Water quality monitoring big data storage and correlation analysis method and system

Abstract

The present application relates to water quality monitoring data processing technical field, specifically to a kind of water quality monitoring big data storage and association analysis method and system;The method comprises: obtaining water quality monitoring business data and association rule data, water quality monitoring business data is processed, and water quality data component is generated;According to component attribute data, generate front edge occlusion code and rear edge occlusion code, and generate association debt item set;When new water quality data component is written, the association debt item in the state to be paid is paid matching, and paid association record is generated;Association debt item that has not formed paid association record is not paid and is converted into certificate processing, and blank evidence record is generated, and water quality association analysis record is generated.The present application realizes the succession of water quality monitoring data storage, payment type association matching and the structure record of non-response relationship.

Description

Technical Field

[0001] This invention relates to the field of water quality monitoring data processing technology, specifically to a method and system for big data storage and correlation analysis of water quality monitoring. Background Technology

[0002] Water quality monitoring big data storage and correlation analysis mainly focuses on storing, retrieving, and analyzing continuous monitoring data generated in scenarios such as river sections, lake and reservoir areas, water intake and discharge outlets, sewage outlets, groundwater monitoring points, and water treatment facilities. In practical applications, water quality monitoring business data is typically characterized by a large number of monitoring sections, multiple types of monitoring indicators, continuous sampling time, frequent changes in equipment status, and data transmission relationships between upstream and downstream areas. The data processing system needs to uniformly organize water quality monitoring data from different monitoring sections, different monitoring indicators, and different sampling time ranges, and perform correlation analysis based on water flow, indicator changes, and time continuity to support water quality anomaly identification, pollution transmission judgment, and monitoring data traceability. In the process of big data storage and correlation analysis of water quality monitoring across cross sections, multiple indicators, and continuous sampling, there are still problems such as the same water quality disturbance being fragmented during the data storage stage, and subsequent responses that should have appeared but did not appear being difficult to retain in a structured manner. Specifically, on the one hand, water quality anomalies have transmission time differences between different monitoring sections and response time differences between different monitoring indicators. The same water quality disturbance is easily recorded as multiple discrete cross section data, indicator data, and time data, making it difficult for subsequent correlation analysis to determine which data belong to the same transmission process and which data are only data records that are close in time or spatially adjacent. On the other hand, when the upstream section or preceding indicator has already shown abnormal changes, but the downstream section or subsequent indicator has not shown a response within the corresponding time range, this non-response relationship is easily represented by no query results, null values, or missing measurement records in ordinary storage processing, making it difficult to participate in subsequent analysis as an unrecovered correlation result. This results in a lack of structured judgment basis for situations such as pollution transmission interruption, local anomaly occurrence, equipment status interference, or changes in the source of anomalies. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for big data storage and correlation analysis of water quality monitoring, so as to solve the problems mentioned in the background art above, where the same water quality disturbance is broken down in the data storage stage, and subsequent responses that should have appeared but did not appear are difficult to be structurally preserved.

[0004] To achieve the above objectives, the technical solution of the present invention is: a method for big data storage and correlation analysis of water quality monitoring, comprising: S1. Obtain water quality monitoring business data and association rule data, perform component processing on the water quality monitoring business data according to monitoring sections, monitoring indicators and continuous sampling intervals to generate water quality data components, and write the component attribute data of the water quality data components into the component storage unit. Among them, the association rule data is a set of data continuity constraints that limit the formation of associated debt items between water quality data components; the water quality data component is a data unit generated based on the same monitoring section, the same monitoring index, and the same continuous sampling interval; the component attribute data is attribute data that describes the data ownership, time boundary, state boundary, and index boundary of the water quality data component, including the component leading edge time and the component trailing edge time. S2. Read the water quality data components and their component attribute data, perform mortise and tenon interlocking structure construction processing on the water quality data components, generate leading edge interlocking code and trailing edge interlocking code by combining the component attribute data, bind the leading edge interlocking code and trailing edge interlocking code to the corresponding water quality data components and write them into the component index table, generate a set of related debt items based on the trailing edge interlocking code and association rule data, and configure debt status identifiers for all related debt items in the set of related debt items. Among them, the leading edge interlocking code is an access code generated based on the component's leading edge time and component attribute data; the trailing edge interlocking code is a derived code generated based on the component's trailing edge time and component attribute data; the associated debt item is a data unit that records the pending repayment relationship between a water quality data component and subsequent water quality data components, including the source water quality data component identifier, target attribute data, repayment time window, target leading edge interlocking code, and debt status identifier; the repayment time window includes the repayment start time and repayment end time; the debt status identifier includes pending repayment status, repaid status, and unpaid status. S3. When a new water quality data component is written into the component index table, the associated debt items with the debt status marked as pending repayment are retrieved based on the leading edge interlock code and component attribute data of the new water quality data component. When the new water quality data component meets the repayment matching conditions, the new water quality data component is written into the corresponding associated debt item and a repayment association record is generated. The debt status mark of the corresponding associated debt item is updated to the repaid status. Among them, the repayment matching condition is the criterion for determining whether a new water quality data component can repay the corresponding related debts; S4. Using the current processing time as the judgment time benchmark, perform unpaid transfer processing on related debt items whose debt status is marked as pending payment and whose current processing time is later than the payment end time, generate blank evidence records, update the debt status of the related debt item to the unpaid status, and generate water quality correlation analysis records based on the payment correlation records and blank evidence records. Among them, blank evidence records are data units that record the results of unpaid related debts that have not formed a repayment related record within the repayment time window.

[0005] Preferably, in step S1, the water quality monitoring operational data is a set of water quality monitoring data recorded according to monitoring sections, monitoring indicators, and sampling times. The water quality monitoring operational data includes monitoring value data, sampling time data, and status data. The association rule data also records the succession order between monitoring sections, the succession order between monitoring indicators, and the succession range between continuous sampling intervals. The association rule data includes section succession data, indicator succession data, and time succession data. Among them, section succession data represents the data succession order between different monitoring sections, indicator succession data represents the data succession order between different monitoring indicators, and time succession data represents the data succession range between continuous sampling intervals. The componentization processing is a processing method that divides the water quality monitoring operational data into water quality data components based on monitoring sections, monitoring indicators, and continuous sampling intervals.

[0006] Preferably, in step S1, the water quality data component is a data structure formed by sequentially associating a component head, a component body, and a component tail. The component head records the data ownership content of the water quality data component, the component body records the monitoring value data and status data within the continuous sampling interval, and the component tail records the time boundary content and indicator boundary content of the water quality data component. The component leading edge time is the start boundary time of the continuous sampling interval corresponding to the water quality data component, and the component trailing edge time is the end boundary time of the continuous sampling interval corresponding to the water quality data component. The component attribute data is attribute data bound to the component identifier of the water quality data component. The component attribute data also includes the component identifier, monitoring section identifier, monitoring indicator identifier, status code, and indicator boundary code. The status code is the code for the status boundary corresponding to the water quality data component, and the indicator boundary code is the code for the indicator boundary corresponding to the water quality data component. The component storage unit is a storage structure used to record the correspondence between the water quality data component and its component attribute data. The component storage unit includes a component recording area and an attribute recording area. The component recording area is used to record the water quality data component, and the attribute recording area is used to record the component attribute data corresponding to the water quality data component.

[0007] Preferably, in S2, the mortise and tenon interlocking structure construction process is a method of encoding the leading and trailing boundaries of the water quality data component and establishing boundary continuity relationships; the leading boundary of the water quality data component is configured as the access side boundary, and the trailing boundary of the water quality data component is configured as the derived side boundary; the leading edge interlocking code is a data code representing the access side boundary, used for repayment matching with the target leading edge interlocking code in the associated debt item; the trailing edge interlocking code is a data code representing the derived side boundary, used for generating associated debt items in combination with the association rule data; the component index table is an index data structure that records the binding relationship between the water quality data component, the leading edge interlocking code, and the trailing edge interlocking code, used for retrieving the corresponding leading edge interlocking code and trailing edge interlocking code according to the component identifier of the water quality data component.

