Water Conservancy Integrated Database Multi-Source Sensing Automatic Construction System

Abstract

This invention relates to the field of water resources data technology, specifically disclosing an automatic multi-source sensing construction system for integrated water resources databases. This system includes a multi-source sensing feature calculation module, a data link encryption and encapsulation module, a self-evolving heterogeneous topology reconstruction center module, a spatiotemporal semantic holographic fusion engine module, and a blockchain trust anchoring mechanism module. The invention calculates environmental fluctuation entropy and dynamically solves for the optimal sampling frequency using a logistic function. Based on the principle of electrochemical corrosion, it uses an exponential decay model to adjust the sensor calibration cycle according to real-time soil salinity, achieving adaptive adjustment of the sampling frequency to accurately capture transient features. It also provides automatic compensation for sensor drift in highly corrosive environments, significantly improving the spatiotemporal accuracy and reliability of long-term monitoring data. This overcomes the shortcomings of existing technologies that lack dynamic calibration compensation mechanisms in highly corrosive environments, leading to severe sensor zero-point drift, distortion of long-term monitoring data, and thus misleading decision-making.

Description

Technical Field

[0001] This invention relates to the field of water resources data technology, specifically to an automatic construction system for multi-source sensing of integrated water resources database. Background Technology

[0002] With the rapid development of IoT, big data, and AI technologies, smart water management and digital agriculture have become key pathways to alleviate global water shortages and improve agricultural productivity. In modern large and medium-sized irrigation districts, in order to achieve precision irrigation and optimal allocation of water resources, it is usually necessary to deploy a large number of sensing devices such as soil moisture sensors, weather stations, and hydrological flow meters to build a digital foundation.

[0003] While current smart irrigation district construction has widely deployed sensing devices, significant deficiencies remain in data acquisition and architecture building. On the one hand, the sensing layer generally adopts a fixed-frequency sampling mechanism, lacking the ability to adaptively adjust to nonlinear environmental fluctuations. This leads to the loss of key features during periods of rapid change and the waste of resources during periods of stability. Furthermore, in highly corrosive environments, the lack of a dynamic calibration and compensation mechanism based on environmental parameters results in severe zero-point drift of sensors, causing long-term data distortion and misleading decision-making. On the other hand, database construction heavily relies on manual modeling, lacking an automatic fusion mechanism for multimodal heterogeneous data. It cannot automatically generate a suitable topological skeleton based on the physical parameters of the irrigation district. This approach not only leads to long deployment cycles and high maintenance costs but also severs the deep semantic connections between archives, temporal streams, and spatial vector data, forming data silos that are difficult to mine and hindering the intelligent upgrading of water conservancy projects. Summary of the Invention

[0004] The purpose of this invention is to provide an automatic construction system for multi-source sensing of integrated water conservancy database to solve the problems mentioned above.

[0005] The objective of this invention can be achieved through the following technical solutions: This system calculates the entropy value of environmental fluctuations and dynamically solves the optimal sampling frequency using the logistic function. Based on the principle of electrochemical corrosion, it uses an exponential decay model to adjust the sensor calibration cycle according to the real-time soil salinity. This enables the sampling frequency to be adaptively adjusted according to environmental fluctuations to accurately capture transient characteristics. It also provides automatic compensation for sensor drift in highly corrosive environments, significantly improving the spatiotemporal accuracy and reliability of long-term monitoring data. This overcomes the shortcomings of existing technologies that lack a dynamic calibration compensation mechanism based on environmental parameters in highly corrosive environments, resulting in severe sensor zero-point drift, distortion of long-term monitoring data, and thus misleading decision-making.

[0006] The integrated water conservancy database multi-source sensing automatic construction system includes a multi-source sensing feature calculation module, a data link encryption and encapsulation module, a self-evolving heterogeneous topology reconstruction center module, a spatiotemporal semantic holographic fusion engine module, and a blockchain trust anchoring mechanism module. The multi-source sensing feature calculation module is configured to perform statistical modeling on the collected irrigation area environmental parameters to calculate the environmental fluctuation entropy value, and run an adaptive algorithm based on the entropy value to dynamically solve the optimal sampling frequency, driving the device to output a discretized raw data stream carrying spatiotemporal labels; The data link encryption and encapsulation module connects to the multi-source sensing feature calculation module, is configured to construct a multi-channel redundant transmission topology, performs differential coding compression and national cryptographic algorithm encryption on the discretized raw data stream, and encapsulates it to generate secure ciphertext data packets; The self-evolving heterogeneous topology reconstruction center module connects to the data link encryption and encapsulation module, and is configured to call preset templates to dynamically generate a dynamic database topology skeleton; it also uses a long short-term memory neural network model to calculate the temporal residuals of the data flow to obtain a data fidelity index, and uses this index to select and generate high-confidence, pure data entities and route them to be written into a multi-dimensional heterogeneous storage matrix; The spatiotemporal semantic holographic fusion engine module, connected to the self-evolving heterogeneous topology reconstruction center module, is configured to project dynamic values ​​from temporal units onto a three-dimensional coordinate system to construct a holographic digital twin mapping; and drives a multi-objective fusion hybrid intelligent decision-making model to deduce and generate ecological regulation optimization vectors using this mapping as input; The blockchain trust anchoring mechanism module connects to the self-evolving heterogeneous topology reconstruction center module and the spatiotemporal semantic holographic fusion engine module. It is configured to generate unique digital fingerprints for high-confidence pure data entities and ecological regulation optimization vectors and write them into the distributed consortium chain ledger to generate a full-link trusted traceability certificate.

