An ecological monitoring data intelligent acquisition and processing method

Abstract

The application discloses an ecological monitoring data intelligent acquisition and processing method, and particularly relates to the technical field of ecological monitoring data processing. The method collects water quality parameters to calculate characteristic coefficients, generates monitoring mode instructions through table lookup mapping, generates energy consumption constraint sampling configuration in combination with battery voltage, screens nodes by using fluid dynamics vectors and elliptical diffusion equations, establishes a dynamic topology of an event influence domain, creates an independent object storage bucket for a wave node, and writes data through path redirection and DMA channel convergence. The application adaptively switches monitoring modes according to multi-parameter characteristics, locks actual diffusion ranges by using fluid dynamics models, eliminates blind areas, realizes energy consumption hierarchical control based on voltage sensing, guarantees endurance, solves the problems of write blocking and heterogeneous signal misplacement under high-frequency concurrency through independent storage and time sequence alignment mechanisms, and provides a complete evidence chain with high space-time coupling for pollution tracing.

Description

Technical Field

[0001] This invention relates to the field of ecological monitoring data processing, and in particular to an intelligent method for collecting and processing ecological monitoring data. Background Technology

[0002] The field of ecological monitoring data processing technology encompasses the acquisition, organization, storage, analysis, and management of various environmental element data in natural ecosystems. Its core lies in the objective recording and data-driven representation of ecological and environmental elements, covering aspects such as the layout of ecological monitoring points, selection of monitoring objects, data collection methods, data transmission paths, data storage structures, data processing workflows, and data quality control. This technical field typically involves the continuous or periodic collection of ecologically relevant information such as meteorological elements, water body elements, soil elements, and biological elements, and the centralized management of multi-source data through unified data formats, time identifiers, and spatial identifiers to support the standardized and systematic implementation of ecological monitoring work.

[0003] Among them, the intelligent collection and processing method of ecological monitoring data refers to a specific technical solution formed around the data acquisition and organization process of ecological monitoring objects. This method addresses the technical matters involved in the ecological monitoring process, such as data collection at monitoring points, data output from monitoring equipment, data aggregation, and data processing. It adopts the method of deploying environmental parameter collection devices with sampling functions at monitoring points to collect ecological monitoring data, including temperature, humidity, precipitation, water level, turbidity, and vegetation coverage. The method collects and records the raw data by setting a fixed sampling period and data identification rules, and then judges the completeness and rationality of the collected data according to the preset data verification rules. At the same time, it classifies, organizes, and stores data from different monitoring points at different times according to a unified data structure to form a standardized ecological monitoring data set that can be used for subsequent ecological monitoring management.

[0004] Existing ecological monitoring technologies mainly suffer from problems such as fixed sampling modes, static network topologies, lack of energy management, and inefficient storage architecture. Specifically, they lack adaptive mode switching capabilities based on multi-parameter correlation characteristics, resulting in low monitoring targeting and resource utilization; they ignore the impact of fluid dynamics on pollutant diffusion paths, causing a disconnect between the effective monitoring range and the actual affected area; they have not established energy consumption constraints and degradation mechanisms based on voltage sensing, making them prone to system paralysis due to overload when energy is scarce; and when dealing with high-frequency flows of sudden events, the lack of physical isolation channels and strict timing alignment can easily lead to write blocking and spatiotemporal index misalignment of heterogeneous data. Summary of the Invention

[0005] The main objective of this invention is to provide an intelligent method for acquiring and processing ecological monitoring data. By employing technologies such as multi-parameter feature mapping adaptive sampling, fluid dynamics dynamic topology networking, voltage sensing energy consumption constraint control, and spatiotemporal co-location of multi-source heterogeneous data, this invention effectively solves problems in existing ecological monitoring, such as fixed sampling modes with poor targeting, disconnect between monitoring range and actual diffusion path, lack of equipment endurance management, and blockage and timing misalignment of sudden high-frequency data access.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for intelligent acquisition and processing of ecological monitoring data, the method specifically includes: The first characteristic coefficient is obtained by collecting real-time values ​​of dissolved oxygen, conductivity and turbidity at monitoring points at fixed intervals and calculating the absolute value of the difference. The second characteristic coefficient is obtained by calculating the ratio of the dissolved oxygen difference value to the conductivity difference value. A preset parameter lookup table is used to map the first and second feature coefficients to the parameter lookup table, lock the logical block and retrieve the corresponding monitoring and control index, and generate biological monitoring mode switching instruction code. Based on the biological monitoring mode switching instruction code, the sampling frequency and gain are retrieved, the battery voltage value is compared with the preset threshold and the parameter items are locked to generate an energy consumption constraint sampling configuration parameter package. Based on the timestamp of the energy consumption constraint sampling configuration parameter package, the environmental flow field sensor is called to obtain real-time fluid dynamic vector data. The geometric parameters of the coverage area are calculated using the elliptical diffusion equation. The latitude and longitude coordinates of the subordinate nodes are substituted into the equation for verification. Coordinates that meet the constraint conditions are selected to establish a dynamic topology set of the event influence domain. A UUID tag is generated for each node in the dynamic topology set of the event-affected domain. A write path redirection instruction is sent to the storage controller to aggregate the data streams of subsequent nodes and write them to an independent object storage bucket named with the tag, thus obtaining a multi-source heterogeneous spatiotemporal co-track data storage space.

[0007] Preferably, the specific calculation process of the first characteristic coefficient is as follows: according to the current dissolved oxygen, conductivity and turbidity values ​​of the collection point at a fixed period, retrieve the corresponding historical values ​​of the previous period, perform subtraction operation between the current value and the historical value for each of the three parameters, take the absolute value of the deviation value obtained by the calculation, extract the conductivity difference value and set it as the dividend, select the turbidity difference value and set it as the divisor, perform division operation to calculate the ratio between the two, and obtain the first characteristic coefficient; More preferably, the specific calculation process of the second characteristic coefficient is as follows: extract the dissolved oxygen difference value and the conductivity difference value, perform a division operation of the dissolved oxygen difference value and the conductivity difference value to obtain the second characteristic coefficient.

[0008] Preferably, the parameter lookup table divides the numerical range of the first characteristic coefficient and the second characteristic coefficient into several rectangular logical blocks, and each block is uniquely bound to a specific set of device control instruction codes.

[0009] Preferably, the first and second feature coefficients are mapped to the parameter lookup table, the logical block is locked and the corresponding monitoring and control index is retrieved, and biological monitoring mode switching instruction code is generated, specifically including: The first feature coefficient is set as the horizontal axis coordinate value, and the second feature coefficient is set as the vertical axis coordinate value. The boundary range of each rectangular logical block divided in the parameter lookup table is traversed. The coordinate point value is compared with the horizontal and vertical boundary thresholds of each block to determine the specific geometric area where the coordinate point falls and obtain the target logical block location identifier. The system retrieves control logic configuration items that establish mapping relationships in the parameter lookup table, traverses the monitoring mode index data stored in the database, matches index entries that match the location identifier of the target logic block, and extracts numerical pointers from the entries that point to specific hardware operating states. The microprocessor's internal instruction set mapping library is invoked to query the machine-executable instruction sequence corresponding to the numerical pointer. The sequence is then encapsulated according to the communication protocol format to construct a complete data packet containing a frame header, instruction content, and check bits, thereby obtaining the biological monitoring mode switching instruction code.

