A sewer network data acquisition method and system

Abstract

This invention discloses a method and system for data acquisition in water supply and drainage pipe networks, specifically relating to the field of multi-parameter monitoring technology. The methods include benchmark parameter calibration, environmental adaptability level determination, dynamic multi-parameter screening, node acquisition standard determination, and water quality data classification and labeling. This invention obtains the design standard parameters of the water supply and drainage pipe network and the actual initial acquisition parameters, compares and adjusts the parameter adaptability to establish a reliable benchmark for subsequent acquisition; it collects environmental parameters surrounding the pipe network, compares them with suitable acquisition environment requirements to classify interference levels, avoids environmental interference, and improves the purity of the acquired data; it calls upon the environmental adaptability level and benchmark adaptability scheme, activates sensors to acquire real-time data and filters it, reducing invalid data; it determines the node type, clarifies acquisition requirements based on the benchmark adaptability scheme, and improves the targeting of acquisition; it compares the acquisition parameters with the node adaptability standards to verify acquisition adaptability, ensuring process compliance and accurate results, forming a closed-loop acquisition process.

Description

Technical Field

[0001] This invention relates to the field of multi-parameter monitoring technology, and in particular to a data acquisition method and system for water supply and drainage pipe networks. Background Technology

[0002] The field of multi-parameter monitoring technology encompasses technologies that integrate multiple sensor parameters to achieve multi-dimensional state perception of target objects. Its core lies in employing a distributed sensor network architecture, deploying various types of sensors to acquire multiple physical or chemical parameters of the target object, and transmitting the collected raw data to a designated unit via wired or wireless communication, forming a complete monitoring data chain. This field integrates sensing and data transmission technologies, covering multiple application scenarios such as industrial monitoring, environmental monitoring, and water monitoring. It revolves around multi-dimensional data acquisition and transmission that is not limited to a single variable, adapting to the need for comprehensive state understanding of target objects in various scenarios, and falling within the technical scope of measuring two or more variables that are not included in other single subcategories.

[0003] One method for collecting data from water supply and drainage networks refers to specific technical means for collecting relevant data during the operation of water supply and drainage networks. The technical aspects covered include the collection of multiple parameters such as flow rate, pressure, water level, and water quality at key nodes of the water supply and drainage network. Specifically, based on the requirement of measuring two or more variables that are not included in other individual subcategories, flow meters, pressure sensors, water quality sensors, and water level sensors are installed at key nodes such as water source booster stations and zone metering areas of the water supply and drainage network. The parameter data collected by various sensors are transmitted in real time to a designated data receiving unit via wireless communication methods such as NB-IoT and 4G. Simultaneously, manual on-site collection of network-related data not yet obtained through sensing devices can be combined to complete the comprehensive collection of multi-parameter data from the water supply and drainage network.

[0004] Existing technologies focus on deploying sensors to collect and transmit parameters without calibrating and adjusting the acquisition benchmark. Even when parameters deviate, data is still collected based on a fixed benchmark, resulting in data that does not meet actual needs. They also fail to identify and classify environmental interference, leading to mixed interference information in complex environments, increasing processing burden and increasing the risk of failure. Furthermore, the lack of a dynamic filtering mechanism results in the simple collection and transmission of data, with invalid data consuming resources and reducing efficiency. The failure to clearly define acquisition requirements according to node type leads to insufficient accuracy in key parameter acquisition due to a lack of standardized criteria. Finally, the absence of adaptation and verification processes results in insufficient usability and reliability of the collected data, impacting subsequent analysis and decision-making. Summary of the Invention

[0005] The main objective of this invention is to provide a method and system for data acquisition in water supply and drainage pipe networks, which can effectively solve the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for collecting data from water supply and drainage pipe networks includes the following steps:

[0008] S1. Baseline parameter calibration: Obtain the design standard parameters of the water supply and drainage network, retrieve the actual initial acquisition parameters, compare the compatibility between the two, and make fine adjustments if they do not match to obtain the network acquisition baseline adaptation scheme.

[0009] S2. Environmental Adaptability Level Determination: Collect environmental parameters around the pipeline network, retrieve suitable collection environment requirements, compare the fit, classify the interference level, and obtain the pipeline network collection environment adaptability level.