[0008] Preferably, in S2, the leading edge biting code includes a component identifier, a monitoring section identifier, a monitoring index identifier, a component leading edge time, a status code, and an index boundary code; the trailing edge biting code includes a component identifier, a monitoring section identifier, a monitoring index identifier, a component trailing edge time, a status code, and an index boundary code; the same water quality data component corresponds to one leading edge biting code and one trailing edge biting code; the method rule for binding the leading edge biting code and the trailing edge biting code with the corresponding water quality data component is as follows: using the component identifier of the water quality data component as the binding index identifier, configuring the leading edge biting code as the access-side index item corresponding to the component identifier, configuring the trailing edge biting code as the derived-side index item corresponding to the component identifier, and forming a binding index record between the component identifier, the leading edge biting code, and the trailing edge biting code in the component index table.

[0009] Preferably, in step S2, the associated debt item set is a data set used to aggregate multiple associated debt items formed by trailing edge interlocking codes and association rule data; the associated debt items in the associated debt item set are jointly defined by trailing edge interlocking codes and association rule data; the source water quality data component identifier is determined based on the trailing edge interlocking code, the target attribute data is determined based on the association rule data, the repayment time window is determined based on the component trailing edge time of the source water quality data component and the association rule data, the target leading edge interlocking code is generated based on the target attribute data and the repayment time window, and the source water quality data component identifier, target attribute data, repayment time window, target leading edge interlocking code, and debt status identifier are combined into an associated debt item; target The attribute data includes the target monitoring section identifier and the target monitoring indicator identifier; the repayment time window uses the component trailing edge time of the source water quality data component as the time reference, and includes the repayment start time and repayment end time; the target leading edge interlock code is the expected access code for subsequent water quality data components to perform repayment matching; the debt status identifier is the status identifier that records the repayment process of related debt items. When a related debt item is generated, the debt status identifier is configured as a pending repayment status. The pending repayment status indicates that the related debt item has not yet generated a repayment association record, the repaid status indicates that the related debt item has generated a repayment association record, and the unpaid status indicates that the related debt item has been converted into a blank evidence record.

[0010] Preferably, in step S3, the new water quality data component is a water quality data component that is written into the component index table after the associated debt item set is generated and participates in the repayment matching of associated debt items in the pending repayment status; the repayment matching conditions include target attribute data matching conditions, repayment time window matching conditions, and target leading edge biting code matching conditions; when the component attribute data of the new water quality data component corresponds to the target attribute data in the associated debt item, the component leading edge time of the new water quality data component is not earlier than the repayment start time and not later than the repayment end time, and the leading edge biting code of the new water quality data component corresponds to the target leading edge biting code in the associated debt item, the new water quality data component is determined as a repayment water quality data component, and a repayment association record is generated based on the repayment water quality data component and the corresponding associated debt item; wherein, the repayment association record is a data record that records the repayment binding relationship between the associated debt item and the repayment water quality data component, including the source water quality data component identifier, the repayment water quality data component identifier, the target attribute data, the repayment time window, the target leading edge biting code, and the repaid status identifier.

[0011] Preferably, in step S3, when a new water quality data component corresponds to multiple associated debt items that meet the repayment matching conditions, a single repayment determination process is performed on the multiple associated debt items that meet the repayment matching conditions. The single repayment determination process includes: calculating the time interval between the component leading edge time of the new water quality data component and the repayment start time of each associated debt item, and determining the associated debt item with the smallest time interval value as the repayment object; when there are multiple associated debt items with the same time interval value, determining the associated debt item with the latest component trailing edge time of the source water quality data component as the repayment object; when there are multiple associated debt items with the same component trailing edge time, determining the repayment object according to the writing order of the associated debt items in the associated debt item set; generating a repayment association record based on the repayment object, and updating the debt status identifier of the repayment object to the repaid status, while maintaining the debt status identifier of the associated debt items that are not repayment objects as the repayment status.

[0012] Preferably, in S4, the outstanding debt transfer processing is a method of converting related debt items with a debt status marked as pending repayment and a current processing time later than the repayment end time into blank evidence records; related debt items with a current processing time later than the repayment end time are related debt items for which no repayment association record has been formed within the repayment time window; blank evidence records are data records that record the outstanding debt association results for which no repayment association record has been formed, and blank evidence records include source water quality data component identifiers, target attribute data, repayment time window, target leading edge interlocking code, current processing time, and outstanding debt items. The repayment status identifier; the source water quality data component identifier, target attribute data, repayment time window, and target leading edge interlocking code in the blank evidence record are respectively written by the source water quality data component identifier, target attribute data, repayment time window, and target leading edge interlocking code in the corresponding related debt item, forming an association pointing relationship between the blank evidence record and the corresponding related debt item; after the non-repayable transfer certificate processing is completed, the debt status identifier of the corresponding related debt item is updated to non-repayable status; the water quality association analysis record includes the repaid association results corresponding to the repayment association record and the non-repayable association results corresponding to the blank evidence record.

[0013] On the other hand, the present invention provides a water quality monitoring big data storage and correlation analysis system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the above-described water quality monitoring big data storage and correlation analysis method.

[0014] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial technical effects: 1. In this invention, based on water quality data components, leading edge biting codes, trailing edge biting codes, and a set of associated debt items, discrete water quality data from different monitoring sections, different monitoring indicators, and different sampling intervals can be organized into data units with sequential relationships, so that the water quality data written subsequently can complete the association confirmation according to the biting codes and payment matching conditions, reducing the problem of the same water quality disturbance being broken up and stored. 2. In this invention, through the processing of outstanding debt transfer certificates and the recording of blank evidence, the structured preservation of related debt items that have not formed a repayment association record is realized, so that the non-response relationship can be used as the result of outstanding debt association to participate in water quality association analysis, and provide data basis for judging the interruption of pollution transmission, the occurrence of local anomalies or equipment status interference. Attached Figure Description

[0015] Figure 1 This is a flowchart of one embodiment of the present invention. Detailed Implementation

[0016] Example 1, as Figure 1 As shown, the present invention proposes a method for big data storage and correlation analysis of water quality monitoring, the specific implementation steps of which are as follows: S1. Obtain water quality monitoring business data and association rule data, perform component processing on the water quality monitoring business data according to monitoring sections, monitoring indicators and continuous sampling intervals to generate water quality data components, and write the component attribute data of the water quality data components into the component storage unit. Among them, the association rule data is a set of data continuity constraints that limit the formation of associated debt items between water quality data components; the water quality data component is a data unit generated based on the same monitoring section, the same monitoring index, and the same continuous sampling interval; the component attribute data is attribute data that describes the data ownership, time boundary, state boundary, and index boundary of the water quality data component, including the component leading edge time and the component trailing edge time. S2. Read the water quality data components and their component attribute data, perform mortise and tenon interlocking structure construction processing on the water quality data components, generate leading edge interlocking code and trailing edge interlocking code by combining the component attribute data, bind the leading edge interlocking code and trailing edge interlocking code to the corresponding water quality data components and write them into the component index table, generate a set of related debt items based on the trailing edge interlocking code and association rule data, and configure debt status identifiers for all related debt items in the set of related debt items. Among them, the leading edge interlocking code is an access code generated based on the component's leading edge time and component attribute data; the trailing edge interlocking code is a derived code generated based on the component's trailing edge time and component attribute data; the associated debt item is a data unit that records the pending repayment relationship between a water quality data component and subsequent water quality data components, including the source water quality data component identifier, target attribute data, repayment time window, target leading edge interlocking code, and debt status identifier; the repayment time window includes the repayment start time and repayment end time; the debt status identifier includes pending repayment status, repaid status, and unpaid status. S3. When a new water quality data component is written into the component index table, the associated debt items with the debt status marked as pending repayment are retrieved based on the leading edge interlock code and component attribute data of the new water quality data component. When the new water quality data component meets the repayment matching conditions, the new water quality data component is written into the corresponding associated debt item and a repayment association record is generated. The debt status mark of the corresponding associated debt item is updated to the repaid status. Among them, the repayment matching condition is the criterion for determining whether a new water quality data component can repay the corresponding related debts; S4. Using the current processing time as the judgment time benchmark, perform unpaid transfer processing on related debt items whose debt status is marked as pending payment and whose current processing time is later than the payment end time, generate blank evidence records, update the debt status of the related debt item to the unpaid status, and generate water quality correlation analysis records based on the payment correlation records and blank evidence records. Among them, blank evidence records are data units that record the results of unpaid related debts that have not formed a repayment related record within the repayment time window.