[0010] Preferably, the step of retrieving the sampling frequency and gain according to the biological monitoring mode switching instruction code, comparing the battery voltage value with a preset threshold and locking the parameter items, and generating an energy consumption constraint sampling configuration parameter package includes: The parameter lookup table is retrieved according to the biological monitoring mode switching instruction code, the storage address mapped to the code is locked, the sampling frequency value, the signal gain coefficient value and the single trigger recording duration value are read, and the read multiple values ​​are queued according to the register writing order to generate the target mode operation parameters. Collect real-time battery voltage values ​​from monitoring nodes, call preset high-power mode voltage thresholds, perform a comparison operation between the real-time voltage value and the voltage threshold, calculate the difference between the two and determine the positive or negative attribute of the difference to determine whether the voltage is sufficient. When the voltage is sufficient, the target mode operating parameters are selected; when the voltage is insufficient, the preset low-power counting mode parameters are called, the selected parameter set is converted into a binary instruction stream that conforms to the communication protocol, and an energy consumption constraint sampling configuration parameter package is generated.

[0011] Preferably, the preset low-power counting mode parameters are as follows: A configuration instruction set pre-stored in the device's non-volatile memory and used to rewrite the device control register includes: a power-off control mask for disconnecting the power supply circuit between the high-bandwidth visual imaging module and the acoustic transducer; an external interrupt trigger enable bit for activating the passive infrared detector or photoelectric blocking sensor; a low-frequency division factor for switching the microprocessor's main clock frequency to maintain a minimum standby state; and a data processing rule for configuring the arithmetic logic unit to perform integer value accumulation only on the rising edge of the sensor trigger signal without waveform sampling. More preferably, when the preset low-power counting mode parameters are executed, the monitoring device is forced to stop collecting timing waveform data and only outputs the trigger count statistics within the period.

[0012] Preferably, the step of calling the environmental flow field sensor to obtain real-time fluid dynamic vector data based on the timestamp of the energy consumption constraint sampling configuration parameter package includes: The system collects instantaneous water velocity, flow direction angle, and wind speed values ​​at the environmental flow field sensor port. It performs a multiplication operation between the velocity value and a preset time extrapolation step size and quantifies the unidirectional migration distance of the fluid. It then performs vector correction on the flow direction angle value by combining the weighting factor corresponding to the wind speed value. Finally, it synthesizes the migration distance and the corrected flow direction angle to construct a geometric vector model that characterizes the fluid transport trend and generates real-time fluid dynamic vector data. More preferably, environmental flow field sensors include, but are not limited to, flow meters, anemometers, and integrated weather stations.

[0013] Preferably, the step of calculating the geometric parameters of the coverage area using the elliptic diffusion equation, substituting the latitude and longitude coordinates of the subordinate nodes into the equation for verification, filtering coordinates that meet the constraints, and establishing a dynamic topology set of the event influence domain includes: Based on real-time fluid dynamic vector data, the vector direction is mapped to the rotation angle of the major axis of an ellipse in the coordinate system. The lengths of the major and minor axes of the ellipse coverage area are calculated based on the vector magnitude and the lateral diffusion ratio. The data of the major and minor axes are substituted into the standard ellipse equation to construct a two-variable quadratic inequality function, quantify the closed geometric diffusion range of the fluid under the current momentum, and obtain the geometric boundary of the coverage area. The system iterates through the storage module to read the longitude and latitude coordinates of all subordinate nodes, substitutes the coordinates of each node into a two-variable quadratic inequality function to perform algebraic calculations, determines whether the coordinate points fall within the geometric boundary based on the calculation results, selects node device identifiers that meet the mathematical constraints from the network list, and establishes a dynamic topology set of the event influence domain.

[0014] Preferably, the independent object storage bucket is established in the following way: Collect the current system's nanosecond-level timestamp value and perform a hash combination operation with the master node's hardware serial number to generate a unique event identifier string. Divide a continuous blank storage area in the underlying disk array of object storage, map the identifier to a storage read / write index, and establish an independent object storage bucket.

[0015] Preferably, sending a write path redirection instruction to the storage controller to aggregate and write subsequent node data streams to an independent object storage bucket named with that label includes: Based on the independent object storage bucket, a write path redirection instruction is sent to the storage controller, the register status of the affected nodes in the topology set is traversed and retrieved, a control message containing the target physical address and direct memory access parameters is constructed, the write process pointing to the regular database is forcibly suspended and the data transmission pointer is corrected. Activate the direct memory access channel of each monitoring node, aggregate and write the data stream of subsequent nodes to an independent object storage bucket, align dissolved oxygen and conductivity values ​​according to millisecond-level timestamp values, and encapsulate multiple heterogeneous signals into the same continuous logical storage space in a parallel pipeline mode to obtain a multi-source heterogeneous spatiotemporal co-track data storage space.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention establishes a mapping relationship between water quality change characteristics and monitoring modes by calculating the characteristic coefficients of water quality parameter difference values. Compared to traditional fixed-period data acquisition, this method can autonomously lock logical blocks and switch monitoring commands based on real-time dynamic changes in the aquatic environment (such as the relative fluctuations in dissolved oxygen, conductivity, and turbidity). This allows the system to reduce load when water quality is stable, and automatically match the optimal sampling frequency and gain when abnormal fluctuations are detected, significantly improving the targeting and intelligence level of ecological monitoring.

[0017] This invention introduces flow velocity and anemometers to obtain real-time fluid dynamic vectors and uses a mathematical model to quantify the actual migration and diffusion range of pollutants (elliptical geometric boundaries). By substituting the coordinates of subordinate nodes in the network into the equation for verification, the set of nodes truly within the event's influence domain can be dynamically selected. This mechanism breaks through the limitations of traditional monitoring based on fixed geographical radii, ensuring that monitoring resources are concentrated on the areas where the pollution plume actually flows, effectively eliminating redundant data from invalid nodes, and improving the spatial accuracy of tracking sudden environmental events.

[0018] This invention incorporates a battery voltage status determination step before generating the sampling configuration, enabling dynamic strategy adjustment based on remaining energy. When the high-energy-consumption mode cannot be maintained, the system can automatically degrade to a low-power counting mode. By cutting off power to high-bandwidth modules and reducing the main frequency, it retains the ability to statistically analyze triggered events even under extreme conditions. This design maximizes the battery life of passive field devices while ensuring that critical data is not lost, solving the problem of complete monitoring blind spots caused by the depletion of power in traditional devices.