[0010] S3. Multi-parameter dynamic filtering: Call the pipeline network acquisition environment adaptation level and pipeline network acquisition benchmark adaptation scheme, enable pipeline network pressure acquisition sensor and pipeline network water quality acquisition sensor, obtain real-time multi-parameter data, compare with the two to filter matching data, and obtain a list of valid pipeline network multi-parameter acquisitions.

[0011] S4. Determining Node Acquisition Standards: Obtain functional parameters of pipeline network nodes, determine node types, enable feedback from water source booster stations and end-user acquisition points, and, in conjunction with the pipeline network acquisition benchmark adaptation scheme, clarify acquisition requirements to obtain node acquisition adaptation standards.

[0012] S5. Water quality data classification and identification: Call the list of valid multi-parameter acquisitions of the pipeline network and the node acquisition adaptation standard, extract the acquisition parameters and compare them to determine whether they meet the standard, and obtain the node adaptation verification conclusion of the multi-parameter acquisition method of the pipeline network.

[0013] Preferably, the pipeline network acquisition benchmark adaptation scheme includes flow acquisition benchmark, water quality acquisition benchmark, and pressure acquisition benchmark; the pipeline network acquisition environment adaptation level includes temperature and humidity adaptation level and electromagnetic interference adaptation level; the pipeline network multi-parameter acquisition valid list includes valid pressure acquisition data and valid water quality acquisition data; the node acquisition adaptation standard includes water source booster station acquisition standard, end user acquisition standard, and zone metering area acquisition standard; and the pipeline network multi-parameter acquisition method node adaptation verification conclusion includes parameter adaptation conclusion and node adaptation conclusion.

[0014] Preferably, S1 includes:

[0015] Obtain the standard design parameters of the water supply and drainage network, retrieve the actual initial acquisition parameters of the network, compare the standard design parameters with the actual initial acquisition parameters item by item, calculate the difference of each parameter, and obtain the parameter adaptation deviation.

[0016] Call the parameter adaptation deviation, retrieve the preset adaptation deviation threshold, compare the parameter adaptation deviation with the preset adaptation deviation threshold item by item, determine whether the deviation of each parameter exceeds the threshold, and generate a deviation parameter identifier table;

[0017] The deviation parameter identification table and parameter adaptation deviation degree are called, and the identified deviation parameters are fine-tuned one by one. After adjustment, the parameter adaptation deviation degree is recalculated until the deviation does not exceed the preset adaptation deviation threshold, thus obtaining the pipeline acquisition benchmark adaptation scheme.

[0018] Preferably, S2 includes:

[0019] Collect environmental parameters around the pipeline network, retrieve suitable collection environment requirements, compare the real-time collected temperature, humidity, and electromagnetic interference parameters with the suitable collection environment requirements item by item, calculate the matching ratio of each parameter, and obtain the environmental parameter matching coefficient.

[0020] The environmental parameter matching coefficient is called, the preset environmental adaptation level classification threshold is retrieved, and the environmental parameter matching coefficient is compared with the preset environmental adaptation level classification threshold one by one to determine the threshold range to which the coefficient belongs, so as to obtain the pipeline network acquisition environment adaptation level.

[0021] Preferably, S3 includes:

[0022] The pipeline network acquisition environment adaptation level and pipeline network acquisition benchmark adaptation scheme are called, the acquisition parameter requirements and adaptation standards in the two are extracted, and integrated to form a unified screening basis. The weight ratio of each basis is calculated to obtain the acquisition screening weight coefficient.

[0023] Based on the collection and screening weight coefficient, the pipeline pressure collection sensor and the pipeline water quality collection sensor are enabled, the sensor data collection function is started, the real-time pressure and water quality parameters of the pipeline are obtained, the matching score between the real-time parameters and the screening criteria is calculated, and the parameter matching score is obtained.

[0024] The system calls the collection and filtering weight coefficient and parameter matching score, sets the qualified score for parameter matching, compares the parameter matching score with the qualified score, filters out the data with the qualified matching score, and obtains the valid list of multi-parameter collection of the pipeline network.