[0017] In this embodiment S1, the water quality monitoring business data is a set of water quality monitoring data recorded according to monitoring sections, monitoring indicators, and sampling times. The water quality monitoring business data includes monitoring value data, sampling time data, and status data. The association rule data also records the succession order between monitoring sections, the succession order between monitoring indicators, and the succession range between continuous sampling intervals. The association rule data includes section succession data, indicator succession data, and time succession data. Among them, section succession data represents the data succession order between different monitoring sections, indicator succession data represents the data succession order between different monitoring indicators, and time succession data represents the data succession range between continuous sampling intervals. The componentization processing is a processing method that divides the water quality monitoring business data into water quality data components based on monitoring sections, monitoring indicators, and continuous sampling intervals. The monitoring section is the monitoring location identifier corresponding to the water quality monitoring business data, the monitoring indicator is the water quality parameter identifier corresponding to the water quality monitoring business data, and the continuous sampling interval is a data interval with continuous sampling time under the same monitoring section and the same monitoring indicator.

[0018] In this embodiment S1, water quality monitoring operational data is recorded according to monitoring sections, monitoring indicators, and sampling time because water quality monitoring data needs to simultaneously possess three basic dimensions—location, indicator, and time—when entering the storage and correlation analysis process. The monitoring section is used to identify the collection location corresponding to the water quality monitoring operational data, including river sections, lake / reservoir monitoring points, water intake monitoring points, sewage outlet monitoring points, groundwater monitoring points, or water treatment facility monitoring nodes. The monitoring indicator is used to identify the type of water quality parameter corresponding to the water quality monitoring operational data, including pH value, dissolved oxygen, turbidity, conductivity, ammonia nitrogen, total phosphorus, total nitrogen, chemical oxygen demand, and other water quality parameters. The sampling time is used to identify the same monitoring section and the same monitoring indicator. Mark the time and location of each monitoring value data point; the monitoring value data is the numerical or graded test result corresponding to the monitoring indicator; the sampling time data is the timestamp, sampling batch time, or monitoring cycle time recorded synchronously with the monitoring value data; the status data is used to describe the status of the monitoring value data in terms of acquisition, transmission, calibration, missing measurements, supplementary recording, equipment operation, and data validity; when performing data access, each water quality monitoring business data is organized into a data record containing monitoring section identifier, monitoring indicator identifier, sampling time, monitoring value data, and status data, and data records with the same monitoring section identifier, the same monitoring indicator identifier, and a continuous relationship in sampling time are used as candidate data groups for subsequent componentization processing.

[0019] In this embodiment S1, the association rule data serves as a set of data continuity constraints that define the associated debt items between water quality data components. It is organized according to cross-sectional continuity data, indicator continuity data, and time continuity data. Cross-sectional continuity data records the data continuity order between different monitoring cross-sections, including the source monitoring cross-section identifier, target monitoring cross-section identifier, cross-sectional continuity direction, and cross-sectional continuity level. In river monitoring scenarios, cross-sectional continuity data represents the data continuity order between upstream, midstream, and downstream cross-sections. In lake / reservoir or water treatment facility scenarios, cross-sectional continuity data represents the data continuity between inlet monitoring points, process monitoring points, and outlet monitoring points. Sequence; indicator continuity data is used to record the data continuity sequence between different monitoring indicators, and its content includes source monitoring indicator identifier, target monitoring indicator identifier, and indicator continuity category; time continuity data is used to record the data continuity range between continuous sampling intervals, and its content includes the end time reference of the source continuous sampling interval, the start time reference of the target continuous sampling interval, the minimum continuity interval, and the maximum continuity interval; after subsequent componentization processing, cross-sectional continuity data is used to define the cross-sectional continuity basis between water quality data components, indicator continuity data is used to define the indicator continuity basis between water quality data components, and time continuity data is used to define the time continuity range between water quality data components.

[0020] In this embodiment S1, componentization processing is performed based on monitoring sections, monitoring indicators, and continuous sampling intervals as grouping criteria. First, the monitoring section identifiers in the water quality monitoring business data are read, and data records with the same monitoring section identifier are grouped into the same section data group. Then, within the same section data group, the monitoring indicator identifiers are read, and data records with the same monitoring indicator identifiers are grouped into the same indicator data group. Subsequently, within the same indicator data group, data records are arranged from first to last according to sampling time, and the continuity of the sampling time series is judged based on the sampling period, allowable time interval, status data, and data breakpoints. When the sampling time interval of two adjacent data records... When the data is within the allowable time interval and the status data meets the criteria for belonging to the same continuous sampling interval, the two adjacent data records are assigned to the same continuous sampling interval. When the sampling time interval exceeds the allowable time interval, the status data switches between intervals, or the data record forms a missing measurement breakpoint, the current position is used as the interval boundary of the continuous sampling interval. Finally, the data records of the same monitoring section, the same monitoring index, and the same continuous sampling interval are encapsulated into a water quality data component, and a component identifier is configured for the water quality data component, so that the original water quality monitoring business data is converted from a single discrete record into a data unit with section affiliation, index affiliation, and time boundary.

[0021] In this embodiment S1, the water quality data component is a data structure formed by sequentially associating a component head, a component body, and a component tail. The component head records the data ownership content of the water quality data component, the component body records the monitoring value data and status data within the continuous sampling interval, and the component tail records the time boundary content and index boundary content of the water quality data component. The component leading edge time is the start boundary time of the continuous sampling interval corresponding to the water quality data component, and the component trailing edge time is the end boundary time of the continuous sampling interval corresponding to the water quality data component. The component attribute data is attribute data bound to the component identifier of the water quality data component. The component attribute data also includes the component identifier, monitoring section identifier, monitoring index identifier, status code, and index boundary code. The status code is the code for the status boundary corresponding to the water quality data component, and the index boundary code is the code for the index boundary corresponding to the water quality data component. The component storage unit is a storage structure used to record the correspondence between the water quality data component and its component attribute data. The component storage unit includes a component recording area and an attribute recording area. The component recording area is used to record the water quality data component, and the attribute recording area is used to record the component attribute data corresponding to the water quality data component.

[0022] In this embodiment S1, the water quality data component adopts a data structure formed by sequentially associating a component head, a component body, and a component tail. The component head is used to record the data ownership content of the water quality data component, including component identifier, monitoring section identifier, monitoring index identifier, and continuous sampling interval identifier. The component body is used to record the monitoring value data and status data within the continuous sampling interval, and its content stores multiple monitoring records in the order of sampling time. Each monitoring record includes sampling time, monitoring value data, and status data. The component tail is used to record the time boundary content and index boundary content of the water quality data component, including the component leading edge time, the component trailing edge time, and the status code. The code and indicator boundary encoding are used; the component head, component body, and component tail establish a sequential association relationship through the same component identifier. The component head provides the component belonging index, the component body provides the business data content within the continuous sampling interval, and the component tail provides the boundary data required for subsequent generation of leading edge and trailing edge bite codes; the component leading edge time is the sampling time corresponding to the first monitoring record within the continuous sampling interval, and the component trailing edge time is the sampling time corresponding to the last monitoring record within the continuous sampling interval; through the above data structure, the water quality data component can integrate data from the same monitoring section, the same monitoring indicator, and the same continuous sampling interval into a single data unit for subsequent processing.