[0007] Furthermore, the operational logic of the multi-source sensing feature calculation module includes: A time-sliding window is constructed at the current acquisition time. The normalized time-series change rate of each environmental parameter within the window is extracted. A preset random forest model is used to predict the fluctuation probability distribution of the time-series change rate. This probability distribution is then substituted into the information entropy formula to calculate the environmental fluctuation entropy value, which characterizes environmental uncertainty. The environmental fluctuation entropy value is input into the adaptive sampling control model, and a nonlinear mapping curve is constructed using the logistic function. The optimal sampling frequency is dynamically calculated so that the optimal sampling frequency increases nonlinearly in an S-shape with the increase of the environmental fluctuation entropy value, in order to capture transient characteristics during periods of drastic environmental change. Based on the principle of electrochemical corrosion, an exponential decay model is used to process real-time monitored soil salinity data and calculate the next calibration cycle of the sensor. This results in the calibration cycle shortening exponentially as the soil salinity increases, thus compensating for sensor drift in highly corrosive environments. The underlying hardware is driven to collect data based on the calculated optimal sampling frequency, and the calibration period is used as metadata. Combined with BeiDou positioning coordinates and UTC timestamps, the data is encapsulated to generate a discrete raw data stream.

[0008] Furthermore, the operational logic of the data link encryption and encapsulation module includes: A redundant transmission topology incorporating 5G mobile communication technology, narrowband IoT, and long-range radio technology is constructed. The signal-to-noise ratio (SNR) and round-trip delay of each physical channel are detected in real time. The SNR and delay are weighted and calculated based on a weighting coefficient and a penalty coefficient, respectively, to obtain a quantified channel quality score. The link with the highest score is then selected for data transmission. The channel quality score is directly related to the packet loss rate of the transmission link. The system presets a critical threshold for the score corresponding to a packet loss rate of 5%. When the channel quality score calculated in real time is lower than this threshold, the adaptive channel switching logic is automatically triggered to schedule to the backup link. The discretized raw input data stream is subjected to edge compression based on differential coding. The differential increment of the data at the current time is calculated by subtracting the absolute value of the environment parameters cached at the previous time step from the absolute value of the raw environment parameters at the current time step. The data bit width is reduced by utilizing the temporal continuity characteristics of the environment parameters, thus achieving lossless compression. The differential increment is encrypted by calling the domestic commercial cryptographic algorithm SM4. The data block is then subjected to multiple rounds of nonlinear iterative operations using the preset system symmetric master key to generate a ciphertext sequence that cannot be reverse-engineered. This ciphertext sequence is then packaged with the channel quality score and compressed metadata to generate a secure ciphertext data packet.

[0009] Furthermore, the operational logic of the self-evolving heterogeneous topology reconstruction center module includes: The system decrypts and restores secure encrypted data packets, parses the irrigation district dimension parameters, automatically calls a pre-set domain knowledge template library, and dynamically generates time-series tables or relational table templates containing specific fields based on crop type or water conservancy facility attributes. It then constructs a dynamic database topology skeleton adapted to the current scenario, achieving adaptive initialization of the database architecture. Heterogeneous matrix routing mapping is performed through an internal mapping protocol to distribute data to a multidimensional heterogeneous storage matrix; the multidimensional heterogeneous storage matrix is ​​specifically composed of the following units: MySQL relational database for storing basic archives, TDengine time-series database for storing high-frequency monitoring streams, PostGIS spatial database for storing geographic vector data, and Neo4j graph-structured database for storing topological associations.

[0010] Furthermore, the self-evolving heterogeneous topology reconstruction center module is configured to execute deep cleaning logic based on a long short-term memory neural network model: The temporal patterns of historical data are learned using a long short-term memory neural network model to deduce the theoretical prediction value at the current moment, and the temporal residual between the actual sensor observation value and the theoretical prediction value is calculated. By normalizing the temporal residuals with a sensitivity penalty coefficient, a data fidelity index, which characterizes the reliability of the data, is calculated. The index ranges from 0 to 1. Implement a gating and filtering mechanism: only when the calculated data fidelity index is higher than the preset confidence threshold, the current data point is marked as a high-confidence, clean data entity and allowed to be stored in the database; otherwise, it is judged as abnormal noise and removed.