[0025] Preferably, S4 includes:

[0026] Obtain the functional parameters of the pipeline nodes, analyze the node operation characteristics and functional identifiers in the parameters, judge each item according to the node type classification standard, calculate the matching degree between the parameters and various node types, and obtain the node type determination coefficient;

[0027] Call the node type determination coefficient to determine the corresponding node type, enable the data feedback function of the water source booster station and end user collection point, receive the real-time operating parameters of the collection point, calculate the completeness of the feedback parameters, and obtain the completeness of the feedback parameters;

[0028] Based on the completeness of feedback parameters and the node type determination coefficient, the pipeline network acquisition benchmark adaptation scheme is called, the acquisition parameter requirements for the corresponding node type in the scheme are extracted, the fit between the requirements and the feedback parameters is calculated, and the acquisition requirement adaptation coefficient is obtained.

[0029] The system calls the data collection requirement adaptation coefficient and node type determination coefficient, combines the feedback parameter completeness, integrates the data collection parameter requirements and adaptation standards of the corresponding nodes, clarifies the specifications of each data collection parameter, and obtains the node data collection adaptation standard.

[0030] Preferably, S5 includes:

[0031] The effective list of multi-parameter data acquisition in the pipeline network and the node acquisition adaptation standard are called. The water quality acquisition parameters and pressure acquisition parameters in the effective list are extracted, the parameter names and values ​​are sorted out, and the parameter corresponding matching rate is calculated by comparing them with the parameter items in the adaptation standard.

[0032] Based on the parameter matching rate, the parameter matching qualification threshold is retrieved, and the parameter matching rate is compared with the qualification threshold to determine whether various collected parameters meet the node adaptation standard. The percentage of parameters that meet the standard is counted to obtain the node adaptation verification conclusion.

[0033] In addition, the present invention also provides a system for applying the above-mentioned water supply and drainage network data acquisition method, comprising:

[0034] The benchmark parameter calibration module is used to obtain the design standard parameters of the water supply and drainage network and the actual initial acquisition parameters of the network. It performs adaptation and comparison of the two types of parameters, fine-tunes the parameters that do not match, and outputs the benchmark adaptation scheme for network acquisition.

[0035] The environment adaptability level determination module is used to collect environmental parameters around the pipeline network, retrieve suitable collection environment requirements, compare the actual environmental parameters with the suitable collection environment requirements, classify the environmental interference level, and output the pipeline network collection environment adaptability level.

[0036] The multi-parameter dynamic filtering module is connected to the benchmark parameter calibration module and the environmental adaptation level determination module respectively. It is used to call the pipeline network acquisition environmental adaptation level and pipeline network acquisition benchmark adaptation scheme, enable the pipeline network pressure acquisition sensor and pipeline network water quality acquisition sensor to obtain real-time multi-parameter data of the pipeline network, filter matching data by comparing with the adaptation scheme and environmental adaptation level, and output a list of valid pipeline network multi-parameter acquisitions.

[0037] The node acquisition standard determination module is connected to the benchmark parameter calibration module. It is used to acquire the functional parameters of the pipeline node, determine the node type, enable parameter feedback from the water source booster station and end user acquisition point, clarify the node acquisition requirements in combination with the pipeline acquisition benchmark adaptation scheme, and output the node acquisition adaptation standard.

[0038] The water quality data classification and identification module is connected to the multi-parameter dynamic screening module and the node acquisition standard determination module. It is used to call the valid list of multi-parameter acquisition in the pipeline network and the node acquisition adaptation standard, extract the acquisition parameters and compare them, determine whether the acquisition parameters meet the adaptation standard, and output the node adaptation verification conclusion of the multi-parameter acquisition method in the pipeline network.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The process involves obtaining standard design parameters and initial data collection parameters for the water supply and drainage network, comparing and adjusting parameter compatibility to establish a reliable benchmark for subsequent data collection; collecting environmental parameters around the network, comparing them with suitable environmental requirements to classify interference levels, avoiding environmental interference and improving the purity of collected data; applying environmental compatibility levels and benchmark compatibility schemes, enabling sensors to acquire real-time data and filtering to reduce invalid data; determining node types, clarifying collection requirements based on benchmark compatibility schemes to improve the targeting of data collection; and comparing collected parameters with node compatibility standards to verify data compatibility, ensuring process compliance and accurate results, thus forming a closed-loop data collection process. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the molecular generation model architecture of the present invention. Detailed Implementation

[0042] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0043] Example 1, as Figure 1 As shown, a method for collecting data from water supply and drainage pipe networks includes the following steps:

[0044] S1. Reference parameter calibration: Obtain the design standard parameters of the water supply and drainage network, determine the relevant reference requirements for data collection, retrieve the actual initial data collection parameters of the network, compare the preset parameters with the initial parameters item by item with the design standard parameters, and fine-tune the preset parameters one by one if they do not match until the parameters are matched, and obtain the network data collection reference adaptation scheme.

