A method, system and storage medium for collecting and storing water network engineering data

Abstract

The present invention discloses a method, system, and storage medium for collecting and storing water network project data, belonging to the technical field of water diversion project data information management. The method comprises: monitoring the water diversion project, analyzing the characteristics of the water diversion project monitoring data, generating a data characteristic report, and constructing model rules; combining predefined storage rules to sequentially construct a stored conceptual model, a logical model, and an entity model, clarifying the relationship between the models; combining the entity model to develop a corresponding storage model; classifying the water diversion project monitoring data according to the characteristics of the water diversion project monitoring data, integrating the time dimension, and performing model matching; combining the matched models, respectively performing model verification and data storage; classifying and storing the verified models and data in a data storage management layer, and performing one-by-one verification on the incoming data and the stored data to generate a data reconciliation statement. The present invention provides a preprocessing function for the application and processing of water diversion project data.

Description

Technical Field

[0001] The present invention relates to the technical field of water diversion project data information management, and more specifically to a method, system and storage medium for collecting and storing water network project data, which is mainly applicable to water diversion project data information management. Background Art

[0002] The automated data collection equipment currently used in water network projects has a relatively high collection frequency. Due to the characteristics of water, it will fluctuate when flowing, and the monitored data will show waveform jumps. Fluctuations within a certain range can be considered normal changes. When the monitoring equipment collects data, it will perform a filtering process inside the equipment and output relatively stable data to ensure calculation, measurement, trend discovery and support scheduling applications.

[0003] In a relatively stable environment, water flow may fluctuate, but the changes are minimal. However, equipment must collect, calculate, output, and transmit data every minute, and the output data rarely changes. Currently, virtually every piece of data must be stored. If every piece of data is stored, a large amount of duplicate data is stored, which is detrimental to data storage and management. When applying data to business applications, it also requires adaptive management in both time and space. To meet the needs of business statistics and data analysis, even if full data storage is implemented, data cleaning, processing, and loading are still required, which still cannot meet business needs.

[0004] Therefore, how to provide a method, system and storage medium for collecting and storing water network engineering data is an urgent problem that those skilled in the art need to solve. Summary of the Invention

[0005] In view of this, the present invention provides a method, system and storage medium for collecting and storing water network engineering data.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] In one aspect, the present invention discloses a method for collecting and storing water network engineering data, comprising:

[0008] S100: Monitor the water diversion project, analyze the characteristics of the water diversion project monitoring data, generate a data characteristics report, and build model rules;

[0009] S200: Based on predefined storage rules, sequentially construct the storage conceptual model, logical model, and entity model, and clarify the relationship between models;

[0010] S300: Developing a corresponding storage model based on the entity model;

[0011] S400: The water diversion project monitoring data is classified according to the characteristics of the water diversion project monitoring data, integrated with the time dimension, and model matching is performed;

[0012] S500: combining the matched models, respectively performing model verification, and storing corresponding data of the water diversion project monitoring data;

[0013] S600: Classify and store the verified models and data in the data storage management layer, verify the incoming data and stored data one by one, and generate a data reconciliation statement.

[0014] Preferably, after S600, the method further includes: S700: managing the generated data statement, recording data logs, model logs, and data services.

[0015] Preferably, after S300, the method further includes: modifying the storage rules and updating the predefined storage rules.

[0016] Preferably, the data storage in S500 includes storage in a warehouse, and the storage model of the storage in the warehouse includes: a timing model, a change model, a state model, an event model, a calculation model and other models;

[0017] The verified compliance monitoring data of water diversion projects are stored in the corresponding matching models respectively.

[0018] Preferably, the storage model for warehousing also includes: a timing storage model, a quantitative storage model, a change storage model, a trend storage model, a packaging storage model, a parsing storage model, a cache storage model, and a results storage model.

[0019] Preferably, after the water diversion project monitoring data is stored in the corresponding data in S500, the following steps are included:

[0020] Obtain data by following the reverse operation, accessing the data resource directory and checking the data services contained in the directory;

[0021] For data already in the data service, it can be directly retrieved. If there is no generated data, it needs to be recalculated and generated.