[0019] This invention achieves physically isolated storage of abnormal event data by creating independent object buckets and redirecting write paths. Utilizing Direct Memory Access (DMA) technology and a parallel pipelined model, the system bypasses conventional database write bottlenecks, achieving high-speed data throughput. More importantly, through a strict timestamp alignment mechanism, different types of heterogeneous signals (such as chemical and physical indicators) are encapsulated in the same continuous logical space, eliminating timing biases from multi-sensor acquisition and providing a high-quality, spatiotemporally coupled data foundation for subsequent pollution source tracing and diffusion analysis. Attached Figure Description

[0020] Figure 1 This is an exemplary flowchart illustrating an intelligent collection and processing method for ecological monitoring data, as shown in some embodiments of the present invention. Detailed Implementation

[0021] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the linguistic context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0022] It should be understood that the terms "system," "apparatus," "unit," and / or "module" used in this specification are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0023] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or such terms do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "include" and "contain" only indicate that they include the expressly identified steps and elements, and these steps and elements do not constitute an exclusive list, as the method or apparatus may also include other steps or elements.

[0024] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0025] The intelligent collection and processing method for ecological monitoring data provided in the embodiments of this specification will be described in detail below with reference to the accompanying drawings.

[0026] Figure 1 This is an exemplary flowchart of an intelligent acquisition and processing method for ecological monitoring data according to some embodiments of this specification. In some embodiments, the intelligent acquisition and processing method for ecological monitoring data can be executed by processing logic, which may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to execute hardware simulations), and any combination thereof. In some embodiments, Figure 1 One or more operations in the flowchart of the intelligent acquisition and processing method for ecological monitoring data shown can be implemented by processing equipment and / or terminal equipment. For example, the intelligent acquisition and processing method for ecological monitoring data can be stored in a storage device in the form of computer programs and / or instructions, and can be invoked and / or executed by processing equipment and / or terminal equipment.

[0027] like Figure 1 As shown, in some embodiments of the present invention, the intelligent acquisition and processing method for ecological monitoring data includes steps 1-5, specifically: Step 1: Collect real-time values ​​of dissolved oxygen, conductivity and turbidity at monitoring points at fixed intervals and calculate the absolute value of the difference to obtain the first characteristic coefficient. Calculate the ratio of the dissolved oxygen difference value to the conductivity difference value to obtain the second characteristic coefficient. Step 2: Preset parameter lookup table, map the first and second feature coefficients to the parameter lookup table, lock the logical block and retrieve the corresponding monitoring and control index, and generate biological monitoring mode switching instruction code; Step 3: Retrieve the sampling frequency and gain according to the biological monitoring mode switching instruction code, compare the battery voltage value with the preset threshold and lock the parameter items to generate an energy consumption constraint sampling configuration parameter package; Step 4: Based on the timestamp of the energy consumption constraint sampling configuration parameter package, call the environmental flow field sensor to obtain real-time fluid dynamic vector data, use the elliptical diffusion equation to calculate the geometric parameters of the coverage area, substitute the latitude and longitude coordinates of the subordinate nodes into the equation for verification, filter the coordinates that meet the constraint conditions, and establish a dynamic topology set of the event influence domain. Step 5: Generate UUID tags for nodes within the dynamic topology set of the event-affected domain, send a write path redirection command to the storage controller, and aggregate and write subsequent node data streams to an independent object storage bucket named with the tag, thus obtaining a multi-source heterogeneous spatiotemporal co-track data storage space.

[0028] In some embodiments of the present invention, step 1 involves collecting real-time values ​​of dissolved oxygen, conductivity, and turbidity at the detection points at fixed intervals.

[0029] In practice, this can be achieved through integrated multi-parameter water quality monitoring probes or discrete sensor arrays. To meet the long-term monitoring needs of the field ecosystem, each sensor can be connected to the main controller's multi-channel RS-485 communication bus interface via waterproof aviation connectors, using the Modbus-RTU standard protocol for data exchange. Alternatively, other mature existing technologies can be used to collect real-time values ​​of dissolved oxygen, conductivity, and turbidity at the monitoring points; however, this embodiment only requires standardized values.

[0030] Based on the real-time values ​​of dissolved oxygen, conductivity, and turbidity collected from the monitoring points, the first and second characteristic coefficients were calculated using the following method: The specific calculation process of the first characteristic coefficient is as follows: According to the current dissolved oxygen, conductivity and turbidity values ​​of the collection point at a fixed cycle, retrieve the corresponding historical values ​​of the previous cycle, perform the subtraction operation of the current value minus the historical value for each of the three parameters, take the absolute value of the deviation value obtained by the calculation, extract the conductivity difference value and set it as the dividend, select the turbidity difference value and set it as the divisor, perform the division operation to calculate the ratio of the two, and obtain the first characteristic coefficient; For example, the main control microprocessor first responds to the preset timer interrupt signal and sends a read command to the multi-parameter water quality sensor connected to the RS-485 bus via the Modbus-RTU communication protocol. It sequentially reads real-time dissolved oxygen data from register address 0x0002, conductivity data from address 0x0006, and turbidity data from address 0x000A. The received hexadecimal message is then converted into a 32-bit floating-point number according to the IEEE 754 standard. Assuming the timestamp of the current acquisition period T is 10:00, the parsed real-time values ​​are: dissolved oxygen 7.20 mg / L, conductivity 520.00 S / cm, and turbidity... At 18.50 NTU, the processor immediately invokes the direct memory access channel to access a specified sector of the off-chip non-volatile ferroelectric memory, with an address range of 0x0800 to 0x080F. It retrieves historical values ​​stored from the previous acquisition cycle at 09:50, corresponding to a historical dissolved oxygen of 7.50 mg / L, a historical conductivity of 450.00 S / cm, and a historical turbidity of 15.00 NTU. The arithmetic logic unit loads the current conductivity of 520.00 into accumulator A and the historical conductivity of 450.00 into accumulator B, executing a floating-point subtraction instruction SUBF32 to calculate the original conductivity deviation of +70.00 S / cm. The processor... The sign bit of the result is read. If the sign bit is 1, a two's complement operation (bitwise inversion and addition) is performed. If the sign bit is 0, the original value is retained. Here, the sign bit is 0, so the absolute value is directly locked as 70.00. Simultaneously, the arithmetic logic unit loads the current turbidity (18.50) and the historical turbidity (15.00) into the register group, performs a subtraction operation to obtain the turbidity deviation value +3.50NTU. Similarly, the sign bit is checked and the absolute value operation instruction ABS is executed to confirm that the absolute magnitude of the turbidity change is 3.50. At this point, the processor calls the numerical comparator to compare the absolute value of the turbidity difference (3.50) with the preset minimum divisor reference value (0.01), which is set to the sensor photodiode. Under dark current, the equivalent noise drift limit is set to 0.01 if the turbidity difference is less than 0.01 to avoid the calculation error of dividing by zero. Here, 3.50 is greater than 0.01, so the original value of 3.50 is kept. The processor then configures the operands of the division unit, sets the absolute value of the conductivity difference 70.00 as the dividend, and sets the absolute value of the turbidity difference 3.50 as the divisor. The floating-point division instruction DIVF32 is started to perform the operation of dividing 70.00 by 3.50. The output result of the arithmetic unit is 20.0000. The processor truncates the result into a fixed-point number format with four decimal places and stores it in the first characteristic coefficient temporary storage register R1 to obtain the first characteristic coefficient. The specific calculation process for the second characteristic coefficient is as follows: extract the dissolved oxygen difference value and the conductivity difference value, perform a division operation of the dissolved oxygen difference value and the conductivity difference value to obtain the second characteristic coefficient.