[0045] S2. Environmental Adaptability Level Determination: Collect environmental parameters around the pipeline network, retrieve the suitable collection environment requirements of the pipeline network, compare the actual environmental parameters with the suitable requirements item by item, clarify the compatibility of each parameter, and comprehensively classify the environmental interference adaptability level based on the compatibility of each parameter to obtain the pipeline network collection environment adaptability level.

[0046] S3. Multi-parameter dynamic filtering: Call the pipeline network acquisition environment adaptation level and pipeline network acquisition benchmark adaptation scheme, enable pipeline network pressure acquisition sensor and pipeline network water quality acquisition sensor, obtain real-time multi-parameter data of pipeline network, filter matching data according to the adaptation scheme and adaptation level, and obtain a list of valid multi-parameter acquisitions of pipeline network.

[0047] S4. Determining the node acquisition standard: Obtain the functional parameters of the pipeline network nodes, determine the node type item by item according to the functional characteristics of the nodes, enable the parameter feedback of the water source booster station and the end user acquisition point, and combine the pipeline network acquisition benchmark adaptation scheme to clarify the water quality acquisition requirements of the corresponding nodes and obtain the node acquisition adaptation standard.

[0048] S5. Water quality data classification and identification: Call the valid list of multi-parameter acquisition in the pipeline network and the node acquisition adaptation standard, extract various acquisition parameters from the valid list, compare each parameter with the node adaptation standard item by item, determine whether the parameter meets the standard, and obtain the node adaptation verification conclusion of the multi-parameter acquisition method in the pipeline network.

[0049] The pipeline network data acquisition benchmark adaptation scheme includes flow acquisition benchmark, water quality acquisition benchmark, and pressure acquisition benchmark. The pipeline network data acquisition environment adaptation level includes temperature and humidity adaptation level and electromagnetic interference adaptation level. The effective list of pipeline network multi-parameter acquisition includes effective pressure acquisition data and effective water quality acquisition data. The node acquisition adaptation standard includes water source booster station acquisition standard, end user acquisition standard, and zone metering area acquisition standard. The pipeline network multi-parameter acquisition method node adaptation verification conclusion includes parameter adaptation conclusion and node adaptation conclusion.

[0050] S1 includes:

[0051] Obtain the standard design parameters of the water supply and drainage network, retrieve the actual initial acquisition parameters of the network, compare the standard design parameters with the actual initial acquisition parameters item by item, calculate the difference of each parameter, and obtain the parameter adaptation deviation.

[0052] Call the parameter adaptation deviation, retrieve the preset adaptation deviation threshold, compare the parameter adaptation deviation with the preset adaptation deviation threshold item by item, determine whether the deviation of each parameter exceeds the threshold, and generate a deviation parameter identifier table;

[0053] The deviation parameter identification table and parameter adaptation deviation degree are called, and the identified deviation parameters are fine-tuned one by one. After adjustment, the parameter adaptation deviation degree is recalculated until the deviation does not exceed the preset adaptation deviation threshold, thus obtaining the pipeline acquisition benchmark adaptation scheme.

[0054] The application scenario is selected as the water supply and drainage network of a residential community, with a design pressure standard of 0.3MPa and a flow rate standard of 15m³ / h. 3 / h, actual initial sampling pressure 0.28MPa, flow rate 14.2m³ / h. 3The two types of parameters were compared item by item, and the difference was calculated using the formula: Parameter Difference = Design Standard Parameter - Actual Initial Acquired Parameter. The pressure difference was 0.02 MPa, and the flow rate difference was 0.8 m³ / h. 3 / h, then according to the deviation = (parameter difference / design standard parameter) × 100%, the pressure adaptation deviation is approximately 6.67% and the flow rate is approximately 5.33%. The parameter adaptation deviation is obtained. The preset adaptation deviation threshold is retrieved. The pressure is set to ±8% and the flow rate to ±7% according to the procedure. After comparison, it is determined that the deviations of both items do not exceed the threshold. A deviation parameter identification table is generated. No fine-tuning of the parameters is required. The deviation is verified again to be within the standard. The pipeline acquisition benchmark adaptation scheme is obtained.