[0023] In this embodiment S1, component attribute data is bound to the component identifier of the water quality data component, used to describe the data ownership, time boundary, state boundary, and index boundary of the water quality data component; the component attribute data includes component identifier, monitoring section identifier, monitoring index identifier, component leading edge time, component trailing edge time, state code, and index boundary code; wherein, the component identifier is used to establish the correspondence between the water quality data component, component attribute data, component index table, and subsequent associated debt items; the monitoring section identifier is used to indicate the monitoring location to which the water quality data component belongs; the monitoring index identifier is used to indicate the water quality parameter type to which the water quality data component belongs; The leading edge time and trailing edge time of the component are used to determine the time boundary of the water quality data component; the status code is formed by merging the status data within the water quality data component and is used to represent the data status boundary within the corresponding continuous sampling interval of the water quality data component. The status code can record the effective status of the equipment, calibration status, communication status, missing measurement status, supplementary recording status, or abnormal marking status; the index boundary code is jointly determined by the monitoring index identifier and the boundary status of the monitoring index at the component boundary and is used to represent the index boundary corresponding to the water quality data component; after the component attribute data is formed, it serves together with the water quality data component as the data basis for subsequent mortise and tenon interlocking structure construction processing.

[0024] In this embodiment S1, when writing water quality data components and their attribute data into the component storage unit, a component identifier is first assigned to the water quality data component, and the component head, component body, and component tail of the water quality data component are written into the component record area of ​​the component storage unit; then, using the same component identifier as the association identifier, the component attribute data is written into the attribute record area of ​​the component storage unit; the component identifier in the component record area corresponds to the component identifier in the attribute record area, so that the component storage unit can read the water quality data component and its attribute data according to the component identifier; during the writing process, the component leading edge time, component trailing edge time, status code, and indicators are recorded. Boundary coding is used for integrity verification. The verification content includes whether the leading edge time of the component is earlier than or equal to the trailing edge time of the component, whether the monitoring section identifier is consistent with the data ownership of the water quality data component, whether the monitoring indicator identifier is consistent with the data ownership of the water quality data component, whether the status code corresponds to the status data in the main body of the component, and whether the indicator boundary code corresponds to the indicator boundary content in the tail of the component. After the verification is completed, the component storage unit forms a logical storage structure composed of the component record area and the attribute record area, which provides a data source for subsequent reading of the water quality data component and its component attribute data from the component storage unit and generating the leading edge interlocking code and the trailing edge interlocking code.

[0025] In this embodiment S2, the mortise and tenon interlocking structure construction process is a method of encoding the leading and trailing boundaries of the water quality data component and establishing boundary continuity relationships. The leading boundary of the water quality data component is configured as the access side boundary, and the trailing boundary of the water quality data component is configured as the derived side boundary. The leading edge interlocking code is a data code representing the access side boundary, used for repayment matching with the target leading edge interlocking code in the associated debt item. The trailing edge interlocking code is a data code representing the derived side boundary, used for generating associated debt items in combination with the association rule data. The component index table is an index data structure that records the binding relationship between the water quality data component, the leading edge interlocking code, and the trailing edge interlocking code, used to retrieve the corresponding leading edge interlocking code and trailing edge interlocking code according to the component identifier of the water quality data component.

[0026] In this embodiment S2, the mortise and tenon interlocking structure construction process uses the water quality data components and component attribute data generated in S1 as the processing objects. Boundary identification, boundary coding, and connection relationship registration are performed on the leading and trailing boundaries of the water quality data components, respectively. Specifically, the water quality data components and their component attribute data are read from the component storage unit. According to the component identifier, the corresponding monitoring section identifier, monitoring index identifier, leading edge time, trailing edge time, status code, and index boundary code are obtained. The data boundary where the leading edge time of the component is located is configured as the leading edge boundary of the water quality data component, and the data boundary where the trailing edge time of the component is located is configured as the trailing edge boundary of the water quality data component. Boundaries; After the boundary configuration is completed, the leading edge boundary is used as the access side boundary, and the trailing edge boundary is used as the derived side boundary. The access side boundary is used to represent the data entry conditions that the water quality data component needs to meet when it can be matched for repayment by the preceding associated debt item, and the derived side boundary is used to represent the data exit conditions provided when the water quality data component can form a repayment association with subsequent water quality data components. Through the above processing, the water quality data component is transformed from a simple continuous sampling data unit into a connectable data component with both access side boundary and derived side boundary, providing a boundary basis for the generation of subsequent leading edge biting codes, trailing edge biting codes, and associated debt item sets.

[0027] In this embodiment S2, both the leading edge biting code and the trailing edge biting code are data codes established around the boundary continuation relationship of water quality data components. The leading edge biting code is generated based on the access side boundary of the water quality data component and is used to characterize the entry conditions that the water quality data component can be identified during subsequent payment matching. The trailing edge biting code is generated based on the derived side boundary of the water quality data component and is used to characterize the exit conditions that the water quality data component can provide when forming associated debt items with subsequent water quality data components. When the leading edge biting code is generated, it reads the leading edge time of the component and the fields related to the leading edge boundary in the component attribute data, so that the code can reflect the cross-section assignment, index assignment, leading edge time boundary, state boundary, and index of the water quality data component. The boundary is marked; the trailing edge bite code reads the trailing edge time and fields related to the trailing edge boundary in the component attribute data during generation, so that the code can reflect the cross-sectional affiliation, index affiliation, trailing edge time boundary, state boundary and index boundary of the water quality data component; when performing repayment matching, the leading edge bite code of the new water quality data component is matched with the target leading edge bite code in the associated debt item; when performing associated debt item generation, the trailing edge bite code of the source water quality data component and the association rule data are used together to determine the subsequent repayment association relationship; by setting the leading edge bite code and trailing edge bite code respectively, the same water quality data component can have both access recognition capability and derivation recognition capability in the data continuation process.

[0028] In this embodiment S2, the component index table serves as an index data structure recording the binding relationships between water quality data components, leading edge bite codes, and trailing edge bite codes. Its establishment process includes reading the component identifier of the water quality data component, using this component identifier as the index positioning basis, registering the leading edge bite code corresponding to the component identifier as an access-side index entry, registering the trailing edge bite code corresponding to the component identifier as a derived-side index entry, and forming binding index records between the component identifier, leading edge bite code, and trailing edge bite code in the component index table. Each binding index record in the component index table corresponds to a water quality data component. The water quality data component enables the system to read the leading and trailing edge engagement codes of the water quality data component based on its component identifier during subsequent processing. It can also locate water quality data components that can participate in payment matching based on the leading edge engagement code, or locate water quality data components that can generate associated debt items based on the trailing edge engagement code. The component index table is not limited to a specific physical database table and can be set as a logical index structure in the data storage system, object storage system, or data processing cache structure. Through the setting of the component index table, a searchable index relationship is formed between the water quality data component and its boundary code.

[0029] In this embodiment S2, the leading edge biting code includes a component identifier, a monitoring section identifier, a monitoring index identifier, a component leading edge time, a status code, and an index boundary code; the trailing edge biting code includes a component identifier, a monitoring section identifier, a monitoring index identifier, a component trailing edge time, a status code, and an index boundary code; the same water quality data component corresponds to one leading edge biting code and one trailing edge biting code; the method rule for binding the leading edge biting code and the trailing edge biting code with the corresponding water quality data component is as follows: the component identifier of the water quality data component is used as the binding index identifier, the leading edge biting code is configured as the access-side index item corresponding to the component identifier, the trailing edge biting code is configured as the derived-side index item corresponding to the component identifier, and a binding index record between the component identifier, the leading edge biting code, and the trailing edge biting code is formed in the component index table.

[0030] In this embodiment S2, both the leading edge biting code and the trailing edge biting code use component identifier, monitoring section identifier, monitoring index identifier, status code, and index boundary code as common coding fields. The component identifier points to the corresponding water quality data component, the monitoring section identifier indicates the monitoring location of the water quality data component, the monitoring index identifier indicates the water quality parameter of the water quality data component, the status code indicates the data status boundary of the water quality data component at the corresponding boundary, and the index boundary code indicates the index boundary of the water quality data component at the corresponding boundary. When generating the leading edge biting code, the component leading edge time is used as the time boundary field and combined with the above common coding fields to form an access code. This access code is used to determine the correspondence between the new water quality data component and the target leading edge biting code in the associated debt item after the new water quality data component is written into the component index table, enabling the new water quality data component to participate in repayment matching based on the access side boundary.