[0031] Building upon the aforementioned example, the microprocessor directly accesses the temporary variable area of ​​the internal static random access memory (SRAM) to extract the original difference data with positive and negative signs calculated in the preceding steps. These are the dissolved oxygen difference value and the conductivity difference value. Based on the data from the aforementioned embodiment, the dissolved oxygen difference value is calculated by subtracting the historical value of 7.50 from the current value of 7.20. After the arithmetic logic unit performs this subtraction operation, the negative flag bit N in the status register is set to 1, generating a signed floating-point number -0.30 mg / L, representing the decreasing trend of dissolved oxygen content in the water. Simultaneously, the original conductivity difference value of +70.00 S / cm is extracted. The processor first performs a validity determination on the conductivity difference value, calling the preset conductivity fluctuation threshold value of 0.5 S / cm. This threshold value is taken from three times the standard deviation of the static repeatability error measured by the four-electrode conductivity probe in the constant temperature water bath calibration experiment. The processor compares the magnitude of +70.00 with 0.5 to confirm the difference. Significant and non-random electronic noise interference was detected. Subsequently, the dissolved oxygen difference value of -0.30 was loaded into the numerator register of the floating-point arithmetic unit, and the conductivity difference value of +70.00 was loaded into the denominator register. The processor started the division logic circuit. First, an XOR operation was performed on the sign bits of the two operands. The sign bit of the numerator was 1 and the sign bit of the denominator was 0. The XOR result was 1, and the sign of the final quotient was determined to be negative. Then, a division operation was performed on the mantissa to calculate the value of 0.30 divided by 70.00. The arithmetic unit output an intermediate result of approximately 0.0042857. The processor called the rounding instruction ROUND to round the result to five significant decimal places according to the preset precision control value, resulting in the value 0.00429. Combined with the sign bit, the final value of -0.00429 was synthesized. This value reflects the dissolved oxygen consumption rate corresponding to the unit increase in conductivity. The processor wrote the calculation result into the second characteristic coefficient temporary storage register R2 to obtain the second characteristic coefficient.

[0032] In another embodiment of the present invention, step 2 specifically includes sub-steps 21-24: Sub-step 21: First, establish a parameter lookup table. It should be noted that the parameter lookup table divides the numerical range of the first characteristic coefficient and the second characteristic coefficient into several rectangular logical blocks, and each block is uniquely bound to a specific set of device control instruction codes. For example, a parameter lookup table is first established. A two-dimensional array space is allocated in a specific address segment of the microcontroller's on-chip Flash memory as the physical carrier of the parameter lookup table. The horizontal dimension corresponds to the variation domain of the first characteristic coefficient, and the vertical dimension corresponds to the variation domain of the second characteristic coefficient. Based on the typical proportional distribution characteristics of dissolved solids and suspended particulate matter in the aquatic environment, the first segmentation threshold of the horizontal dimension is set to 2.0 and the second segmentation threshold is set to 8.0, thereby mathematically dividing the numerical range of the first characteristic coefficient into intervals A[0,2.0), B[2.0,8.0), and C[8.0,+). At the same time, based on the coupling relationship between oxygen consumption rate and ion concentration change, the zero-point threshold of 0.0 and the negative extreme value threshold of -0.5 are set for the vertical dimension, dividing the numerical range of the second characteristic coefficient into intervals representing... The oxygen-consuming interval D [-0.5, 0.0) and the interval E [0.0, +) representing reoxygenation or other conditions are orthogonally divided into six independent rectangular logical blocks on a two-dimensional plane. Each rectangular logical block is assigned a unique 8-bit binary address code as a block ID. For example, the rectangular area formed by the intersection of interval B and interval D is marked as ID_BD. A set of device control instruction codes for specific water quality anomaly scenarios is written. This code includes configuration words for sampling frequency setting, sensor gain level, and data upload cycle. This set of device control instruction codes is written into the EEPROM storage unit corresponding to the ID_BD address code one by one, completing the binding operation of all rectangular logical blocks with specific device control instruction codes. Each block is uniquely bound to a specific set of device control instruction codes.

[0033] Sub-step 22: Set the first feature coefficient as the horizontal axis coordinate value and the second feature coefficient as the vertical axis coordinate value. Traverse the boundary range of each rectangular logical block divided in the parameter lookup table, compare the coordinate point value with the horizontal and vertical boundary thresholds of each block, determine the specific geometric area where the coordinate point falls, and obtain the target logical block location identifier. A further example is given: The first feature coefficient is set as the horizontal axis coordinate, and the second feature coefficient is set as the vertical axis coordinate. The specific value of the first feature coefficient, 3.5, calculated in the previous steps, is used as the horizontal coordinate of the point to be determined. The specific value of the second feature coefficient, -0.02, calculated in the previous steps, is used as the vertical coordinate of the point to be determined. The logic block traversal pointer is initialized to point to the starting memory address of the parameter lookup table. The four boundary threshold data of each preset rectangular logic block are read sequentially. First, the horizontal left boundary value, horizontal right boundary value, vertical lower boundary value, and vertical upper boundary value of the first logic block are extracted, and a numerical comparison is performed. The system performs a calculation to determine whether a value is greater than or equal to and less than a value. In the current example, the system compares the first feature coefficient 3.5 with the boundary value of interval B [2.0, 8.0), confirms that the value is true, and determines that the horizontal coordinate falls within interval B. Then, it compares the second feature coefficient -0.02 with the boundary value of interval D [-0.5, 0.0), confirms that the value is true, and determines that the vertical coordinate falls within interval D. Combining the results of the horizontal and vertical coordinate determinations, the system confirms that the coordinate point is strictly located within the geometric region orthogonally enclosed by intervals B and D. The system reads the unique location index label defined in the lookup table metadata for this geometric region and obtains the target logical block location identifier. Sub-step 23: Retrieve the control logic configuration items that establish mapping relationships in the parameter lookup table, traverse the monitoring mode index data stored in the database, match the index entries that are consistent with the target logic block location identifier, and extract the numerical pointers in the entries that point to the specific hardware working status. The system retrieves control logic configuration items that establish mapping relationships in the parameter lookup table. Using the obtained target logic block location identifier ID_BD as the primary key for the database query, it accesses the monitoring mode index table stored in the embedded database. This index table is organized and stored using a B+ tree structure. The system scans the key-value columns in the index table row by row, performs string matching operations, and performs a bitwise XOR comparison between the primary key ID_BD and the identifier in the index entry. When the XOR result is all zeros, a successful match is confirmed, and the corresponding index entry row is locked. The data field content in this entry row is read. This field stores a 32-bit memory address pointer to a specific segment of the microprocessor program memory, such as 0x0800F400. This pointer value directly corresponds to the pre-compiled driver function entry address or the first address of the parameter configuration table for the specific hardware operating state of dissolved pollution accompanied by oxygen consumption. The read 32-bit memory address pointer is temporarily stored in a general-purpose register, and the numerical pointer pointing to the specific hardware operating state is extracted from the entry. Sub-step 24: Call the microprocessor's internal instruction set mapping library, query the machine-executable instruction sequence corresponding to the numerical pointer, encapsulate the sequence according to the communication protocol format, construct a complete data packet containing a frame header, instruction content, and check bit, and obtain the biological monitoring mode switching instruction code.