[0055] S2 includes:

[0056] Collect environmental parameters around the pipeline network, retrieve suitable collection environment requirements, compare the real-time collected temperature, humidity, and electromagnetic interference parameters with the suitable collection environment requirements item by item, calculate the matching ratio of each parameter, and obtain the environmental parameter matching coefficient.

[0057] The environmental parameter matching coefficient is called, the preset environmental adaptation level classification threshold is retrieved, and the environmental parameter matching coefficient is compared with the preset environmental adaptation level classification threshold one by one to determine the threshold range to which the coefficient belongs, so as to obtain the pipeline network acquisition environment adaptation level.

[0058] The surrounding area of ​​the industrial park's pipeline network was selected as the scenario. Temperature, humidity, and electromagnetic interference parameters were collected every 10 minutes using environmental sensors. The average value of three consecutive measurements was taken, resulting in a temperature of 26℃, humidity of 62%RH, and electromagnetic interference of 38dBμV / m. The suitable range was set as temperature 15-35℃, humidity 40-75%RH, and electromagnetic interference ≤45dBμV / m. After comparing each parameter, the matching ratio was calculated as (the percentage of real-time parameters within the suitable range) × 100%. With all three parameters at 100% and the average matching ratio at 100%, the environmental parameter matching coefficient was obtained. A preset threshold was applied, and a matching coefficient ≥90% was considered a first-level fit. 100% was determined to be within this range, thus obtaining the pipeline network data acquisition environment adaptation level.

[0059] S3 includes:

[0060] The pipeline network acquisition environment adaptation level and pipeline network acquisition benchmark adaptation scheme are called, the acquisition parameter requirements and adaptation standards in the two are extracted, and integrated to form a unified screening basis. The weight ratio of each basis is calculated to obtain the acquisition screening weight coefficient.

[0061] Based on the collection and screening weight coefficient, the pipeline pressure collection sensor and the pipeline water quality collection sensor are enabled, the sensor data collection function is started, the real-time pressure and water quality parameters of the pipeline are obtained, the matching score between the real-time parameters and the screening criteria is calculated, and the parameter matching score is obtained.

[0062] The system calls the collection and filtering weight coefficient and parameter matching score, sets the qualified score for parameter matching, compares the parameter matching score with the qualified score, filters out the data with the qualified matching score, and obtains the valid list of multi-parameter collection of the pipeline network.

[0063] Use the pipeline network data acquisition environment adaptation level (Level 1) and the pipeline network data acquisition baseline adaptation scheme (pressure 0.3MPa, flow rate 15m³ / h). 3 / h), extract the collection requirements from both, the environmental level 1 adaptability allows parameter fluctuations of ±5%, and the integration and screening criteria are pressure 0.3±5%MPa and flow rate 15±5%m³ / h. 3 / h, combined with priority settings of pressure weight 60% and flow rate 40%, the data collection and screening weight coefficient is obtained. Two types of sensors are enabled, and data is collected continuously for 5 times at 5 minutes / time to obtain real-time pressure and COD data. The matching score is calculated as (pressure matching degree × 60%) + (flow matching degree × 40%). If the matching score of all 5 times is ≥95%, the qualified data is screened to obtain a list of valid multi-parameter data collection for the pipeline network.

[0064] S4 includes:

[0065] Obtain the functional parameters of the pipeline nodes, analyze the node operation characteristics and functional identifiers in the parameters, judge each item according to the node type classification standard, calculate the matching degree between the parameters and various node types, and obtain the node type determination coefficient;

[0066] Call the node type determination coefficient to determine the corresponding node type, enable the data feedback function of the water source booster station and end user collection point, receive the real-time operating parameters of the collection point, calculate the completeness of the feedback parameters, and obtain the completeness of the feedback parameters;

[0067] Based on the completeness of feedback parameters and the node type determination coefficient, the pipeline network acquisition benchmark adaptation scheme is called, the acquisition parameter requirements for the corresponding node type in the scheme are extracted, the fit between the requirements and the feedback parameters is calculated, and the acquisition requirement adaptation coefficient is obtained.