[0031] In this embodiment S2, when generating the trailing edge biting code, the component trailing edge time is used as a time boundary field, and combined with the component identifier, monitoring section identifier, monitoring indicator identifier, status code, and indicator boundary code to form a derived code. This derived code is used to determine the target attribute data, repayment time window, and target leading edge biting code that the source water quality data component can point to, in conjunction with the association rule data, so that the source water quality data component can generate associated debt items based on the derived side boundary. Among them, the leading edge biting code corresponds to the access side boundary of the water quality data component, and the trailing edge biting code corresponds to the derived side boundary of the water quality data component. The two share the component ownership, section ownership, indicator ownership, status boundary, and indicator boundary fields in the coding structure, and distinguish their boundary direction and subsequent processing purpose by the component leading edge time and component trailing edge time.

[0032] In this embodiment S2, each water quality data component corresponds to a leading edge biting code and a trailing edge biting code. The leading edge biting code corresponds to the access-side boundary of the water quality data component, and the trailing edge biting code corresponds to the derived-side boundary of the water quality data component. When binding the leading edge biting code, trailing edge biting code, and water quality data component, the component identifier of the water quality data component is used as the binding index identifier. An access-side index entry and a derived-side index entry corresponding to the component identifier are established in the component index table. The access-side index entry records the leading edge biting code, and the derived-side index entry records the trailing edge biting code. The binding index record includes at least the component identifier, the leading edge biting code, and the trailing edge biting code. And index write time; when there is an update of component attribute data under the same component identifier, the component index table locates the original bound index record according to the component identifier, and regenerates the corresponding leading edge bit or trailing edge bit according to the updated component attribute data; during the index reading process, the access side index item and the derived side index item can be read simultaneously according to the component identifier, the corresponding water quality data component can be located according to the leading edge bit, and the source water quality data component that can generate associated debt items can be located according to the trailing edge bit; through the above binding rules, the component index table forms a unified index relationship between water quality data components, leading edge bit, and trailing edge bit.

[0033] In this embodiment S2, the associated debt item set is a data set used to collect multiple associated debt items formed by trailing edge interlocking codes and association rule data. This set stores associated debt items in pending, paid, and unpaid states, and serves as the retrieval object for subsequent payment matching and unpaid certificate transfer processing. The associated debt items in the associated debt item set are jointly defined by trailing edge interlocking codes and association rule data. The source water quality data component identifier is determined based on the trailing edge interlocking code; the target attribute data is determined based on the association rule data; the payment time window is determined based on the component trailing edge time of the source water quality data component and the association rule data; the target leading edge interlocking code is generated based on the target attribute data and the payment time window; and the source water quality data component identifier, target attribute data, and payment time window are then combined. The payment time window, target leading edge interlock code, and debt status identifier are combined to form a related debt item; target attribute data includes target monitoring section identifier and target monitoring indicator identifier; the payment time window uses the component trailing edge time of the source water quality data component as the time reference and includes the payment start time and payment end time; the target leading edge interlock code is the expected access code for subsequent water quality data components to perform payment matching; the debt status identifier is a status identifier that records the payment process of the related debt item. When the related debt item is generated, the debt status identifier is configured as pending payment. The pending payment status indicates that the related debt item has not yet generated a payment association record, the paid status indicates that the related debt item has generated a payment association record, and the unpaid status indicates that the related debt item has been converted into a blank evidence record.

[0034] In this embodiment S2, when generating the set of associated debt items, the registered trailing edge interlocking codes in the component index table are first read, and the component identifier of the source water quality data is determined according to the component identifier in the trailing edge interlocking code; then, the cross-section continuity data, indicator continuity data, and time continuity data in the association rule data are read, and the monitoring cross-section identifier in the trailing edge interlocking code is matched with the cross-section continuity data to determine the target monitoring cross-section identifier; the monitoring indicator identifier in the trailing edge interlocking code is matched with the indicator continuity data to determine the target monitoring indicator identifier; the target monitoring cross-section identifier and the target monitoring indicator identifier together constitute the target attribute data; Using the trailing edge time of the source water quality data component as the time reference, the repayment start time and repayment end time are determined based on the time continuity data, and the repayment start time and repayment end time form a repayment time window; after the target attribute data and repayment time window are determined, a target leading edge biting code is generated, which serves as the expected access code when subsequent water quality data components perform repayment matching; finally, the source water quality data component identifier, target attribute data, repayment time window, target leading edge biting code, and debt status identifier are combined into a related debt item, and one or more related debt items are aggregated to form a related debt item set.

[0035] In this embodiment S2, the target attribute data includes target monitoring section identifiers and target monitoring index identifiers. The target monitoring section identifier indicates the subsequent monitoring location that can be continued for the source water quality data component, and the target monitoring index identifier indicates the subsequent water quality parameters that can be continued for the source water quality data component. The target attribute data is determined by association rule data and forms a corresponding relationship with the trailing edge interlocking code of the source water quality data component. The compensation time window uses the trailing edge time of the source water quality data component as the time reference. The minimum interlocking interval recorded in the time continuity data is used to determine the compensation start time, and the maximum interlocking interval recorded in the time continuity data is used to determine the compensation end time. When the trailing edge time of the source water quality data component... Once determined, the component trailing edge time is mapped to the minimum continuation interval to form the repayment start time, and the component trailing edge time is mapped to the maximum continuation interval to form the repayment end time. The repayment time window is used to limit the time range within which subsequent water quality data components can repay the corresponding associated debt items. The target leading edge biting code is formed based on the target attribute data, the repayment time window, and the target access requirements of the associated debt items, and is used to represent the access-side coding conditions that subsequent water quality data components need to meet when performing repayment matching. Through the combination of target attribute data, repayment time window, and target leading edge biting code, the associated debt items can simultaneously limit the target location, target indicators, target time range, and target access boundary of subsequent water quality data components.

[0036] In this embodiment S2, the debt status identifier is a status identifier that records the repayment progress of related debt items. The debt status identifier includes a pending repayment status, a repaid status, and an outstanding repayment status. When a related debt item is generated, the debt status identifier is configured to a pending repayment status, indicating that the related debt item has been generated and is waiting for subsequent new water quality data components to perform repayment matching. When a subsequent new water quality data component meets the repayment matching conditions of the corresponding related debt item and generates a repayment association record, the debt status identifier of the related debt item is updated to a repaid status. When the current processing time is later than the repayment settlement time... If the deadline for the related debt item is approaching and no repayment record has been generated, the related debt item is converted into a blank evidence record, and the debt status identifier is updated to an unpaid status. In the set of related debt items, related debt items in the pending repayment status are used as the search objects for repayment matching in S3, related debt items in the repaid status are used for the corresponding repayment record, and related debt items in the unpaid status are used for the corresponding blank evidence record. Through the configuration of the debt status identifier, the set of related debt items can record the status change process of each related debt item from generation, repayment matching to unpaid evidence conversion.

[0037] In this embodiment S3, the new water quality data component is the water quality data component that is written into the component index table after the associated debt item set is generated and participates in the repayment matching of associated debt items in the pending repayment status. The repayment matching conditions include target attribute data matching conditions, repayment time window matching conditions, and target leading edge biting code matching conditions. When the component attribute data of the new water quality data component corresponds to the target attribute data in the associated debt item, the component leading edge time of the new water quality data component is not earlier than the repayment start time and not later than the repayment end time, and the leading edge biting code of the new water quality data component corresponds to the target leading edge biting code in the associated debt item, the new water quality data component is determined as the repayment water quality data component, and a repayment association record is generated based on the repayment water quality data component and the corresponding associated debt item. The repayment association record is a data record that records the repayment binding relationship between the associated debt item and the repayment water quality data component, including the source water quality data component identifier, the repayment water quality data component identifier, the target attribute data, the repayment time window, the target leading edge biting code, and the repaid status identifier.