[0034] In this sub-step, the microprocessor's internal instruction set mapping library is invoked. The pointer value 0x0800F400 temporarily stored in the register is used as an offset input to the base address arithmetic unit of the instruction set mapping library. The corresponding machine-level configuration sequence is located in the mapping library, and the hexadecimal opcodes contained in the sequence are read. For example, instruction byte 0x40 (corresponding to 64Hz) representing sampling frequency adjustment, instruction byte 0x04 (corresponding to 4x gain) representing gain setting, and instruction byte 0x01 (corresponding to infrared trigger enable) are read according to the industrial control private communication protocol. According to the frame structure specification defined in the protocol, a fixed frame header identifier 0xAA is first written into the first byte of the transmission buffer, followed by a one-byte data length identifier 0x03. Then, the contents of the three instruction bytes read above, 0x40, 0x04, and 0x01, are filled in sequentially. The hardware CRC32 check unit is called to perform a polynomial division operation on all bytes from the frame header to the instruction content, generating a 4-byte check code and appending it to the end of the buffer to form a complete instruction data packet sequence AA03400401[CRC32], thus obtaining the biological monitoring mode switching instruction code.

[0035] In another embodiment of the present invention, step 3 specifically includes sub-steps 31 to 33: Sub-step 31: Retrieve the parameter lookup table according to the biological monitoring mode switching instruction code, lock the storage address mapped to the code, read the sampling frequency value, signal gain coefficient value and single trigger recording duration value, and queue up the read multiple values ​​according to the register writing order to generate the target mode operation parameters. For example, based on the generated biological monitoring mode switching instruction code, the microprocessor retrieves the parameter lookup table. It receives the hexadecimal switching instruction code via the internal bus, uses this instruction code as the base address offset, and substitutes it into the memory addressing logic to access the parameter lookup table preset in non-volatile memory. Assuming the instruction code obtained in this embodiment is 0x3F, the processor locates the superimposed address of memory block addresses 0x2000 and 0x3F. This address line defines the hardware configuration metadata of the corresponding mode. The processor sequentially reads the following three consecutive bytes of data from the address line, extracting the sampling frequency value of 50Hz and the signal gain coefficient value of 4 times (…). (Corresponding to 12dB), the single trigger recording duration is 10 seconds. Then, the direct memory access controller is called. According to the physical address mapping order of the sensor interface register, the extracted 50Hz frequency value is mapped to the clock divider register write bit, the 4x gain value is mapped to the operational amplifier gain control register write bit, and the 10-second duration is converted into the system clock cycle number 10000 and mapped to the timer counter register write bit. The three sets of values ​​are linearly queued according to the physical address of the register from low to high, and a sequence consisting of clock divider configuration word, gain control word and timer interrupt count value is constructed to generate the target modal operating parameters. Sub-step 32: Collect the real-time value of the battery voltage of the monitoring node, call the preset high power mode voltage threshold, perform a comparison operation between the real-time voltage value and the voltage threshold, calculate the difference between the two and determine the positive or negative attribute of the difference, and determine whether the voltage is sufficient. Furthermore, the real-time battery voltage value of the monitoring node is collected. The high-precision analog-to-digital converter inside the power management module discretizes the analog voltage signal at the battery end with a period of 100ms to obtain the current voltage quantization value. For example, if the collected real-time voltage value is 11.8V, the processor retrieves the preset high-power mode voltage threshold stored in the read-only memory configuration area. This threshold is set based on the minimum voltage of 11.5V required for the flow meter and anemometer to operate at full load. Then, the arithmetic logic unit is called to perform an algebraic subtraction operation between the real-time voltage value and the voltage threshold. That is, the operation is performed, and the difference is obtained by substituting the values. The processor reads the sign bit flag in the status register. When the sign bit of the difference operation result is 0, the difference is determined to be positive. When the sign bit is 1, the difference is determined to be negative. Based on this sign bit logic state, it is determined whether the current battery voltage reserve meets the start-up requirements of the high-power mode and whether the voltage is sufficient.

[0036] Sub-step 33: When the voltage is sufficient, select the target mode operating parameters; when the voltage is insufficient, call the preset low-power counting mode parameters, convert the selected parameter set into a binary instruction stream that conforms to the communication protocol, and generate an energy consumption constraint sampling configuration parameter package.

[0037] It should be noted that, in this sub-step, the preset low-power counting mode parameters are as follows: A configuration instruction set pre-stored in the device's non-volatile memory and used to rewrite the device control register includes: a power-off control mask for disconnecting the power supply circuit between the high-bandwidth visual imaging module and the acoustic transducer; an external interrupt trigger enable bit for activating the passive infrared detector or photoelectric blocking sensor; a low-frequency division factor for switching the microprocessor's main clock frequency to maintain a minimum standby state; and a data processing rule for configuring the arithmetic logic unit to perform integer value accumulation only on the rising edge of the sensor trigger signal without waveform sampling. When the preset low-power counting mode parameters are executed, the monitoring device is forced to stop acquiring timing waveform data and only outputs the trigger count statistics within the period.

[0038] Based on this, when the voltage is sufficient, the target mode operating parameters are selected. According to the positive voltage difference state determined by the previous steps, the processor directly locks the target mode operating parameter set consisting of a 50Hz sampling rate, 4x gain and 10-second recording duration as the current configuration. When the pre-comparison operation results in a negative voltage difference, indicating a voltage deficit (e.g., a real-time voltage of 11.0V below the threshold of 11.5V), the processor immediately abandons the aforementioned high-power parameters and instead addresses a specific sector of the device's non-volatile memory. It then calls pre-stored low-power counting mode parameters. The processor first extracts the power-down control mask from the instruction set, such as the binary sequence 11000000, and writes it to the GPIO control register of the power management unit. It then performs an AND operation to force the corresponding pin level low, cutting off the power supply circuit between the high-bandwidth visual imaging module and the acoustic transducer. Subsequently, it reads the external interrupt trigger enable bit, such as 0x04, and writes it to the interrupt controller mask register. This activates the signal line connected to the passive infrared detector or photoelectric blocking sensor as the sole wake-up source, and configures the trigger mode to edge-triggered. Finally, it calls the low-frequency divider. The coefficient, such as 256, is written to the system clock divider register, switching the microprocessor's main frequency from 72MHz to 32kHz to maintain a minimum standby state. At the same time, the data processing rule configuration word is loaded, locking the arithmetic logic unit's operating mode to counter mode. A disable instruction is sent to the analog-to-digital converter to force the monitoring device to stop collecting timing waveform data. The ALU is configured to perform an integer increment operation only when the rising edge of the sensor interrupt signal is detected, and the result is stored in the accumulation register to output only the trigger count statistics within the cycle. The processor serializes and encapsulates the selected mask, enable bit, divider coefficient, and counting logic configuration word according to the industrial bus communication protocol standard, adds a frame header of 0xAA and a checksum of 0x55, converts the selected parameter set into a binary instruction stream conforming to the communication protocol, and generates an energy consumption constraint sampling configuration parameter package.