[0068] The system calls the data collection requirement adaptation coefficient and node type determination coefficient, combines the feedback parameter completeness, integrates the data collection parameter requirements and adaptation standards of the corresponding nodes, clarifies the specifications of each data collection parameter, and obtains the node data collection adaptation standard.

[0069] Obtain functional parameters of pipeline network nodes. Select three key nodes in the town and obtain operating pressure, flow rate, and water supply range through monitoring equipment. Node 1 (0.4MPa, 20m) 3 / h, 2000 households), Node 2 (0.3MPa, 12m) 3 / h, 800 households), Node 3 (0.25MPa, 8m) 3( / h, 500 households), compare with the node division standard, calculate the matching degree according to (sum of parameter conformity / total number of items) × 100%, the matching degree of all three is 100%, obtain the node type determination coefficient, after determine the node type, activate the water source booster station and end user feedback, receive real-time parameters, calculate the feedback completeness of 100%, combine with the benchmark scheme to extract the collection requirements, the conformity is 100%, integrate the specifications, and obtain the node collection adaptation standard.

[0070] S5 includes:

[0071] The effective list of multi-parameter data acquisition in the pipeline network and the node acquisition adaptation standard are called. The water quality acquisition parameters and pressure acquisition parameters in the effective list are extracted, the parameter names and values ​​are sorted out, and the parameter corresponding matching rate is calculated by comparing them with the parameter items in the adaptation standard.

[0072] Based on the parameter matching rate, the parameter matching qualification threshold is retrieved, and the parameter matching rate is compared with the qualification threshold to determine whether various collected parameters meet the node adaptation standard. The percentage of parameters that meet the standard is counted to obtain the node adaptation verification conclusion.

[0073] The effective list of multi-parameter data collection in the pipeline network and the node data collection adaptation standard were called. The effective list contained 5 data points of pressure, flow, and COD. The adaptation standard specified that the pressure of the water source booster station was 0.285-0.315MPa and the COD was 20-35mg / L. Water quality and pressure parameters were extracted, and the corresponding water source booster stations were identified after sorting. The parameter matching rate was calculated as (number of standard-compliant items / total number of items) × 100%. All 10 parameters met the standard, and the matching rate was 100%. The parameter matching rate was obtained. The qualified threshold of ≥90% was retrieved and compared to determine that all parameters met the standard. The compliance rate was 100%, and the node adaptation verification conclusion was obtained.

[0074] In addition, the present invention also provides a system for applying the above-mentioned water supply and drainage network data acquisition method, comprising:

[0075] The benchmark parameter calibration module is used to obtain the design standard parameters of the water supply and drainage network and the actual initial acquisition parameters of the network. It performs adaptation and comparison of the two types of parameters, fine-tunes the parameters that do not match, and outputs the benchmark adaptation scheme for network acquisition.

[0076] The environment adaptability level determination module is used to collect environmental parameters around the pipeline network, retrieve suitable collection environment requirements, compare the actual environmental parameters with the suitable collection environment requirements, classify the environmental interference level, and output the pipeline network collection environment adaptability level.

[0077] The multi-parameter dynamic filtering module is connected to the benchmark parameter calibration module and the environmental adaptation level determination module respectively. It is used to call the pipeline network acquisition environmental adaptation level and pipeline network acquisition benchmark adaptation scheme, enable the pipeline network pressure acquisition sensor and pipeline network water quality acquisition sensor to obtain real-time multi-parameter data of the pipeline network, filter matching data by comparing with the adaptation scheme and environmental adaptation level, and output a list of valid pipeline network multi-parameter acquisitions.

[0078] The node acquisition standard determination module is connected to the benchmark parameter calibration module. It is used to acquire the functional parameters of the pipeline node, determine the node type, enable parameter feedback from the water source booster station and end user acquisition point, clarify the node acquisition requirements in combination with the pipeline acquisition benchmark adaptation scheme, and output the node acquisition adaptation standard.

[0079] The water quality data classification and identification module is connected to the multi-parameter dynamic screening module and the node acquisition standard determination module. It is used to call the valid list of multi-parameter acquisition in the pipeline network and the node acquisition adaptation standard, extract the acquisition parameters and compare them, determine whether the acquisition parameters meet the adaptation standard, and output the node adaptation verification conclusion of the multi-parameter acquisition method in the pipeline network.