[0011] Furthermore, the blockchain trust anchoring mechanism module adopts a hybrid architecture of "hash on-chain and original data off-chain storage," and its operating logic includes: High-confidence, clean data entities and ecological regulation optimization vectors are extracted in real time, and the current UTC atomic timestamp and device identity authentication ID are appended to assemble and generate traceability data blocks to be uploaded to the blockchain; The SHA-256 hash algorithm is used to perform a one-way encryption operation on the traceability data block. Specifically, a unique digital fingerprint is generated based on the XOR concatenation result of high-confidence pure data entities, ecological regulation optimization vectors, and metadata sets. Finally, the digital fingerprint is broadcast to the distributed consortium blockchain network, and its legitimacy is verified using the Practical Byzantine Fault-Tolerant (PBFT) consensus algorithm. Once verified, it is written into the distributed ledger, and a full-chain trusted traceability certificate containing the block height and transaction hash value is returned.

[0012] Furthermore, within the spatiotemporal semantic holographic fusion engine module, the multi-objective fusion hybrid intelligent decision-making model is a composite computing architecture integrating multiple artificial intelligence algorithms, comprising three sub-models operating in parallel: Water demand prediction sub-model: A BP neural network is used, configured to input time-series soil moisture and meteorological data, to predict the future water demand of crops; Disease identification sub-model: Employs the YOLOv8 algorithm, configured to extract convolutional features from images transmitted back by the UAV to identify disease and pest types; Global optimization sub-model: A genetic algorithm is used as the optimization solver, configured to iteratively calculate the optimal water resource allocation weights based on the above prediction results and the total water resources of the irrigation district. As a further aspect of the present invention:

[0013] The beneficial effects of this invention: (1). This system calculates the entropy value of environmental fluctuations and uses the logistic function to dynamically solve the optimal sampling frequency. Based on the principle of electrochemical corrosion, it uses the exponential decay model to adjust the sensor calibration cycle according to the real-time soil salinity. This achieves the adaptive adjustment of the sampling frequency with environmental fluctuations to accurately capture transient characteristics, as well as automatic compensation for sensor drift in highly corrosive environments. This significantly improves the spatiotemporal accuracy and reliability of long-term monitoring data, thereby making up for the shortcomings of existing technologies that lack a dynamic calibration compensation mechanism based on environmental parameters in highly corrosive environments, resulting in severe zero-point drift of the sensor and distortion of long-term monitoring data, which can mislead decision-making.

[0014] (2). This system automatically calls preset templates to generate a dynamic database topology skeleton by parsing irrigation district parameters, and distributes data to a multi-dimensional heterogeneous storage matrix containing relational, time-series, spatial and graph structures by performing heterogeneous matrix routing mapping. This achieves adaptive initialization of the database architecture and deep integration of multimodal data, effectively breaking down barriers between heterogeneous data and greatly reducing the manual cost of system deployment and maintenance. This makes up for the shortcomings of existing technologies, which rely heavily on manual modeling for database construction, lack an automatic fusion mechanism for multimodal heterogeneous data, and have long deployment cycles and high maintenance costs that restrict the intelligent upgrading of water conservancy projects. Attached Figure Description