[0022] Preferably, the S500 performs corresponding data storage for the water diversion project monitoring data, and also includes: cache storage, which is directly connected to the automatically collected data and does not require actual storage. This type of data runs in the cache and can be retrieved by itself when needed. It does not need to go through multiple links through services or background database calls, meeting the requirements of real-time dynamic display.

[0023] On the other hand, the present invention also provides a system for collecting and storing water network engineering data, comprising:

[0024] Acquisition and processing module: used to monitor water diversion projects, analyze the characteristics of water diversion project monitoring data, generate data characteristic reports, and build model rules;

[0025] An analysis module, connected to the acquisition and processing module, is used to sequentially construct a storage conceptual model, a logical model, and an entity model in accordance with predefined storage rules, and to clarify the relationship between models;

[0026] A development module, connected to the analysis module, for developing a corresponding storage model in combination with the entity model;

[0027] A matching module, connected to the development module, is used to classify the water diversion project monitoring data according to the characteristics of the water diversion project monitoring data, integrate the time dimension, and perform model matching;

[0028] An execution module, connected to the matching module, for combining the matched models, performing respective model verifications, and storing the data;

[0029] The management module is connected to the execution module and is used to classify and store the verified models and data in the data storage management layer, verify the incoming data and the stored data one by one, and generate a data statement.

[0030] On the other hand, the present invention further provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for collecting and storing water network engineering data.

[0031] It can be seen from the above technical solution that compared with the existing technology, the present invention discloses a method, system and storage medium for collecting and storing water network project data, which solves the problems in collecting, storing and standardizing water diversion project monitoring data. The present invention uses pre-processing storage technology and methods, and analyzes data characteristics to perform dimensionality reduction storage and management of data, effectively reducing data storage capacity, improving resource utilization, enhancing data effectiveness, and providing pre-processing functions for data application and processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0033] Figure 1 A schematic diagram of the data storage process provided by the present invention;

[0034] Figure 2 A schematic diagram of the initial storage logic structure provided by the present invention;

[0035] Figure 3 A timing model mechanism diagram provided by the present invention;

[0036] Figure 4 A diagram of the quantitative storage mechanism provided by the present invention;

[0037] Figure 5 A logic flow chart of quantitative storage provided by the present invention;

[0038] Figure 6 Schematic diagram of the fitting curve provided by the present invention

[0039] Figure 7 A schematic diagram of the data citation process provided by the present invention;

[0040] Figure 8 The analytical diagram of the inner difference linear function algorithm provided by the present invention;

[0041] Figure 9 This is a schematic diagram of the system structure provided by the present invention. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] The embodiment of the present invention discloses a method for collecting and storing water network engineering data, comprising:

[0044] S100: Monitor the water diversion project, analyze the characteristics of the water diversion project monitoring data, generate a data characteristics report, and build model rules;

[0045] S200: Based on predefined storage rules, sequentially construct the storage conceptual model, logical model, and entity model, and clarify the relationship between models;

[0046] S300: Develop the corresponding storage model based on the entity model;

[0047] S400: The water diversion project monitoring data is classified according to the characteristics of the water diversion project monitoring data, integrated with the time dimension, and model matching is performed;

[0048] S500: Combine the matched models, perform model verification on each model, and store the data;

[0049] S600: Classify and store the verified models and data in the data storage management layer, verify the incoming data and stored data one by one, and generate a data reconciliation statement.

[0050] See attached Figure 1 As shown, in a specific embodiment, data storage includes the steps of:

[0051] S100: Analyze the monitoring data characteristics of existing water diversion projects, such as water level fluctuation, water flow stratification, water quality regionalization, equipment status alternation, etc., to form a data feature report and build model rules.

[0052] S200: Based on the defined storage rules, the storage conceptual model, logical model, and entity model are sequentially constructed to clarify the relationship between models.

[0053] S300: Develop a corresponding storage model based on the entity model. When building the storage model, some differentiated requirements and new rules need to be redefined into the storage rules. In the entire storage system, the rules, models, and entities remain consistent.

[0054] S400: Various types of business monitoring data will be classified according to their characteristics, integrated with the time dimension, and various types of model matching will be performed.

[0055] S500: Combine the matched models and perform model verification on each model, such as timing model, change model, state model, event model, calculation model, and other models, and store the compliant data in the database.