[0038] In this embodiment S3, a new water quality data component refers to a water quality data component that, after the associated debt item set has been generated, is written back into the component index table and participates in the repayment matching of associated debt items in the pending repayment state. When a new water quality data component enters the component index table, the component attribute data corresponding to the new water quality data component is first read according to the component identifier, and then the leading edge engagement code bound to the new water quality data component is read. The leading edge engagement code, monitoring section identifier, monitoring indicator identifier, component leading edge time, status code, and indicator boundary code are used as the data input for repayment matching. Only associated debt items in the associated debt item set with the debt status identifier of pending repayment participate in this repayment matching, while those with the debt status identifier of already repaid participate in the repayment matching. Data relationships with existing repayment records are considered as related debt items. Data relationships with outstanding debt status are considered as related debt items with completed outstanding certificate transfer processes and are therefore no longer considered as repayment matching objects for new water quality data components. When searching for related debt items in the pending repayment status, the component attribute data of the new water quality data component is used as the search entry point. Its monitoring section identifier and monitoring indicator identifier are matched with the target attribute data in the related debt item, its component leading edge time is matched with the repayment time window in the related debt item, and its leading edge biting code is matched with the target leading edge biting code in the related debt item. This forms the initial search result for whether the new water quality data component has a repayment basis.

[0039] In this embodiment S3, the repayment matching conditions include target attribute data matching conditions, repayment time window matching conditions, and target leading edge biting code matching conditions. The target attribute data matching conditions are used to determine whether the component attribute data of the new water quality data component corresponds to the target attribute data in the associated debt item. Specifically, this includes the monitoring section identifier of the new water quality data component corresponding to the target monitoring section identifier, and the monitoring indicator identifier of the new water quality data component corresponding to the target monitoring indicator identifier. The repayment time window matching conditions are used to determine whether the component leading edge time of the new water quality data component falls within the repayment time window of the associated debt item. Specifically, the component leading edge time of the new water quality data component is not earlier than the repayment start time and not later than the repayment end time. The target leading edge biting code matching conditions... The matching conditions are used to determine whether the leading edge biting code of a new water quality data component corresponds to the target leading edge biting code in the associated debt item. The correspondence between the leading edge biting code and the target leading edge biting code includes at least the correspondence of the access side boundary, the correspondence of the monitoring section affiliation, the correspondence of the monitoring indicator affiliation, the correspondence of the state boundary, and the correspondence of the indicator boundary. When the above target attribute data matching conditions, repayment time window matching conditions, and target leading edge biting code matching conditions are all met, the new water quality data component is determined as the repayment water quality data component of the corresponding associated debt item. The repayment water quality data component is not a new data type, but a repayment role assigned to the new water quality data component after the repayment matching conditions are met, which is used to indicate that the new water quality data component has completed the repayment matching of the corresponding associated debt item.

[0040] In this embodiment S3, the repayment association record is used to record the repayment binding relationship between the associated debt item and the repayment water quality data component. When generating the repayment association record, the source water quality data component identifier, target attribute data, repayment time window, and target leading edge engagement code are first read from the corresponding associated debt item. Then, the repayment water quality data component identifier, leading edge engagement code, monitoring section identifier, monitoring indicator identifier, and component leading edge time are read from the repayment water quality data component. The above data is then written into the same repayment association record. The repayment association record includes at least the source water quality data component identifier, the repayment water quality data component identifier, target attribute data, repayment time window, target leading edge engagement code, and repayment status identifier. The source water quality data component identifier is used... The water quality data component that generates the associated debt item is used to point to the new water quality data component that completes the repayment matching. The target attribute data is used to record the target section and target index of this repayment matching. The repayment time window is used to record the time range corresponding to this repayment matching. The target leading edge interlock code is used to record the expected access code on which this repayment matching is based. The repaid status identifier is used to record that the repayment binding relationship has been formed. After the repayment association record is generated, the debt status identifier of the corresponding associated debt item is updated from the pending repayment status to the repaid status, and the status update result is retained in the associated debt item set so that the subsequent unpaid transfer certificate processing will no longer generate blank evidence records for the associated debt item.

[0041] In this embodiment S3, when a new water quality data component corresponds to multiple associated debt items that meet the repayment matching conditions, a single repayment determination process is performed on the multiple associated debt items that meet the repayment matching conditions. The single repayment determination process includes: calculating the time interval between the component leading edge time of the new water quality data component and the repayment start time of each associated debt item, and determining the associated debt item with the smallest time interval value as the repayment object; when there are multiple associated debt items with the same time interval value, determining the associated debt item with the latest component trailing edge time of the source water quality data component as the repayment object; when there are multiple associated debt items with the same component trailing edge time, determining the repayment object according to the writing order of the associated debt items in the associated debt item set; generating a repayment association record based on the repayment object, and updating the debt status identifier of the repayment object to the repaid status, while maintaining the debt status identifier of the associated debt items that are not repayment objects as the repayment status.

[0042] In this embodiment S3, when a new water quality data component corresponds to multiple associated debt items that meet the repayment matching conditions, it means that the component attribute data, component leading time, and leading edge engagement code of the new water quality data component simultaneously meet the target attribute data, repayment time window, and target leading edge engagement code required by multiple associated debt items in the pending repayment state. To avoid the same new water quality data component being repeatedly used as a repayment water quality data component by multiple associated debt items, a single repayment determination process is performed. The single repayment determination process uses multiple associated debt items that have already met the repayment matching conditions as candidate objects and does not sort associated debt items that do not meet the repayment matching conditions. Before performing the single repayment determination process, first... A candidate related debt item list is established, and all related debt items that meet the repayment matching conditions are written into this candidate related debt item list. The repayment start time, source water quality data component identifier, component trailing time of source water quality data component, and writing order of related debt item in the related debt item set are read from each candidate related debt item. Among them, the repayment start time is used to determine the time proximity between the candidate related debt item and the new water quality data component, the component trailing time of source water quality data component is used to determine the formation order of the source data component corresponding to the candidate related debt item, and the writing order of related debt item in the related debt item set is used to provide a basis for determination when the aforementioned time conditions still cannot distinguish them.

[0043] In this embodiment S3, the single repayment determination process includes three sequentially executed determination steps: time interval value calculation, component trailing edge time comparison, and write order comparison. First, the time interval value between the component leading edge time of the new water quality data component and the repayment start time of each candidate associated debt item is calculated, and the candidate associated debt items are sorted according to the time interval value from smallest to largest. The associated debt item with the smallest time interval value is determined as the repayment object. When there are multiple candidate associated debt items with the same time interval value, the component trailing edge time comparison step is entered, and the component of each source water quality data component is read from the multiple candidate associated debt items. The component's trailing edge time is determined and sorted from back to front. The component with the latest trailing edge time is identified as the object to be repaid. If multiple candidate related debts with the same trailing edge time still exist, the write order comparison step is entered. The write order of these multiple candidate related debts in the related debt set is read, and one of the related debts is identified as the object to be repaid according to the write order. Through the above sequential determination steps, the new water quality data component corresponds to only one object to be repaid among multiple candidate related debts, thereby avoiding multiple repayment association records from being repeatedly bound to the same new water quality data component.

[0044] In this embodiment S3, after the object to be repaid is determined, a new water quality data component is used as the corresponding water quality data component for the object to be repaid, and a repayment association record is generated based on the object to be repaid and the water quality data component for repayment. When the repayment association record is generated, the source water quality data component identifier, target attribute data, repayment time window, target leading edge engagement code, and debt status identifier are read from the object to be repaid. At the same time, the component identifier, leading edge engagement code, component leading edge time, and component attribute data are read from the new water quality data component, and the component identifier of the new water quality data component is written into the repayment association record as the identifier of the water quality data component for repayment. After the repayment association record is generated, the object to be repaid is... The debt status identifier of the repayment object is updated to the repaid status; for related debt items not included in the candidate related debt item list as repayment objects, no repayment association record is generated, and their debt status identifier is maintained as pending repayment, so that they continue to remain in the related debt item set to participate in the repayment matching of subsequent new water quality data components or enter the unpaid certificate conversion process when the current processing time is later than the repayment end time; through the above status processing method, when multiple related debt items meet the repayment matching conditions at the same time, it can ensure that only one repayment association record is generated for a new water quality data component, while maintaining the integrity of the subsequent processing path of the unselected related debt items.