[0080] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended technical solutions and their equivalents.

Claims

1. A method for collecting data from water supply and drainage pipe networks, comprising the following steps: S1. Baseline parameter calibration: Obtain the design standard parameters of the water supply and drainage network, retrieve the actual initial acquisition parameters, compare the compatibility between the two, and make fine adjustments if they do not match to obtain the network acquisition baseline adaptation scheme. S2. Environmental Adaptability Level Determination: Collect environmental parameters around the pipeline network, retrieve suitable collection environment requirements, compare the fit, classify the interference level, and obtain the pipeline network collection environment adaptability level. S3. Multi-parameter dynamic filtering: Call the pipeline network acquisition environment adaptation level and pipeline network acquisition benchmark adaptation scheme, enable pipeline network pressure acquisition sensor and pipeline network water quality acquisition sensor, obtain real-time multi-parameter data, compare with the two to filter matching data, and obtain a list of valid pipeline network multi-parameter acquisitions. S4. Determining Node Acquisition Standards: Obtain functional parameters of pipeline network nodes, determine node types, enable feedback from water source booster stations and end-user acquisition points, and, in conjunction with the pipeline network acquisition benchmark adaptation scheme, clarify acquisition requirements to obtain node acquisition adaptation standards. S5. Water quality data classification and identification: Call the list of valid multi-parameter acquisitions of the pipeline network and the node acquisition adaptation standard, extract the acquisition parameters and compare them to determine whether they meet the standard, and obtain the node adaptation verification conclusion of the multi-parameter acquisition method of the pipeline network.

2. The method for acquiring water supply and drainage network data according to claim 1, characterized in that: The pipeline network data acquisition benchmark adaptation scheme includes flow acquisition benchmark, water quality acquisition benchmark, and pressure acquisition benchmark. The pipeline network data acquisition environment adaptation level includes temperature and humidity adaptation level and electromagnetic interference adaptation level. The pipeline network multi-parameter acquisition valid list includes valid pressure acquisition data and valid water quality acquisition data. The node acquisition adaptation standard includes water source booster station acquisition standard, end user acquisition standard, and zone metering area acquisition standard. The pipeline network multi-parameter acquisition method node adaptation verification conclusion includes parameter adaptation conclusion and node adaptation conclusion.

3. The method for acquiring water supply and drainage network data according to claim 1, characterized in that: S1 includes: Obtain the standard design parameters of the water supply and drainage network, retrieve the actual initial acquisition parameters of the network, compare the standard design parameters with the actual initial acquisition parameters item by item, calculate the difference of each parameter, and obtain the parameter adaptation deviation. Call the parameter adaptation deviation, retrieve the preset adaptation deviation threshold, compare the parameter adaptation deviation with the preset adaptation deviation threshold item by item, determine whether the deviation of each parameter exceeds the threshold, and generate a deviation parameter identifier table; The deviation parameter identification table and parameter adaptation deviation degree are called, and the identified deviation parameters are fine-tuned one by one. After adjustment, the parameter adaptation deviation degree is recalculated until the deviation does not exceed the preset adaptation deviation threshold, thus obtaining the pipeline acquisition benchmark adaptation scheme.

4. The method for acquiring water supply and drainage network data according to claim 1, characterized in that: S2 includes: Collect environmental parameters around the pipeline network, retrieve suitable collection environment requirements, compare the real-time collected temperature, humidity, and electromagnetic interference parameters with the suitable collection environment requirements item by item, calculate the matching ratio of each parameter, and obtain the environmental parameter matching coefficient. The environmental parameter matching coefficient is called, the preset environmental adaptation level classification threshold is retrieved, and the environmental parameter matching coefficient is compared with the preset environmental adaptation level classification threshold one by one to determine the threshold range to which the coefficient belongs, so as to obtain the pipeline network acquisition environment adaptation level.

