters in the abnormal medium confidence scenario as a medium-consumption building, filter and obtain the building ID of the medium-consumption building, and define the building ID of the medium-consumption building as the monitoring plan for the edge water meter in the abnormal medium confidence scenario; The building with the highest priority in the number of edge water meters in the abnormally high confidence scene is defined as a low-consumption building, and the building IDs of the low-consumption buildings are obtained by screening, and the building IDs of the low-consumption buildings are defined as the monitoring plan for the edge water meters in the abnormally high confidence scene.

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