[0045] In this embodiment S4, the outstanding debt transfer process is a method of converting related debt items with a debt status marked as pending repayment and a current processing time later than the repayment end time into blank evidence records. Related debt items with a current processing time later than the repayment end time are those for which no repayment-related record has been formed within the repayment time window. Blank evidence records are data records documenting the outstanding debt items for which no repayment-related record has been formed. Blank evidence records include source water quality data component identifiers, target attribute data, repayment time window, target leading edge interlocking code, current processing time, and outstanding debt. The status identifier; the source water quality data component identifier, target attribute data, repayment time window, and target leading edge interlocking code in the blank evidence record are respectively written by the source water quality data component identifier, target attribute data, repayment time window, and target leading edge interlocking code in the corresponding related debt item, forming an association relationship between the blank evidence record and the corresponding related debt item; after the outstanding debt transfer is completed, the debt status identifier of the corresponding related debt item is updated to the outstanding status; the water quality association analysis record includes the repaid association results corresponding to the repayment association record and the outstanding association results corresponding to the blank evidence record.

[0046] In this embodiment S4, the outstanding debt transfer processing takes the aforementioned set of related debt items as the processing object and uses the current processing time as the judgment time benchmark to verify the time status of related debt items whose debt status identifier remains in the pending repayment state. Specifically, when executing S4, the debt status identifier of each related debt item in the set of related debt items is first read, and related debt items with the debt status identifier in the pending repayment state are filtered out. Then, the repayment time window in the related debt item is read, and the repayment time window includes the repayment start time and repayment end time. Subsequently, the current processing time is compared with the repayment end time. If the preprocessing time is later than the repayment end time, the related debt item is identified as a related debt item that meets the conditions for unpaid transfer of certificates. A related debt item that meets the conditions for unpaid transfer of certificates means that the related debt item has not formed a repayment association record within its repayment time window, and its debt status identifier is still in the pending repayment state. The unpaid transfer of certificates process performs data conversion on the related debt items that meet the above conditions, converting the related debt items that were originally used to wait for the subsequent water quality data component repayment matching into blank evidence records, so that the state of the related debt item that has not formed a repayment association record enters the subsequent water quality association analysis process in the form of structured data records.

[0047] In this embodiment S4, the blank evidence record is used to record the unpaid related debt items for which no repayment related record has been formed within the repayment time window. When generating the blank evidence record, the source water quality data component identifier, target attribute data, repayment time window, and target leading edge interlocking code in the corresponding related debt item are read, and the current processing time is read as the transfer time benchmark. The above content is written into the blank evidence record. Among them, the source water quality data component identifier is used to point to the water quality data component that generated the related debt item, the target attribute data is used to record the target monitoring section and target monitoring indicators that the related debt item originally pointed to, and the repayment time window is used to... Within the time frame during which the associated debt item is allowed to form a repayment-related record, the target leading edge interlock code is used to record the expected access code corresponding to the associated debt item, the current processing time is used to record the time node when the associated debt item is processed for non-repayment transfer, and the non-repayment status identifier is used to record the non-repayment related status corresponding to the blank evidence record; after the blank evidence record is generated, the debt status identifier of the corresponding associated debt item is updated from pending repayment status to non-repayment status, and the status update result is written back to the associated debt item set, so that the associated debt item corresponds to the blank evidence record in the subsequent processing process instead of continuing to participate in repayment matching.

[0048] In this embodiment S4, a correlation is established between the blank evidence record and the corresponding associated debt item. This correlation is established by matching the source water quality data component identifier, target attribute data, repayment time window, and target leading edge interlocking code in the blank evidence record with the corresponding data in the associated debt item. During the write operation, the source water quality data component identifier in the corresponding associated debt item is written into the source water quality data component identifier field in the blank evidence record, the target attribute data in the corresponding associated debt item is written into the target attribute data field in the blank evidence record, and the repayment time window in the corresponding associated debt item is written into the blank evidence record. The repayment time window field in the record writes the target leading edge interlock code of the corresponding related debt item into the target leading edge interlock code field of the blank evidence record, and configures the unpaid status identifier for the blank evidence record. Through the above corresponding writing relationship, the blank evidence record can refer back to the related debt item that generated its content, and can also refer back to the unpaid related relationship derived from the source water quality data component and its trailing edge interlock code. When generating water quality correlation analysis records in the future, the blank evidence record participates in the summary as the unpaid correlation result, so that the related debt items that have not formed a repayment correlation record can still be retained in the analysis chain in the form of a structured record.

[0049] In this embodiment S4, the water quality correlation analysis record is generated jointly based on the repayment correlation record and the blank evidence record. The repayment correlation record corresponds to the repaid correlation result and is used to record the continuity relationship where the source water quality data component and the repaid water quality data component have completed repayment matching. The blank evidence record corresponds to the unpaid correlation result and is used to record the result where the associated debt items derived from the source water quality data component have not formed a repayment correlation record within the repayment time window. When generating the water quality correlation analysis record, the repayment correlation record and the blank evidence record are configured according to the source water quality data component identifier, target attribute data, repayment time window, and debt status identifier. The records are merged, and the repaid and unpaid association results are written into the same water quality association analysis record. In the water quality association analysis record, the repaid association results are used to indicate that the corresponding associated debt has been repaid by subsequent water quality data components, and the unpaid association results are used to indicate that the corresponding associated debt has exceeded the repayment end time and no repayment association record has been formed. Through the above recording method, the water quality association analysis record can simultaneously retain the data relationships that have been formed and those that have not been formed, so that subsequent analysis can query, classify and trace the continuity relationship between water quality monitoring data based on the complete repayment status.

[0050] Example 2: The present invention proposes a water quality monitoring big data storage and correlation analysis system, which is applied to the water quality monitoring big data storage and correlation analysis method proposed in Example 1. It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the water quality monitoring big data storage and correlation analysis method in Example 1.

[0051] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for big data storage and correlation analysis of water quality monitoring, characterized in that, Includes the following steps: S1. Obtain water quality monitoring business data and association rule data, perform component processing on the water quality monitoring business data according to monitoring sections, monitoring indicators and continuous sampling intervals to generate water quality data components, and write the component attribute data of the water quality data components into the component storage unit. Among them, the association rule data is a set of data continuity constraints that limit the formation of associated debt items between water quality data components; the water quality data component is a data unit generated based on the same monitoring section, the same monitoring index, and the same continuous sampling interval; the component attribute data is attribute data that describes the data ownership, time boundary, state boundary, and index boundary of the water quality data component, including the component leading edge time and the component trailing edge time. S2. Read the water quality data components and their component attribute data, perform mortise and tenon interlocking structure construction processing on the water quality data components, generate leading edge interlocking code and trailing edge interlocking code by combining the component attribute data, bind the leading edge interlocking code and trailing edge interlocking code to the corresponding water quality data components and write them into the component index table, generate a set of related debt items based on the trailing edge interlocking code and association rule data, and configure debt status identifiers for all related debt items in the set of related debt items. Among them, the leading edge interlocking code is an access code generated based on the component's leading edge time and component attribute data; the trailing edge interlocking code is a derived code generated based on the component's trailing edge time and component attribute data; the associated debt item is a data unit that records the pending repayment relationship between a water quality data component and subsequent water quality data components, including the source water quality data component identifier, target attribute data, repayment time window, target leading edge interlocking code, and debt status identifier; the repayment time window includes the repayment start time and repayment end time; the debt status identifier includes pending repayment status, repaid status, and unpaid status. S3. When a new water quality data component is written into the component index table, the associated debt items with the debt status marked as pending repayment are retrieved based on the leading edge interlock code and component attribute data of the new water quality data component. When the new water quality data component meets the repayment matching conditions, the new water quality data component is written into the corresponding associated debt item and a repayment association record is generated. The debt status mark of the corresponding associated debt item is updated to the repaid status. Among them, the repayment matching condition is the criterion for determining whether a new water quality data component can repay the corresponding related debts; S4. Using the current processing time as the judgment time benchmark, perform unpaid transfer processing on related debt items whose debt status is marked as pending payment and whose current processing time is later than the payment end time, generate blank evidence records, update the debt status of the related debt item to the unpaid status, and generate water quality correlation analysis records based on the payment correlation records and blank evidence records. Among them, blank evidence records are data units that record the results of unpaid related debts that have not formed a repayment related record within the repayment time window.