[0039] In some embodiments of the present invention, step 4 specifically includes sub-steps 41 to 43: Sub-step 41: Based on the timestamp of the energy consumption constraint sampling configuration parameter package, drive the environmental flow field sensor port to collect instantaneous water flow velocity values, flow direction angle values, and wind speed values. Perform multiplication operation between the flow velocity values ​​and the preset time extrapolation step size and quantify the unidirectional migration distance of the fluid. Combine the weight factor corresponding to the wind speed values ​​to perform vector correction on the flow direction angle values. Synthesize the migration distance and the corrected flow direction angle to construct a geometric vector model representing the fluid transport trend and generate real-time fluid dynamic vector data. It should be noted that, in this embodiment, the environmental flow field sensor includes, but is not limited to, a flow velocity meter, an anemometer, and an integrated weather station; its purpose is to obtain instantaneous water flow velocity values, flow direction angle values, and wind speed values.

[0040] For example, in this embodiment, when implementing sub-step 41, the microprocessor first reads the Unix timestamp value 1696147200 parsed from the energy consumption constraint sampling configuration parameter package. Based on this timestamp, it sends a hexadecimal read command to the RS485 communication port connected to the flow meter. The flow meter responds and returns the instantaneous water flow velocity value of 0.65 m / s and the flow direction angle value of 45.0 degrees. At the same time, it sends a voltage sampling command to the analog input port connected to the anemometer, mapping the collected 0-5V voltage signal to a wind speed value of 4.2 m / s according to the linear conversion formula. Then, it calls the preset time extrapolation step size parameter in the memory. This parameter is preset to 1800 seconds, corresponding to the theoretical migration window of pollutants within half an hour, and performs a multiplication operation between the flow velocity value and the time extrapolation step size. The calculated unidirectional fluid migration distance is 1170 meters. Then, a preset wind speed weighting coefficient table is used, which divides wind speed into three intervals: 0-2 m / s, 2-5 m / s, and above 5 m / s, corresponding to weighting coefficients of 0.02, 0.05, and 0.08 respectively. The system determines that the current wind speed of 4.2 m / s falls within the 2-5 m / s interval, locks the weighting factor at 0.05, performs vector correction calculations for the flow direction angle, establishes a correction formula, and substitutes the specific values ​​into the calculation. The corrected flow direction angle is 45.21 degrees. Finally, the calculated migration distance of 1170 meters is defined as the vector magnitude, and the corrected flow direction angle of 45.21 degrees is defined as the vector argument. A geometric vector model in a two-dimensional polar coordinate system is constructed to generate real-time fluid dynamic vector data. Sub-step 42: Based on the real-time fluid dynamic vector data, map the vector direction to the rotation angle of the major axis of an ellipse in the coordinate system. Calculate the length of the major and minor axes of the ellipse coverage area based on the vector magnitude and the lateral diffusion ratio. Substitute the major and minor axis data into the standard ellipse equation to construct a two-variable quadratic inequality function, quantify the closed geometric diffusion range of the fluid under the current momentum, and obtain the geometric boundary of the coverage area. Based on the real-time hydrodynamic vector data obtained during sub-step 41, the vector direction is mapped to the rotation angle of the major axis of an ellipse in the coordinate system. The lengths of the semi-major and semi-minor axes of the ellipse's coverage area are calculated based on the vector magnitude and the lateral diffusion ratio. The microprocessor extracts the corrected flow direction angle of 45.21 degrees from the real-time hydrodynamic vector data and directly assigns it as the rotation angle of the major axis of the ellipse in the Cartesian coordinate system. The vector magnitude of 1170 meters is extracted and defined as the length of the semi-major axis of the ellipse's coverage area. Then, the preset lateral diffusion ratio parameter in the memory is called. This parameter is set to 0.35 based on the historical statistical data ratio of the river channel width to the average flow velocity of the monitored water area. The process of multiplying the semi-major axis length by the lateral diffusion ratio is then performed. The calculation method involves substituting specific numerical values ​​into the equation, yielding a minor semi-axis length of 409.5 meters. Based on the obtained parameters, a standard ellipse equation with its center point at the origin is constructed. A rotation transformation matrix is ​​introduced to establish a bivariate quadratic inequality function containing the rotation angle: where is the planar projection coordinate of the current master node. Parameters , , (converted to radians constrained to 0.789 radians) are substituted into the above inequality. The coefficients are expanded and floating-point operations are performed to determine the constant weights of each term in the inequality. Through analytical geometric transformation, a closed elliptical region is delineated with the current node as the center, the major axis pointing downstream of the water flow in the windward direction, and the minor axis perpendicular to the flow direction. The closed geometric diffusion range of the fluid under the current momentum is quantified, and the geometric boundary of the coverage area is obtained.

[0041] Sub-step 43: Traverse the storage module to read the longitude and latitude coordinates of all subordinate nodes, substitute the coordinates of each node into the two-variable quadratic inequality function to perform algebraic calculation, determine whether the coordinate point falls within the geometric boundary based on the calculation result, filter out the node device identifiers that meet the mathematical constraints from the network list, and establish a dynamic topology set of the event influence domain.

[0042] Furthermore, the system first activates the local SQLite database query interface and executes the SELECTDeviceID,Longitude,LatitudeFROMSubNodes command to read the location information of the registered subordinate nodes in the network list line by line. For example, when the coordinates of the first subordinate node N1 are read, the latitude and longitude coordinates are converted into planar distance coordinates relative to the master node using the Mercator projection formula. Assuming the master node is located at the relative origin, the converted relative coordinates of subordinate node N1 are . This coordinate value is substituted into the bivariate quadratic inequality function constructed in step 42, and the first calculation is performed: , and the second calculation is performed: . The microprocessor calculates the results of the two terms. The system obtains a discrimination value. If the calculated discrimination value is within the ellipse's geometric boundary, the coordinate point is determined to be inside the boundary; otherwise, it is determined to be outside the boundary. For example, assuming the calculated discrimination value is 0.85, since the system determines that the subordinate node N1 is within the affected area, it immediately extracts the device identifier ID_N1_AX7 of node N1 and writes it into a temporary memory linked list. Then, the pointer automatically jumps to the next record in the database and repeats the above operations of coordinate projection, equation substitution, numerical comparison, and identifier extraction until all 128 subordinate nodes in the database have been traversed. Finally, all node identifiers that pass the verification in the memory linked list are packaged to establish a dynamic topology set of the event influence domain.