[0015] The invention will now be further described with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart of the module of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1: Please see Figure 1 As shown, this invention is an automatic multi-source sensing construction system for integrated water conservancy databases, including a multi-source sensing feature calculation module, a data link encryption and encapsulation module, a self-evolving heterogeneous topology reconstruction center module, a spatiotemporal semantic holographic fusion engine module, and a blockchain trust anchoring mechanism module. The multi-source sensing feature calculation module is configured to perform statistical modeling on the collected irrigation area environmental parameters to calculate the environmental fluctuation entropy value, and run an adaptive algorithm based on the entropy value to dynamically solve the optimal sampling frequency, driving the device to output a discretized raw data stream carrying spatiotemporal labels; The data link encryption and encapsulation module connects to the multi-source sensing feature calculation module, is configured to construct a multi-channel redundant transmission topology, performs differential coding compression and national cryptographic algorithm encryption on the discretized raw data stream, and encapsulates it to generate secure ciphertext data packets; The self-evolving heterogeneous topology reconstruction center module connects to the data link encryption and encapsulation module, and is configured to call preset templates to dynamically generate a dynamic database topology skeleton; it also uses a long short-term memory neural network model to calculate the temporal residuals of the data flow to obtain a data fidelity index, and uses this index to select and generate high-confidence, pure data entities and route them to be written into a multi-dimensional heterogeneous storage matrix; The spatiotemporal semantic holographic fusion engine module, connected to the self-evolving heterogeneous topology reconstruction center module, is configured to project dynamic values ​​from temporal units onto a three-dimensional coordinate system to construct a holographic digital twin mapping; and drives a multi-objective fusion hybrid intelligent decision-making model to deduce and generate ecological regulation optimization vectors using this mapping as input; The blockchain trust anchoring mechanism module connects to the self-evolving heterogeneous topology reconstruction center module and the spatiotemporal semantic holographic fusion engine module. It is configured to generate unique digital fingerprints for high-confidence pure data entities and ecological regulation optimization vectors and write them into the distributed consortium chain ledger to generate a full-link trusted traceability certificate. Communication links are established with soil moisture sensors, hydrological flow meters, and meteorological monitoring stations through a built-in national standard protocol conversion interface to capture real-time data on soil salinity, soil moisture, ambient temperature, and power supply voltage within the irrigation area. The multi-source sensing feature calculation module does not directly forward these raw values. Instead, it first normalizes them and constructs a time sliding window, extracts the temporal change rate of each environmental parameter within the window, establishes an environmental state model using statistical information entropy theory, and calculates the environmental fluctuation entropy value that can characterize the intensity of the current environmental fluctuation. The expression for calculating the entropy value of environmental fluctuations is given by equation (1): (1); In the formula: This represents the calculated environmental fluctuation entropy value; the higher the value, the greater the dispersion and instability of the environmental parameters. This represents the dimension of the i-th environment parameter in the feature vector; This represents the probability proportion of the change in the i-th environmental parameter within the current time window relative to the total change. In the above formula, the probability distribution of the range of change of environmental parameters Obtaining the value of entropy is crucial for calculating its value. The system does not randomly assign this probability; instead, it makes predictions based on the RandomForest model. Specifically, the system pre-loads a random forest classifier trained on historical meteorological and soil moisture data. The model is then fed with currently collected parameters such as salinity and humidity. The model outputs an estimated probability of each parameter fluctuating drastically within a future time window; this estimated probability serves as the basis for its calculation. Substitute the values ​​into the entropy formula for calculation; In this way, the system achieves accurate quantification of environmental uncertainty using machine learning algorithms, thereby enabling adaptive sampling; In obtaining the above environmental fluctuation entropy values Then, the module uses it as a key independent variable input into the adaptive sampling control model; Considering the non-linear mapping relationship between environmental changes and sampling requirements, the module uses a logistic function to construct a smooth transition curve and dynamically calculates the optimal sampling frequency at the current moment. The purpose of this step is to reduce power consumption during periods of stable environmental conditions and to capture transient characteristics during periods of rapid environmental change, thereby outputting control commands that balance energy efficiency and accuracy. The expression for calculating the optimal sampling frequency is given by equation (2): (2); In the formula: This indicates the optimal sampling frequency to be executed at the current moment; This indicates the system's preset minimum sleep sampling frequency; This represents the constant representing the difference between the system's highest burst sampling frequency and its lowest sleep sampling frequency; This represents the environmental fluctuation entropy value calculated in the preceding steps; The shift parameter represents the function and is used to set the sensitivity threshold for triggering high-frequency sampling; The gain coefficient, representing the entropy value, is used to adjust the weighting effect of environmental fluctuations on frequency boost. Furthermore, to address the drift issue caused by sensor probe corrosion in high-salt-alkali environments, the module simultaneously extracts real-time monitored soil salinity data while outputting sampling commands; The module is based on the principle of electrochemical corrosion and uses an exponential decay model to calculate the time interval at which the sensor needs to be calibrated next, i.e., the calibration cycle. This process ensures that the system can automatically shorten maintenance intervals in highly corrosive environments to maintain long-term data reliability; The calculation expression for the calibration period is given by equation (3): (3); In the formula: This indicates the recommended sensor calibration cycle under the current conditions; Indicates the reference calibration cycle under standard laboratory conditions. This represents the real-time monitored values ​​of soil salinity and electrical conductivity. This represents the preset salt and alkali corrosion attenuation coefficient; Finally, the multi-source sensing feature calculation module calculates the optimal sampling frequency based on the solution. The underlying hardware synchronously collects various environmental parameters and generates calibration cycles. As metadata, it is appended to the data packet header, combined with BeiDou positioning coordinates and UTC timestamp, and encapsulated into a discrete raw data stream, which is then sent to the next-level data link encryption and encapsulation module for processing via the internal bus; The data