[0056] S600: Data storage management classifies and stores the verified models and data, and verifies the incoming data and stored data one by one to form a data reconciliation statement.

[0057] Specifically, it also includes: S100: managing the generated data, recording logs, data services, and publishing related data services to directly provide support for business applications.

[0058] Specifically, it also includes data cache storage. Cache storage is directly connected to automatically collected data and does not require actual storage. This type of data runs in the cache and can be retrieved when needed. It does not need to go through multiple links through services or background database calls to meet the requirements of real-time dynamic display.

[0059] In a specific embodiment, after the data is stored in the database, the reverse operation is performed to obtain the data. First, the business will access the data resource directory to check which data services are included in the directory. If there is already data in the data service, it will be directly retrieved. If there is no generated data, it needs to be recalculated and generated.

[0060] More specifically, this embodiment optimizes the existing storage method as follows:

[0061] 1. Timed storage; 2. Quantitative storage: mechanism - storage in a certain space; 3. Change storage; 4. State storage; 5. Trend storage; 6. Packaged storage; 7. Parsed storage; 8. Cache storage; 9. Results storage; 10. Computational storage; 11. Data storage and algorithm management system functions; 12. Support for different types of data; 13. Support for the compilation and parsing of data storage algorithms; 14. Support for data allocation strategies and methods; 15. Support for data increment and result set output, providing various types of data in the form of services.

[0062] In a specific embodiment, various storage algorithms are configured to achieve periodic and irregular data storage, as well as both characteristic and non-characteristic data storage. This storage is primarily divided into two types: direct storage of raw data and computational data storage. Direct storage has multiple forms, primarily through timed storage, quantitative storage, change storage, state storage, and event storage. Computational storage primarily includes fitting calculation storage, trend storage, and results storage. Cache storage serves business applications, does not require storage, and provides real-time feedback on data changes.

[0063] Each storage is not isolated but interconnected and mutually constrained to ensure data availability on every node. For example, when data changes, it is necessary to store both the changed data and the change status data, which records the time of change, the original value, the changed value, the change range, and related trends. If a scheduling instruction is found to be the cause, the scheduling instruction, the changed data, and the change range must all be linked, and so on. Establishing rules for data storage is crucial.

[0064] The storage and parsing algorithms are as follows:

[0065] Direct storage of raw data

[0066] Timing storage: With time as the dimension, set the time interval and obtain the current real-time data in the order of time. For the storage logic structure, see the attached Figure 2 As shown, the horizontal axis is time, with hours, minutes, and seconds as units, and the vertical axis is data, with corresponding data stored at the time node.

[0067] Specifically, timing storage is performed through a timing model. Timing storage requires the support of a time model algorithm, which uses real-time data input to specify the time period and data type, as well as the type of database to be written. For the actual processing mechanism, see the attached Figure 3 As shown, the specific steps include:

[0068] 1. The timing model realizes the timing storage of data and partial logical judgment.

[0069] 2. Combine input conditions and data conditions to conditionally load relevant data and perform calculations and classifications. Normal data is stored in the normal database, and abnormal data is stored in the abnormal database.

[0070] 3. Normal data supports business logic and statistics, while abnormal data supports fault diagnosis, data tracking, and defect analysis.

[0071] 4. The model can output data in formats including encoding, data, and timestamp according to the set rules.

[0072] 5. The model can directly output time-related data, such as five minutes and ten minutes after the current time. This function will assist other models in use.

[0073] 6. When the input data does not meet the requirements of rationality and validity, the timing model will take the latest correct data from the normal database and modify the time to the time when the database should be stored.

[0074] 7. After the model executes a valid data, it will enter the next cycle, pass the data scheduling model, and then enter the next round of timed storage.

[0075] 8. The exception databases generated by the models are all standard libraries. Timing models can be written, and quantitative models can be written together. There are distinguishing fields to distinguish model types and business types, etc.

[0076] Specifically, quantitative storage: with time as the dimension, measurement starts from a certain time, and automatically stores when the measurement value is reached. This method can measure the current cumulative value and calculate the number of accumulations. Quantitative storage not only stores the cumulative amount but also the frequency of accumulation. This type of storage can be used as the basis for measurement, avoiding the method of only having data at both ends and ignoring the intermediate process. It is suitable for water accumulation and electricity accumulation.