2. The method for big data storage and correlation analysis of water quality monitoring according to claim 1, characterized in that: In S1, the water quality monitoring operational data is a set of water quality monitoring data recorded according to monitoring sections, monitoring indicators, and sampling times. The water quality monitoring operational data includes monitoring value data, sampling time data, and status data. The association rule data also records the succession order between monitoring sections, the succession order between monitoring indicators, and the succession range between continuous sampling intervals. The association rule data includes section succession data, indicator succession data, and time succession data. Among them, section succession data represents the data succession order between different monitoring sections, indicator succession data represents the data succession order between different monitoring indicators, and time succession data represents the data succession range between continuous sampling intervals. The componentization processing is a processing method that divides the water quality monitoring operational data into water quality data components based on monitoring sections, monitoring indicators, and continuous sampling intervals.

3. The method for big data storage and correlation analysis of water quality monitoring according to claim 2, characterized in that: In S1, the water quality data component is a data structure formed by sequentially associating a component head, a component body, and a component tail. The component head records the data ownership of the water quality data component, the component body records the monitoring value data and status data within the continuous sampling interval, and the component tail records the time boundary content and indicator boundary content of the water quality data component. The component leading edge time is the start boundary time of the continuous sampling interval corresponding to the water quality data component, and the component trailing edge time is the end boundary time of the continuous sampling interval corresponding to the water quality data component. The component attribute data is attribute data bound to the component identifier of the water quality data component. The component attribute data also includes the component identifier, monitoring section identifier, monitoring indicator identifier, status code, and indicator boundary code. The status code is the code for the status boundary corresponding to the water quality data component, and the indicator boundary code is the code for the indicator boundary corresponding to the water quality data component. The component storage unit is a storage structure used to record the correspondence between the water quality data component and its component attribute data. The component storage unit includes a component recording area and an attribute recording area.

4. The method for big data storage and correlation analysis of water quality monitoring according to claim 3, characterized in that: In S2, the mortise and tenon interlocking structure construction process is a method of encoding the front and rear boundaries of the water quality data component and establishing the boundary continuity relationship; the front boundary of the water quality data component is configured as the access side boundary, and the rear boundary of the water quality data component is configured as the derived side boundary. The leading edge bit code is a data encoding that represents the access side boundary and is used to match the target leading edge bit code in the associated debt item for repayment; the trailing edge bit code is a data encoding that represents the derived side boundary and is used to generate associated debt items by combining the associated rule data. The component index table is an index data structure that records the binding relationship between water quality data components, leading edge bite codes, and trailing edge bite codes. It is used to retrieve the corresponding leading edge bite codes and trailing edge bite codes according to the component identifier of the water quality data component.

5. The method for big data storage and correlation analysis of water quality monitoring according to claim 4, characterized in that: In S2, the leading edge biting code includes a component identifier, a monitoring section identifier, a monitoring index identifier, a component leading edge time, a status code, and an index boundary code; the trailing edge biting code includes a component identifier, a monitoring section identifier, a monitoring index identifier, a component trailing edge time, a status code, and an index boundary code; the same water quality data component corresponds to one leading edge biting code and one trailing edge biting code; the method rule for binding the leading edge biting code and the trailing edge biting code with the corresponding water quality data component is as follows: the component identifier of the water quality data component is used as the binding index identifier, the leading edge biting code is configured as the access-side index item corresponding to the component identifier, the trailing edge biting code is configured as the derived-side index item corresponding to the component identifier, and a binding index record between the component identifier, the leading edge biting code, and the trailing edge biting code is formed in the component index table.

6. The method for big data storage and correlation analysis of water quality monitoring according to claim 5, characterized in that: In step S2, the associated debt item set is a data set used to aggregate multiple associated debt items formed by trailing edge interlocking codes and association rule data. The associated debt items in the associated debt item set are jointly defined by trailing edge interlocking codes and association rule data. The source water quality data component identifier is determined based on the trailing edge interlocking code; the target attribute data is determined based on the association rule data; the repayment time window is determined based on the component trailing edge time of the source water quality data component and the association rule data; the target leading edge interlocking code is generated based on the target attribute data and the repayment time window; and the source water quality data component identifier, target attribute data, repayment time window, target leading edge interlocking code, and debt status identifier are combined into an associated debt item. The target attribute data includes the target monitoring section identifier and the target monitoring indicator identifier. The repayment time window uses the component trailing edge time of the source water quality data component as the time reference and includes the repayment start time and repayment end time. The target leading edge bite code is the expected access code for subsequent water quality data components to perform reimbursement matching; The debt status identifier is used to record the status of the repayment process of related debt items. When a related debt item is generated, the debt status identifier is configured as pending repayment. Pending repayment status indicates that the related debt item has not yet generated a repayment record. Repaid status indicates that the related debt item has generated a repayment record. Unpaid status indicates that the related debt item has been converted into a blank evidence record.

7. The method for big data storage and correlation analysis of water quality monitoring according to claim 6, characterized in that: In step S3, the new water quality data component is the water quality data component that is written into the component index table after the associated debt item set is generated and participates in the repayment matching of associated debt items in the pending repayment status. The repayment matching conditions include target attribute data matching conditions, repayment time window matching conditions, and target leading edge biting code matching conditions. When the component attribute data of the new water quality data component corresponds to the target attribute data in the associated debt item, the component leading edge time of the new water quality data component is not earlier than the repayment start time and not later than the repayment end time, and the leading edge biting code of the new water quality data component corresponds to the target leading edge biting code in the associated debt item, the new water quality data component is determined as the repayment water quality data component, and a repayment association record is generated based on the repayment water quality data component and the corresponding associated debt item. The repayment association record is a data record that records the repayment binding relationship between the associated debt item and the repayment water quality data component, including the source water quality data component identifier, the repayment water quality data component identifier, the target attribute data, the repayment time window, the target leading edge biting code, and the repaid status identifier.

8. The method for big data storage and correlation analysis of water quality monitoring according to claim 7, characterized in that: In S3, when a new water quality data component corresponds to multiple related debt items that meet the repayment matching conditions, a single repayment determination process is performed on the multiple related debt items that meet the repayment matching conditions. The single repayment determination process includes: calculating the time interval between the leading edge time of the new water quality data component and the repayment start time of each associated debt item, and determining the associated debt item with the smallest time interval value as the repayment target; when there are multiple associated debt items with the same time interval value, determining the associated debt item with the latest trailing edge time of the source water quality data component as the repayment target; when there are multiple associated debt items with the same trailing edge time, determining the repayment target according to the writing order of the associated debt items in the associated debt item set. Repayment association records are generated based on the objects to be repaid, and the debt status identifier of the objects to be repaid is updated to the repaid status. The debt status identifier of related debt items that are not objects to be repaid is maintained as pending repayment status.

9. The method for big data storage and correlation analysis of water quality monitoring according to claim 8, characterized in that: In S4, the outstanding debt transfer processing is a method of converting related debt items with a debt status marked as pending repayment and a current processing time later than the repayment end time into blank evidence records. Related debt items with a current processing time later than the repayment end time are those for which no repayment-related record has been formed within the repayment time window. Blank evidence records are data records documenting the outstanding debt items for which no repayment-related record has been formed. Blank evidence records include source water quality data component identifiers, target attribute data, repayment time window, target leading edge interlocking code, current processing time, and outstanding debt. Status identifier; The source water quality data component identifier, target attribute data, repayment time window, and target leading edge interlocking code in the blank evidence record are respectively written by the source water quality data component identifier, target attribute data, repayment time window, and target leading edge interlocking code in the corresponding related debt item, forming an association pointing relationship between the blank evidence record and the corresponding related debt item; After the outstanding debt transfer is completed, the debt status identifier of the corresponding related debt item is updated to the outstanding status; The water quality association analysis record includes the repaid association results corresponding to the repayment association record and the outstanding association results corresponding to the blank evidence record.

10. A water quality monitoring big data storage and correlation analysis system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the water quality monitoring big data storage and correlation analysis method as described in any one of claims 1-9.

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