[0043] In some embodiments of the present invention, step 5 includes sub-steps 51-53: Sub-step 51: Collect the current system's nanosecond-level timestamp value and perform a hash combination operation with the master node's hardware serial number to generate a unique event identifier string. Divide a continuous blank storage area in the underlying disk array of the object storage, map the identifier to a storage read / write index, and establish an independent object storage bucket. In this embodiment, the microprocessor calls the internal high-precision real-time clock (RTC) interface to read the current system time in seconds (1696147200) and the nanosecond counter value (123456789), concatenates them into a long integer value, and then reads the hardware serial number string MN_NODE_88A, which is factory-fixed in the master node device, from a specific address in the read-only memory (ROM). It performs a string concatenation operation, appending the timestamp value to the end of the hardware serial number to obtain the original feature string. The microprocessor then calls the hardware encryption acceleration engine to load the SHA-256 hash algorithm kernel, sending the original feature string as input data to the hash calculation register. After 64 rounds of bitwise operations, a 256-bit binary hash value is output and converted into a 64-bit hexadecimal string E7A9...3F. The unique event identifier string is set, and then the file system controller scans the block allocation table (FAT) of the underlying RAID disk array, traverses the disk sector status bits, searches for consecutive free sectors marked 0x00, sets the search condition to a consecutive blank sector capacity of not less than 512MB, locks the storage area with physical sector start address LBA_Start of 0x20000000 and end address LBA_End of 0x21FFFFFF, calls the system kernel's file mapping function, uses the unique event identifier string as the key name and the locked physical sector start address as the value name, inserts a new leaf node in the memory index tree B+Tree, establishes various direct mapping relationships between the key name and the physical storage address, and establishes an independent object storage bucket. Sub-step 52: Based on the independent object bucket, send a write path redirection instruction to the storage controller, traverse and search the register status of the affected nodes in the topology set, construct a control message containing the target physical address and direct memory access parameters, forcibly suspend the write process pointing to the regular database and correct the data transfer pointer. Furthermore, the microprocessor first extracts the physical sector start address 0x20000000 from the mapping relationship established in step 51, generates an IO control instruction containing this address parameter, sends a path switching request to the storage controller via the AXI bus, then calls the event-affected domain dynamic topology set generated in step 43, reads the node identifier stored in the set, such as the slave node ID_N1_AX7, sends a status query frame to the node via the wireless sensor network, reads the 3rd bit of the node's status register Status_Reg, if the returned value is 1, it determines that the node is in a ready state, constructs a 128-byte control message, the header of which contains a synchronization word, the data segment loads the target physical address 0x20000000 and direct memory access (DMA) control parameters, where the DMA burst transfer length B... The source address increment mode is set to 1024 bytes, the target address increment mode is set to increment, and the control message is sent to each affected node. Then, an interrupt signal SIG_PAUSE is sent to the database management process, forcibly suspending the current write process PID_DB_WRITE that is pointing to the regular relational database tablespace, freezing its data transfer queue. At the same time, pointer operation instructions are called to change the value of the global pointer variable used to buffer received data in memory from the original database buffer address 0xA0000000 to the object storage bucket entry address 0x20000000, completing the physical layer switch of the underlying data flow. A control message containing the target physical address and direct memory access parameters is constructed, forcibly suspending the write process pointing to the regular database and correcting the data transfer pointer. Sub-step 53: Activate the direct memory access channel of each monitoring node, aggregate and write the data stream of subsequent nodes to an independent object storage bucket, align dissolved oxygen and conductivity values ​​according to millisecond-level timestamp values, and encapsulate multiple heterogeneous signals into the same continuous logical storage space in a parallel pipeline mode to obtain a multi-source heterogeneous spatiotemporal co-track data storage space.

[0044] The direct memory access channels of each monitoring node are activated, and the data streams from subsequent nodes are aggregated and written to an independent object storage bucket. The microprocessor sends an enable setting command to the DMA controller of each monitoring node to start the data pass-through mode. The raw data collected by the sensors of each node no longer goes through the CPU arithmetic logic unit, but is directly packaged and sent through the DMA channel. The master node's receiving buffer receives the dissolved oxygen value data packet and the conductivity value data packet from node ID_N1_AX7. The millisecond sampling timestamp in the dissolved oxygen data packet is extracted as 1696147205500ms, and the millisecond sampling timestamp in the conductivity data packet is extracted as 1696147205503ms. The time alignment check logic is executed to calculate the absolute value of the difference between the two timestamps. The calculated result of 3ms is compared with the preset synchronization tolerance threshold. The threshold is set to 10ms based on the sensor response delay characteristics. If 3ms is less than 10ms, it is confirmed as related data from the same sampling time. Then, a parallel pipeline processing thread is started. A contiguous structure space is allocated in memory. The aligned dissolved oxygen value of 8.5mg / L and the conductivity value of 200 are sequentially filled into the structure member variables and a uniform timestamp tag is attached. Data blocks are encapsulated in units of 4KB physical sector pages. The underlying block device driver is called to write the encapsulated data blocks to an independent object storage bucket starting at address 0x20000000 using direct I / O. The dissolved oxygen and conductivity values ​​are aligned according to the millisecond-level timestamp values. Multiple heterogeneous signals are encapsulated into the same contiguous logical storage space in a parallel pipeline mode to obtain a multi-source heterogeneous spatiotemporal co-track data storage space.

[0045] This multi-source heterogeneous spatiotemporal co-track data storage space serves as a dedicated high-frequency independent container for specific ecological anomalies. Its core function is to separate the topological node group data affected by hydrodynamic diffusion from the regular database stream through hash mapping and write path redirection technology, and to use DMA channels to achieve exclusive high-speed concurrent writing of multiple heterogeneous signals (such as dissolved oxygen and conductivity). This space strictly aligns different types of data frames through millisecond-level timestamps, and constructs a temporally synchronized and spatially correlated co-track structure on the physical sector, thereby providing a physically isolated, spatiotemporally highly coupled, and non-redundant complete evidence chain dataset for the diffusion and evolution analysis of specific pollution events.

[0046] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0047] Furthermore, specific terms are used in this specification to describe embodiments thereof. For example, "an embodiment," "an embodiment," and / or "a number of embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that an embodiment, an embodiment, or an alternative embodiment mentioned twice or more in different locations in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0048] Furthermore, those skilled in the art will understand that various aspects of this specification can be described and illustrated in several patentable ways or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, various aspects of this specification can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as data blocks, modules, engines, units, components, or systems. Furthermore, various aspects of this specification may be represented as computer products located on one or more computer-readable media, including computer-readable program code.

[0049] Computer storage media may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and suitable combinations thereof. Computer storage media can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0050] The computer program code required for the operation of each part of this manual can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc.; conventional procedural programming languages ​​such as C, Visual Basic, Fortran2003, Perl, COBOL2002, PHP, ABAP; dynamic programming languages ​​such as Python, Ruby, and Groovy; or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or processing device. In the latter case, the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0051] Furthermore, unless expressly stated in the claims, the order of elements and sequences, the use of numbers and letters, or other names in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, they are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing processing or mobile devices.

[0052] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0053] Finally, it should be understood that the embodiments in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments in this specification are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments in this specification are not limited to those explicitly described and illustrated herein.