link encryption and encapsulation module receives the discretized raw data stream from the multi-source sensing feature calculation module via an internal high-speed bus; The core task of this module is to establish a highly reliable transmission topology at the edge, and to compress the data volume to the maximum extent without sacrificing accuracy and to ensure communication security through algorithmic means, thereby completing the morphological encapsulation from plaintext to ciphertext packets; First, the module does not send data directly, but instead activates the built-in multi-mode communication unit to build a redundant transmission topology that includes fifth-generation mobile communication technology, narrowband Internet of Things, and long-range radio technology; To select the optimal transmission path at any given moment among multiple parallel links, the module monitors the signal-to-noise ratio and round-trip delay of each physical channel in real time. A quantified channel quality score is then calculated through weighted summation. The system activates only the link with the highest score for data transmission, thereby ensuring the robustness of the transmission link in complex outdoor electromagnetic environments. The calculation expression for the channel quality score is shown in equation (4): (4); In the formula: This represents the calculated channel quality score; a higher value indicates a better channel condition. This represents the real-time monitored channel signal-to-noise ratio (SNR) value. Indicates the round-trip communication delay of the link; Weighting coefficients representing the signal-to-noise ratio, used to emphasize the importance of signal strength; The penalty coefficient represents the latency, used to reduce the probability of a high-latency link being selected; It should be noted that the above channel quality score It is a comprehensive mapping of the physical channel state, and its physical meaning is directly related to the packet loss rate of the transmission link; In this embodiment, the system presets a scoring threshold corresponding to a 5% packet loss rate. ; When the channel score is calculated in real time When the potential packet loss rate of the current link exceeds 5%, the system immediately triggers adaptive channel switching logic, automatically switching to a backup link (such as switching from 5G to LoRaWAN) to ensure the reliability of data transmission. After determining the physical transmission channel, in order to reduce bandwidth consumption and improve transmission efficiency, the module performs edge compression based on differential coding on the input raw data stream. The module no longer transmits the full absolute value, but instead calculates the change increment of the data at the current time based on the sampled value of the previous time step. Since environmental parameters typically exhibit temporal continuity, this differential processing can significantly compress the bit width of data values, thereby achieving a very high proportion of lossless compression. The expression for calculating the differential compression value is given by equation (5): (5); In the formula: This represents the calculated compressed difference value at the current moment, whose storage bit width is much smaller than the original value; This represents the absolute value of the original environmental parameters at the current sampling time; This represents the absolute value of the environment parameters cached at the previous sampling time. Finally, to prevent data from being stolen or tampered with during transmission over the public network, the module calls the domestically developed commercial cryptographic algorithm SM4 to perform block encryption on the compressed differential data. The module uses a preset symmetric key to perform multiple rounds of nonlinear iterative operations on the data block, generating an unreverse-decipherable ciphertext sequence, and then combines it with a channel quality score. The compressed metadata is packaged to generate the final secure encrypted data packet, which is then output to the next-level self-evolving heterogeneous topology reconstruction center. The calculation expression for generating the ciphertext is shown in equation (6): (6); In the formula: This represents the final generated encrypted ciphertext data block; This represents the round function encryption transformation process defined in the SM4 algorithm; This represents a pre-set 128-bit system symmetric master key; This represents the differential compressed data generated in the preceding steps; First, the center decrypts and decompresses the received data packets, and then parses the irrigation area dimension parameters carried within, including crop type, distribution of water conservancy facilities, and topological relationships of monitoring points. The center abandons the traditional method of manually creating database table structures, and instead automatically calls a pre-set domain knowledge template library by parsing the above dimension parameters. For example, for rice crops, the template library has pre-set time series table templates containing fields such as water depth and chlorophyll content. For drip irrigation systems, a pre-set relationship table template including pressure threshold and flow coefficient attributes is provided; Based on the actual physical structure of the irrigation area, the system dynamically calculates and generates a dynamic database topology skeleton that adapts to the current scenario. This process is equivalent to the system algorithm automatically building a "shelf" before the data is stored in the database, based on "what to put in", thus achieving adaptive initialization of the database architecture. The logical expression for generating the topological skeleton is given by equation (7): (7); In the formula: This represents the generated dynamic database topology skeleton structure; This represents the set of input irrigation district dimension parameters; This represents a pre-built domain knowledge template library; This represents an architecture mapping function based on parameter matching. Next, to ensure the purity of the data and prevent contamination of the database by dirty data caused by sensor malfunctions, the center loads a Long Short-Term Memory (LSTM) neural network model to perform deep cleaning on the restored time-series data stream. Unlike traditional fixed threshold filtering, this model learns the temporal patterns of historical data to predict the theoretically reasonable range of the data at the current moment. The center calculates the temporal residual between the actual observed value and the model's predicted value and converts it into a normalized value, namely the data fidelity index. This index intuitively reflects the reliability of the current data point. The calculation expression for the data fidelity index is shown in equation (8): (8); In the formula: This represents the calculated data fidelity index, which ranges from 0 to 1. The closer the value is to 1, the more reliable the data is. This represents the actual observed value transmitted by the sensor at the current moment; This represents the predicted value derived by the LSTM model based on the historical sequence; The sensitivity penalty coefficient represents the residuals and is used to adjust the tolerance for outliers. The system executes a gating filtering mechanism based on this index: only when the calculated fidelity index is... Only when the confidence level exceeds the preset confidence threshold is the data point marked as a high-confidence, clean data entity and allowed to proceed to the next stage; otherwise, the data point will be judged as abnormal noise caused by equipment failure or sudden environmental changes, and will be discarded or marked as awaiting manual review. Finally, the center performs heterogeneous matrix routing and mapping operations. Based on the metadata type characteristics of the pure data entities, the system distributes them to different units of the multidimensional heterogeneous