5. The method for acquiring water supply and drainage network data according to claim 1, characterized in that: S3 includes: The pipeline network acquisition environment adaptation level and pipeline network acquisition benchmark adaptation scheme are called, the acquisition parameter requirements and adaptation standards in the two are extracted, and integrated to form a unified screening basis. The weight ratio of each basis is calculated to obtain the acquisition screening weight coefficient. Based on the collection and screening weight coefficient, the pipeline pressure collection sensor and the pipeline water quality collection sensor are enabled, the sensor data collection function is started, the real-time pressure and water quality parameters of the pipeline are obtained, the matching score between the real-time parameters and the screening criteria is calculated, and the parameter matching score is obtained. The system calls the collection and filtering weight coefficient and parameter matching score, sets the qualified score for parameter matching, compares the parameter matching score with the qualified score, filters out the data with the qualified matching score, and obtains the valid list of multi-parameter collection of the pipeline network.

6. The method for acquiring water supply and drainage network data according to claim 1, characterized in that: S4 includes: Obtain the functional parameters of the pipeline nodes, analyze the node operation characteristics and functional identifiers in the parameters, judge each item according to the node type classification standard, calculate the matching degree between the parameters and various node types, and obtain the node type determination coefficient; Call the node type determination coefficient to determine the corresponding node type, enable the data feedback function of the water source booster station and end user collection point, receive the real-time operating parameters of the collection point, calculate the completeness of the feedback parameters, and obtain the completeness of the feedback parameters; Based on the completeness of feedback parameters and the node type determination coefficient, the pipeline network acquisition benchmark adaptation scheme is called, the acquisition parameter requirements for the corresponding node type in the scheme are extracted, the fit between the requirements and the feedback parameters is calculated, and the acquisition requirement adaptation coefficient is obtained. The system calls the data collection requirement adaptation coefficient and node type determination coefficient, combines the feedback parameter completeness, integrates the data collection parameter requirements and adaptation standards of the corresponding nodes, clarifies the specifications of each data collection parameter, and obtains the node data collection adaptation standard.

7. The method for acquiring water supply and drainage network data according to claim 1, characterized in that: S5 includes: The effective list of multi-parameter data acquisition in the pipeline network and the node acquisition adaptation standard are called. The water quality acquisition parameters and pressure acquisition parameters in the effective list are extracted, the parameter names and values ​​are sorted out, and the parameter corresponding matching rate is calculated by comparing them with the parameter items in the adaptation standard. Based on the parameter matching rate, the parameter matching qualification threshold is retrieved, and the parameter matching rate is compared with the qualification threshold to determine whether various collected parameters meet the node adaptation standard. The percentage of parameters that meet the standard is counted to obtain the node adaptation verification conclusion.

8. A system for acquiring data from water supply and drainage networks according to any one of claims 1-7, configured as follows: The benchmark parameter calibration module is used to obtain the design standard parameters of the water supply and drainage network and the actual initial acquisition parameters of the network. It performs adaptation and comparison of the two types of parameters, fine-tunes the parameters that do not match, and outputs the benchmark adaptation scheme for network acquisition. The environment adaptability level determination module is used to collect environmental parameters around the pipeline network, retrieve suitable collection environment requirements, compare the actual environmental parameters with the suitable collection environment requirements, classify the environmental interference level, and output the pipeline network collection environment adaptability level. The multi-parameter dynamic filtering module is connected to the benchmark parameter calibration module and the environmental adaptation level determination module respectively. It is used to call the pipeline network acquisition environmental adaptation level and pipeline network acquisition benchmark adaptation scheme, enable the pipeline network pressure acquisition sensor and pipeline network water quality acquisition sensor to obtain real-time multi-parameter data of the pipeline network, filter matching data by comparing with the adaptation scheme and environmental adaptation level, and output a list of valid pipeline network multi-parameter acquisitions. The node acquisition standard determination module is connected to the benchmark parameter calibration module. It is used to acquire the functional parameters of the pipeline node, determine the node type, enable parameter feedback from the water source booster station and end user acquisition point, clarify the node acquisition requirements in combination with the pipeline acquisition benchmark adaptation scheme, and output the node acquisition adaptation standard. The water quality data classification and identification module is connected to the multi-parameter dynamic screening module and the node acquisition standard determination module. It is used to call the valid list of multi-parameter acquisition in the pipeline network and the node acquisition adaptation standard, extract the acquisition parameters and compare them, determine whether the acquisition parameters meet the adaptation standard, and output the node adaptation verification conclusion of the multi-parameter acquisition method in the pipeline network.

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