[0077] More specifically, quantitative storage is another implementation of timed storage. Quantification can output a current value based on a time period as a cumulative amount. This method is different from timed storage in that it requires the production of data volume first and then verification of compliance and validity. There are significant differences in processing logic, and the accuracy of the data will depend on the model parameters, execution frequency, assistance to other models, etc., and will vary depending on different business characteristics.

[0078] In a specific embodiment, a fixed time amount of data is stored by a quantitative time model, and a fixed data growth amount of data is stored by a quantitative data model, and these two storage methods are cross-checked and cross-processed to ensure that the final data is accurate and effective. The actual processing mechanism is shown in the attached Figure 4 shown.

[0079] The cumulative amount can reflect the changing relationship between the monitoring data and time, such as the curve of water volume changing over time, the curve of electricity volume changing over time, and the changing trend of other XXX quantities over time.

[0080] Original data storage: ID, data code, data name, data value, update time.

[0081] Accumulated data storage: ID, data code, data name, start monitoring value, end monitoring value, measurement times, measurement function, data curve, etc.

[0082] For details, see the attached Figure 5 The figure shows the logical process of quantitative storage, which involves obtaining storage rules, initializing the system, selecting a storage model, selecting a storage base point, growth amount, and maximum growth threshold, setting a start time, and looping based on this. Quantitative judgment is made through increments and thresholds. When the set time period is reached, it is determined whether the growth amount meets the storage requirements. If so, the current time node data is recorded and stored. If the storage quantitative requirements are not met, the loop execution continues. When the loop execution reaches the maximum threshold or external conditions interfere within the time period, the quantitative storage ends.

[0083] In a specific embodiment, change storage: with time as the dimension, the monitoring data is stored when it changes. This storage method needs to be based on the device's own filtering model processing and is performed under the condition that the data is relatively stable. There are some differences between change storage and quantitative storage. This storage mode is mainly to determine a range of impacts, which is a reflection of the situation within a time area, rather than a single value reflection. In order to reflect the change storage, it is necessary to first perform timing and quantitative storage, and then make a determination on the change storage.

[0084] The influencing factors include the length of time, range of change, change trend, standard value, etc.

[0085] In a specific embodiment, event storage: actively initiates actions to affect changes or modifications in the state of affairs. In a water conservancy automation system, the issuance of events will be accompanied by the start and stop of equipment, followed by changes in the state of the equipment.

[0086] Event storage is a special case of timed storage and needs to be triggered by an event. This is what distinguishes it from timed storage. The data itself does not change, but an event information is recorded, and the type is event.

[0087] Specifically, see Table 1. Taking events as the basis, it records how to record event information when an event occurs, including the source of the event, data identifier, event data, etc. The table defines the table structure for event storage. The specific storage is subject to the content marked in the notes.

[0088] Table 1: Event information storage structure / format reference table

[0089]

[0090] In a specific embodiment, state storage: adapts to changes in the state of water conservancy equipment, from 0 to 1, 1 to 0. State storage is similar to event storage, the difference being that one stores state and the other stores raw data.

[0091] The biggest purpose of state storage is process judgment. By judging the device status, it is marked whether to proceed to the next step, whether to perform mutual exclusion processing, whether to perform forced operations, and whether to determine the status of the device operation.

[0092] Specifically, see Table 2. Using the state as the basis, this table records the circumstances before and after a state change, including system source, data identifier, data state, and time. The table defines the structure for state storage; specific storage is subject to the content specified in the notes.

[0093] Table 2: Status information storage structure / format reference table

[0094]

[0095] Computational data storage

[0096] Computational data storage is a supplement to timing, quantitative, and change storage. It calculates the distribution of data within a certain interval based on a set time dimension as a gradient under a certain data volume, and generates a quadratic function of the data distribution as the calculated related data.

[0097] Specifically, see Table 3, which is a record table of actual data collection information. The distribution of data within a certain time interval can be deduced through this information.

[0098] 2. The data structure of this table is key-value mode and is independent data storage.