storage matrix through an internal mapping protocol: static device attribute routes are written to relational storage units, high-frequency dynamic monitoring values ​​are written to time-series storage units, geographical location coordinates are written to spatial storage units, and the connection relationships between devices are written to graph structure storage units. The multi-dimensional heterogeneous storage matrix in this embodiment and claims corresponds to a multi-database fusion core architecture in a specific engineering implementation architecture, and the specific correspondence is as follows: Relational storage units: corresponding to MySQL databases, used to store basic structured data such as irrigation area equipment files and farmer information; Time-series storage units: corresponding to the TDengine database, utilizing its high-concurrency write characteristics to store high-frequency monitoring streams such as water level and soil moisture; Spatial storage unit: Corresponding to the PostGIS database, it is used to store geospatial data such as canal vector maps and field boundaries; Graph-structured storage units: corresponding to the Neo4j database, used to store complex topological relationships between water sources, pumping stations, and fields. The system automatically routes cleaned and purified data entities to the specific databases mentioned above through a built-in mapping protocol. The logical expression for storing route mapping is shown in equation (9): (9); In the formula: Indicates the physical write address of the target memory unit; This represents a predefined data type routing function; This step involves labeling the type characteristics carried by data entities. Through this step, the system achieves automatic transformation of unstructured data flow into a structured multimodal knowledge matrix, providing a standardized data foundation for subsequent intelligent decision-making. First, the engine performs a holographic tensor projection operation. The engine does not simply retrieve data, but uses the R-tree spatial indexing algorithm to spatially interpolate and map the high-frequency dynamic monitoring values ​​in the temporal storage unit, using the three-dimensional geographic coordinates in the spatial storage unit as the reference skeleton. The engine establishes a spatiotemporal coupling equation to attach one-dimensional time-series data to the surface of a three-dimensional BIM model, thereby constructing a holographic digital twin mapping body with real-time status feedback. This step gives the originally isolated sensor values ​​a specific physical location and shape in virtual space, realizing the dimensionality upgrade of data from discrete points to holographic volumes. The state calculation expression for the holographic mapping volume is given by equation (10): (10); In the formula: This represents the state matrix of the constructed holographic digital twin mapping at the current moment; This represents the three-dimensional geographic static base coordinates provided by the spatial database; This represents the dynamic environmental monitoring values ​​after spatiotemporal interpolation. Represents the identity matrix constant; This represents the preset spatiotemporal semantic coupling coefficient, used to adjust the weight of the impact of dynamic data on the static model; The multi-objective fusion hybrid intelligent decision-making model is a composite computing architecture that integrates multiple artificial intelligence algorithms. It contains three sub-models that work in parallel: 1. Water demand prediction sub-model: using a BP neural network, inputting time-series soil moisture and meteorological data, to predict the future water demand of crops; Disease identification sub-model: The YOLOv8 algorithm is used to extract convolutional features from images transmitted back by the UAV to identify the types of diseases and pests; Global optimization sub-model: A genetic algorithm is used as the optimization solver. Based on the above prediction results and the total water resources in the irrigation area, the optimal water resource allocation weights are calculated iteratively. The final output ecological regulation optimization vector is the set of calculation results of the above sub-model, which includes precise irrigation instructions (water volume, time), fertilization suggestions and disease prevention and control plans. The solution expression for the control vector is given by equation (11): (11); In the formula: This represents the final output vector of the model for ecological regulation optimization. Each dimension of this vector corresponds to a specific control command (such as valve opening degree or pesticide dosage). This represents the state matrix of the holographic digital twin mapping volume input from the preceding steps; This represents the trained and optimized synaptic weight matrix in a neural network. The bias threshold parameter represents the neuron; Represents the natural constant; Through the above steps, the engine completes the closed-loop calculation from data perception to intelligent decision-making, and the generated ecological regulation optimization vector is then sent to the blockchain trust anchoring mechanism for storage. First, the mechanism performs key element extraction and assembly operations. Instead of directly uploading massive amounts of raw data to the blockchain to avoid congestion of blockchain storage resources, the mechanism adopts a hybrid architecture of "hash on-chain and raw data off-chain storage". The mechanism extracts high-confidence, pure data entities output by the Evolutionary Heterogeneous Topology Reconstruction Center and ecological regulation and optimization vectors output by the spatiotemporal semantic holographic fusion engine in real time. The mechanism uses these two types of core data as "evidence payload", and adds the current UTC atomic timestamp and the unique device identity authentication ID of the edge gateway to form a traceability data block to be uploaded to the chain. Next, in order to generate a unique digital identity card, the mechanism calls the SHA-256 hash algorithm to perform a one-way encryption operation on the aforementioned traceability data block. This operation transforms data input of arbitrary length into a fixed-length encrypted string. Any modification to the original data, even by a single bit, will cause a drastic change in the result, like an avalanche effect. The string calculated by the mechanism is the digital fingerprint, which is the core basis for subsequent authenticity verification. The calculation expression for the digital fingerprint is shown in equation (12): (12); In the formula: This represents a unique digital fingerprint generated through computation; This indicates the system's pre-defined one-way hash function algorithm; This represents a high-confidence, clean data entity as input. This represents the input ecological regulation optimization vector; This represents a collection of metadata containing timestamps and device IDs. This represents the XOR concatenation operation for data bits. The mechanism will then generate a digital fingerprint. The fingerprint is broadcast to a distributed consortium blockchain network composed of nodes from the irrigation district management office, water resources bureau, and agricultural cooperatives. Each node verifies the legality of the fingerprint using a practical Byzantine fault-tolerant consensus algorithm. Once verified, the fingerprint is packaged and written into a timestamped block, forming an immutable distributed ledger record. Ultimately, the mechanism returns a fully trusted traceability certificate containing block height, transaction hash value, and signature information. When a user needs to verify the authenticity of an irrigation instruction, they only need to provide this certificate, and the system can recalculate the hash value and compare it with the on-chain record, thereby realizing a fully trusted traceability process from field perception to cloud decision-making.

[0019] The working principle of this invention is as follows: First, the multi-source sensing feature calculation module executes adaptive sensing logic. The system does not mechanically collect data at fixed intervals, but calculates the environmental fluctuation entropy value that represents the uncertainty of the environment in real time. It uses the logistic function to dynamically solve for the optimal sampling frequency. During periods of drastic environmental changes, it automatically samples at high frequency to capture transient features, and goes into hibernation during stable periods to save energy. At the same time, based on the exponential decay model, it automatically shortens the sensor calibration cycle according to the soil salinity, eliminating drift error from the source.

[0020] Secondly, the data link encryption and encapsulation module executes secure transmission logic. The module calculates channel quality scores in real time to selectively activate transmission links, performs differential coding compression on the data stream to reduce bandwidth usage, and calls the SM4 national cryptographic algorithm to generate secure ciphertext data packets, ensuring absolute data security at the transport layer.

[0021] Next, the self-evolving heterogeneous topology reconstruction center module executes automatic database creation and cleaning logic. The center parses the irrigation area parameters in the data packets, automatically calls the preset template to generate a dynamic database topology skeleton, and realizes adaptive initialization of the storage architecture; it uses the LSTM model to calculate the data fidelity index to remove abnormal noise, and automatically routes and maps the clean data to a multi-dimensional heterogeneous storage matrix composed of MySQL, TDengine, PostGIS and Neo4j, breaking down data silos.

[0022] Subsequently, the spatiotemporal semantic holographic fusion engine module executes intelligent decision-making logic. The engine projects multi-dimensional data to construct a holographic digital twin mapping, driving a hybrid intelligent decision-making model integrating BP neural networks, YOLOv8, and genetic algorithms to perform parallel deduction of water demand prediction and disease identification, and outputting the globally optimal ecological regulation optimization vector.

[0023] Finally, the blockchain trust anchoring mechanism module executes trusted evidence storage logic. The system generates unique digital fingerprints for high-confidence data and control vectors, writes them into the distributed consortium blockchain ledger, and generates immutable, end-to-end trusted traceability certificates, providing legal-level evidence for water rights transactions and audits.

[0024] The foregoing detailed an embodiment of the present invention, but this is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the patent coverage of the present invention.

Claims

1. A water integration database multi-source sensing automatic construction system, characterized in that, The system comprises a multi-source perception feature calculation module, a data link encryption packaging module, a self-evolving heterogeneous topology reconstruction center module, a space-time semantic holographic fusion engine module, and a blockchain trust anchoring mechanism module. The multi-source perception feature calculation module is configured to statistically model the collected irrigation area environmental parameters to calculate environmental fluctuation entropy values, and to run an adaptive algorithm based on the entropy values to dynamically solve the optimal sampling frequency, driving the device to output discrete raw data streams carrying space-time labels. The data link encryption packaging module is connected to the multi-source perception feature calculation module and is configured to build a multi-channel redundant transmission topology, perform differential encoding compression and national encryption algorithm encryption on the discrete raw data streams, and package to generate secure ciphertext data packets. The self-evolving heterogeneous topology reconstruction center module is connected to the data link encryption packaging module and is configured to dynamically generate a dynamic database topology skeleton by calling a preset template; and to use a long short-term memory neural network model to solve the time sequence residual of the data stream to obtain a data fidelity index, based on which high-confidence pure data entities are generated and routed and mapped to a multi-dimensional heterogeneous storage matrix. The space-time semantic holographic fusion engine module is connected to the self-evolving heterogeneous topology reconstruction center module and is configured to project dynamic values in the time series type unit into a three-dimensional coordinate system to construct a holographic digital twin mapping body. And drive a multi-target fusion hybrid intelligent decision-making model to generate an ecological regulation and control optimization vector based on the mapping body as input. The blockchain trust anchoring mechanism module is connected to the self-evolving heterogeneous topology reconstruction center module and the space-time semantic holographic fusion engine module and is configured to generate a unique digital fingerprint for the high-confidence pure data entities and the ecological regulation and control optimization vector and write it into a distributed consortium chain account book to generate a full-link trusted traceability certificate.

2. The integrated water database multi-source perception automatic building system according to claim 1, characterized in that, The operation logic of the multi-source perception feature calculation module includes: A time sliding window is constructed for the current collection time, the normalized time series change rate of each environmental parameter in the window is extracted, a preset random forest model is used to predict the fluctuation probability distribution of the time series change rate, and the probability distribution is substituted into the information entropy formula to calculate the environmental fluctuation entropy value representing environmental uncertainty. The environmental fluctuation entropy value is input into the adaptive sampling control model, a logistic function is used to construct a non-linear mapping curve, and the optimal sampling frequency is dynamically solved, so that the optimal sampling frequency increases in an S-shaped non-linear manner as the environmental fluctuation entropy value increases, to capture transient characteristics during environmental changes. Based on the principle of electrochemical corrosion, the real-time monitored soil salinity data is processed using an exponential decay model to calculate the next calibration period of the sensor, so that the calibration period is exponentially shortened as the soil salinity value increases, to compensate for sensor drift in high corrosion environments. The bottom layer hardware collects data according to the calculated optimal sampling frequency, and the calibration period is used as metadata, combined with Beidou positioning coordinates and UTC time stamps, to package and generate discrete raw data streams.