[0099] Table 3: Real-time data storage information / format reference table

[0100]

[0101]

[0102] The built-in algorithm is used to fit the data in the time domain, following the quadratic function Y = A*x² + B*x + C. Combining the above data, we can fit the formula y = -0.0018x² + 0.0695x + 43587, where A is -0.0018, B is +0.0695, and C is +43587. The vertical axis represents time, and the horizontal axis represents data. A curve is fitted. See the attached diagram for a schematic diagram of the fitted curve. Figure 6 shown.

[0103] As shown in Table 4, after fitting the data, the fitting results are stored in the format.

[0104] Table 4: Built-in algorithm storage structure / format reference table

[0105]

[0106]

[0107] If the calculation type is 1: use K, B parameters; if the calculation type is 2: use A, B, C parameters.

[0108] The biggest advantage of computational storage is that it processes data as a collection, integrating data from one day or even multiple days into one dimension, which greatly reduces storage space. Generally, when working on an automatic control system, the daily data volume of a certain data type is around 200MB. However, with computational storage, only 2KB of storage space is required, which is 102,400 times the difference between total storage and computational storage.

[0109] Computational storage also has an error defect, which is that there is a difference between the calculated data and the original data. If the error is allowed within the range, it can be used.

[0110] Computational storage can be used as a type of compressed storage. It classifies data according to dimensions, calculates the position and distribution of each point, and stores the same type of data together, while different types of data are stored independently.

[0111] In a specific embodiment, see the attached Figure 7 As shown, data citation includes the following steps:

[0112] 1. Analyze business needs, obtain cache monitoring data, and search for matches between existing data resource directories and data. If existing data services meet the requirements, you can directly access the data services.

[0113] 2. If no match is successful, other pattern matching is performed. At this time, the model plays a key role. It can match the corresponding database, query and parse the stored data based on business conditions, generate target data, directly feedback the result set, or publish data services.

[0114] 3. If the model matching is unsuccessful, a special service will be generated. First, the storage rules are reviewed. The storage paths, storage methods, and storage data information of various types of data are defined in the storage rules. By matching with the storage rules, it is determined which database or databases to obtain the data from. After obtaining the data, the format and content of the data are analyzed, and a preliminary method for parsing the data is constructed. The data is further parsed and processed to generate a data set in the target format, and the result set is directly fed back, or a data service is published.

[0115] Specifically, it also includes the analysis algorithm: grid storage

[0116] More specifically, the grid storage steps are: start; analysis---rules; business needs; data resource directory; retrieval of data services; matching of data services; classification: retrieving data, retrieving storage rules, analyzing storage rules; scheduling analysis services, generating rule data; feedback of data; end.

[0117] Specifically, each type of storage algorithm corresponds to a corresponding parsing algorithm. Combined with business needs, the algorithm is traversed. When the appropriate conditions are triggered, the corresponding algorithm is selected for parsing and storage. Simpler algorithms do not require parsing and can be directly referenced. More complex algorithms require reverse parsing, involving two core algorithms, Algorithm 1 and Algorithm 2. Algorithm 1 is an inner difference linear function, and Algorithm 2 is an inner difference quadratic function, which are fitted.

[0118] Algorithm 1: Inner difference linear function is suitable for parsing most of the above data. The principle is to select two sets of data with similar input conditions from the corresponding storage database. First, the time format is converted to a numerical type. The coefficients k and b between the two points are calculated within the linear function Y = kX + b. The value of Y is calculated by combining the points of the data to be calculated and substituting them into the formula. At this time, the inner difference function will feed back the calculation results to meet business needs.

[0119] For details, see the attached Figure 8 As shown, through the relationship between the two points, the data of any node in the middle is calculated, such as 15 corresponding to 20221112010100, and 50 corresponding to 20221112011000, then k=0.039, b=-7863765766.66667, then the timestamp of 20221112010600 corresponds to a value on the left is 34.44.

[0120] Algorithm 2: Inner difference quadratic function. If it is stored as a quadratic function, perform time matching and bring time into the quadratic function (convert the time format to a numerical type for calculation), y = -0.0018x2 + 0.0695x + 43587, and the value of any data can be obtained.

[0121] On the other hand, see Figure 9 As shown, an embodiment of the present invention further discloses a system for collecting and storing water network engineering data, including:

[0122] Acquisition and processing module: used to monitor water diversion projects, analyze the characteristics of water diversion project monitoring data, generate data characteristic reports, and build model rules;

[0123] An analysis module, connected to the acquisition and processing module, is used to sequentially construct a storage conceptual model, a logical model, and an entity model in accordance with predefined storage rules, and to clarify the relationship between models;

[0124] A development module, connected to the analysis module, for developing a corresponding storage model in combination with the entity model;

[0125] A matching module, connected to the development module, is used to classify the water diversion project monitoring data according to the characteristics of the water diversion project monitoring data, integrate the time dimension, and perform model matching;

[0126] An execution module, connected to the matching module, for combining the matched models, performing respective model verifications, and storing the data;

[0127] The management module is connected to the execution module and is used to classify and store the verified models and data in the data storage management layer, verify the incoming data and the stored data one by one, and generate a data statement.

[0128] On the other hand, an embodiment of the present invention further discloses a computer storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the steps of a method for collecting and storing water network engineering data.

[0129] This invention addresses the current data storage dilemma of automatic control systems in the water conservancy industry, promotes the actual needs of the business and the construction of a digital twin data base, solves the problem of full data storage, and improves data storage. It solves the current storage method of water diversion project monitoring data, provides a support for this storage method, and develops a data storage and algorithm management system that integrates multiple algorithm mechanisms, provides data mechanism management and strategy, and serves data applications.

[0130] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0131] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for collecting and storing water network engineering data, characterized in that: include: S100: Monitor the water diversion project, analyze the characteristics of the water diversion project monitoring data, generate a data characteristics report, and build model rules; S200: Based on predefined storage rules, sequentially construct the storage conceptual model, logical model, and entity model, and clarify the relationship between models; S300: Developing a corresponding storage model based on the entity model; S400: The water diversion project monitoring data is classified according to the characteristics of the water diversion project monitoring data, integrated with the time dimension, and model matching is performed; S500: combining the matched models, respectively verifying the water diversion project monitoring data and storing the data; S600: The verified models and data are classified and stored in the data storage management layer, and the incoming data and stored data are verified to generate a data reconciliation statement; S700: Manage the generated data statements, record data logs, record model logs, and data services; The data storage in S500 includes warehousing, and the warehousing storage models include: timing model, change model, state model, event model, calculation model, timing storage model, quantitative storage model, change storage, trend storage model, packaging storage model, parsing storage model, cache storage model, and achievement storage model; The verified compliance water diversion project monitoring data are stored in the corresponding matching models respectively; The S500 stores the corresponding data for the water diversion project monitoring data, and also includes: cache storage. The cache storage is directly connected to the automatically collected data and does not require actual storage. The cache stored data runs in the cache and can be retrieved by itself when needed. It does not need to go through multiple links through services or background database calls, meeting the requirements of real-time dynamic display.

2. The method for collecting and storing water network engineering data according to claim 1, characterized in that: After S300, the process further includes: modifying the storage rules and updating the predefined storage rules.

3. The method for collecting and storing water network engineering data according to claim 1, characterized in that: After the corresponding data is stored in S500, the following steps are included: Obtain data by following the reverse operation, accessing the data resource directory and checking the data services contained in the directory; For data already in the data service, it can be directly retrieved. If there is no generated data, it needs to be recalculated and generated.

4. A system for collecting and storing water network engineering data using the method for collecting and storing water network engineering data according to any one of claims 1 to 3, characterized in that: include: Acquisition and processing module: used to monitor water diversion projects, analyze the characteristics of water diversion project monitoring data, generate data characteristic reports, and build model rules; An analysis module, connected to the acquisition and processing module, is used to sequentially construct a storage conceptual model, a logical model, and an entity model in accordance with predefined storage rules, and to clarify the relationships between models; A development module, connected to the analysis module, for developing a corresponding storage model in combination with the entity model; A matching module, connected to the development module, is used to classify the water diversion project monitoring data according to the characteristics of the water diversion project monitoring data, integrate the time dimension, and perform model matching; An execution module, connected to the matching module, for combining the matched models, performing respective model verifications, and storing the data; The management module is connected to the execution module and is used to classify and store the verified models and data in the data storage management layer, verify the incoming data and the stored data, and generate a data statement.

5. A computer storage medium, characterized in that The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for collecting and storing water network engineering data as described in any one of claims 1 to 3.

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