3. The integrated water database multi-source perception automatic building system according to claim 1, characterized in that, The operation logic of the data link encryption packaging module includes: A redundant transmission topology comprising the fifth generation mobile communication technology, narrowband Internet of Things and long-range radio technology is constructed, the signal-to-noise ratio and the round-trip delay of each physical channel are detected in real time, the signal-to-noise ratio is weighted according to the weight coefficient and the delay is weighted according to the penalty coefficient, the quantified channel quality score is calculated, and the link with the highest score is activated for data transmission; The channel quality score is directly related to the packet loss rate of the transmission link, and the system presets a score threshold corresponding to a 5% packet loss rate. When the real-time calculated channel quality score is lower than the threshold, the adaptive channel switching logic is automatically triggered to schedule to the standby link; The edge compression operation based on differential coding is performed on the input discrete raw data stream. The differential increment of the current time data is calculated by subtracting the absolute value of the environmental parameter at the last time from the absolute value of the current time environmental parameter. The time sequence continuity feature of the environmental parameter is used to reduce the data bit width, and lossless compression is realized; The domestic commercial cipher algorithm SM4 is called to group encrypt the differential increment, and the preset system symmetric master key is used for multi-round nonlinear iteration operation on the data block to generate a ciphertext sequence that cannot be reverse cracked, and the ciphertext sequence is packed with the channel quality score and the compressed metadata to generate a secure ciphertext data packet.

4. The integrated water database multi-source perception automatic building system according to claim 1, characterized in that, The running logic of the self-evolving heterogeneous topology reconstruction center module includes: Decrypting and restoring the secure ciphertext data packet and parsing the irrigation area dimension parameters, automatically calling the preset domain knowledge template library, dynamically matching and generating a time sequence table or a relationship table template containing specific fields according to the crop type or water conservancy facility attribute, constructing a dynamic database topology skeleton adapted to the current scene, and realizing adaptive initialization of the database architecture; Performing heterogeneous matrix routing mapping through an internal mapping protocol to distribute data to a multi-dimensional heterogeneous storage matrix, which is specifically composed of the following units: MySQL relational database for storing basic archives, TDengine time series database for storing high-frequency monitoring streams, PostGIS spatial database for storing geographic vector data, and Neo4j graph structure database for storing topology associations.

5. The integrated water database multi-source perception automatic building system according to claim 1, characterized in that, The self-evolving heterogeneous topology reconstruction center module is configured to execute a deep cleaning logic based on a long short-term memory neural network model: The long short-term memory neural network model is used to learn the time sequence rules of historical data to deduce the theoretical prediction value at the current time, and calculate the time sequence residual error between the actual observation value of the sensor and the theoretical prediction value; The time sequence residual error is normalized by combining the sensitivity penalty coefficient to calculate the data fidelity index representing the data confidence level, which ranges from 0 to 1; Perform a gating filtering mechanism: only when the calculated data fidelity index is higher than the preset confidence threshold, mark the current data point as high-confidence pure data entity and allow it to be stored in the database, otherwise, it is determined as an abnormal noise point and is excluded.

6. The integrated water database multi-source perception automatic building system according to claim 1, characterized in that, The blockchain trust anchoring mechanism module adopts a hybrid architecture of "hashing on-chain and original data off-chain storage", and its running logic includes: Extract high-confidence, pure data entities and ecological regulation optimization vectors in real time, attach the current UTC atomic timestamp and device identity authentication ID, and assemble them to generate traceability data blocks to be uploaded to the blockchain; The SHA-256 hash algorithm is called to perform a one-way encryption operation on the traceability data block. Specifically, a unique digital fingerprint is generated based on the XOR concatenation result of high-confidence pure data entities, ecological regulation optimization vectors, and metadata sets. Finally, the digital fingerprint is broadcast to the distributed consortium blockchain network, and its legitimacy is verified using the Practical Byzantine Fault-Tolerant (PBFT) consensus algorithm. Once verified, it is written into the distributed ledger, and a full-chain trusted traceability certificate containing the block height and transaction hash value is returned.

7. The integrated water database multi-source perception automatic building system according to claim 1, characterized in that, In the spatiotemporal semantic holographic fusion engine module, the multi-objective fusion hybrid intelligent decision-making model is a composite computing architecture that integrates multiple artificial intelligence algorithms, including three sub-models that work in parallel: Water demand prediction sub-model: A BP neural network is used, configured to input time-series soil moisture and meteorological data, to predict the future water demand of crops; Disease identification sub-model: The YOLOv8 algorithm is used to extract convolutional features from images transmitted back by the UAV and identify the types of diseases and pests. Global optimization sub-model: A genetic algorithm is used as the optimization solver, configured to iteratively calculate the optimal water resource allocation weights based on the above prediction results and the total water resources of the irrigation area.

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