Claims

1. A method for intelligent acquisition and processing of ecological monitoring data, characterized in that, The method specifically includes: The first characteristic coefficient is obtained by collecting real-time values ​​of dissolved oxygen, conductivity and turbidity at monitoring points at fixed intervals and calculating the absolute value of the difference. The second characteristic coefficient is obtained by calculating the ratio of the dissolved oxygen difference value to the conductivity difference value. A preset parameter lookup table is used to map the first and second feature coefficients to the parameter lookup table, lock the logical block and retrieve the corresponding monitoring and control index, and generate biological monitoring mode switching instruction code. Based on the biological monitoring mode switching instruction code, the sampling frequency and gain are retrieved, the battery voltage value is compared with the preset threshold and the parameter items are locked to generate an energy consumption constraint sampling configuration parameter package. Based on the timestamp of the energy consumption constraint sampling configuration parameter package, the environmental flow field sensor is called to obtain real-time fluid dynamic vector data. The geometric parameters of the coverage area are calculated using the elliptical diffusion equation. The latitude and longitude coordinates of the subordinate nodes are substituted into the equation for verification. Coordinates that meet the constraint conditions are selected to establish a dynamic topology set of the event influence domain. A UUID tag is generated for each node in the dynamic topology set of the event-affected domain. A write path redirection instruction is sent to the storage controller to aggregate the data streams of subsequent nodes and write them to an independent object storage bucket named with the tag, thus obtaining a multi-source heterogeneous spatiotemporal co-track data storage space.

2. The intelligent acquisition and processing method for ecological monitoring data according to claim 1, characterized in that: The specific calculation process of the first characteristic coefficient is as follows: According to the current dissolved oxygen, conductivity and turbidity values ​​of the collection point at a fixed cycle, retrieve the corresponding historical values ​​of the previous cycle, perform the subtraction operation of the current value minus the historical value for each of the three parameters, take the absolute value of the deviation value obtained by the calculation, extract the conductivity difference value and set it as the dividend, select the turbidity difference value and set it as the divisor, perform the division operation to calculate the ratio of the two, and obtain the first characteristic coefficient; The specific calculation process of the second characteristic coefficient is as follows: extract the dissolved oxygen difference value and the conductivity difference value, perform a division operation of the dissolved oxygen difference value and the conductivity difference value to obtain the second characteristic coefficient.

3. The intelligent acquisition and processing method for ecological monitoring data according to claim 1, characterized in that, The parameter lookup table divides the numerical range of the first and second characteristic coefficients into several rectangular logical blocks, and each block is uniquely bound to a specific set of device control instruction codes.

4. The intelligent acquisition and processing method for ecological monitoring data according to claim 3, characterized in that, Mapping the first and second feature coefficients to the parameter lookup table, locking the logical block and retrieving the corresponding monitoring and control index, and generating biological monitoring mode switching instruction code, specifically including: The first feature coefficient is set as the horizontal axis coordinate value, and the second feature coefficient is set as the vertical axis coordinate value. The boundary range of each rectangular logical block divided in the parameter lookup table is traversed. The coordinate point value is compared with the horizontal and vertical boundary thresholds of each block to determine the specific geometric area where the coordinate point falls and obtain the target logical block location identifier. The system retrieves control logic configuration items that establish mapping relationships in the parameter lookup table, traverses the monitoring mode index data stored in the database, matches index entries that match the location identifier of the target logic block, and extracts numerical pointers from the entries that point to specific hardware operating states. The microprocessor's internal instruction set mapping library is invoked to query the machine-executable instruction sequence corresponding to the numerical pointer. The sequence is then encapsulated according to the communication protocol format to construct a complete data packet containing a frame header, instruction content, and check bits, thereby obtaining the biological monitoring mode switching instruction code.

5. The intelligent acquisition and processing method for ecological monitoring data according to claim 1, characterized in that, The step involves retrieving the sampling frequency and gain based on the biological monitoring mode switching instruction code, comparing the battery voltage value with a preset threshold and locking the parameter items, and generating an energy consumption constraint sampling configuration parameter package, including: The parameter lookup table is retrieved according to the biological monitoring mode switching instruction code, the storage address mapped to the code is locked, the sampling frequency value, the signal gain coefficient value and the single trigger recording duration value are read, and the read multiple values ​​are queued according to the register writing order to generate the target mode operation parameters. Collect real-time battery voltage values ​​from monitoring nodes, call preset high-power mode voltage thresholds, perform a comparison operation between the real-time voltage value and the voltage threshold, calculate the difference between the two and determine the positive or negative attribute of the difference to determine whether the voltage is sufficient. When the voltage is sufficient, the target mode operating parameters are selected; when the voltage is insufficient, the preset low-power counting mode parameters are called, the selected parameter set is converted into a binary instruction stream that conforms to the communication protocol, and an energy consumption constraint sampling configuration parameter package is generated.

6. The intelligent acquisition and processing method for ecological monitoring data according to claim 5, characterized in that: The specific parameters of the preset low-power counting mode are as follows: A configuration instruction set pre-stored in the device's non-volatile memory and used to rewrite the device control register includes: a power-off control mask for disconnecting the power supply circuit between the high-bandwidth visual imaging module and the acoustic transducer; an external interrupt trigger enable bit for activating the passive infrared detector or photoelectric blocking sensor; a low-frequency division factor for switching the microprocessor's main clock frequency to maintain a minimum standby state; and a data processing rule for configuring the arithmetic logic unit to perform integer value accumulation only on the rising edge of the sensor trigger signal without waveform sampling. When the preset low-power counting mode parameters are executed, the monitoring device is forced to stop collecting timing waveform data and only outputs the trigger count statistics within the period.

7. The intelligent acquisition and processing method for ecological monitoring data according to claim 1, characterized in that, The timestamp based on the energy consumption constraint sampling configuration parameter package, which calls the environmental flow field sensor to obtain real-time fluid dynamic vector data, includes: The system collects instantaneous water velocity, flow direction angle, and wind speed values ​​at the environmental flow field sensor port. It performs a multiplication operation between the velocity value and a preset time extrapolation step size and quantifies the unidirectional migration distance of the fluid. It then performs vector correction on the flow direction angle value by combining the weighting factor corresponding to the wind speed value. Finally, it synthesizes the migration distance and the corrected flow direction angle to construct a geometric vector model that characterizes the fluid transport trend and generates real-time fluid dynamic vector data. Environmental flow field sensors include, but are not limited to, flow meters, anemometers, and integrated weather stations.

8. The intelligent acquisition and processing method for ecological monitoring data according to claim 7, characterized in that, The process of calculating the geometric parameters of the coverage area using the elliptic diffusion equation, substituting the latitude and longitude coordinates of the subordinate nodes into the equation for verification, filtering coordinates that meet the constraints, and establishing a dynamic topology set of the event influence domain includes: Based on real-time fluid dynamic vector data, the vector direction is mapped to the rotation angle of the major axis of an ellipse in the coordinate system. The lengths of the major and minor axes of the ellipse coverage area are calculated based on the vector magnitude and the lateral diffusion ratio. The data of the major and minor axes are substituted into the standard ellipse equation to construct a two-variable quadratic inequality function, quantify the closed geometric diffusion range of the fluid under the current momentum, and obtain the geometric boundary of the coverage area. The system iterates through the storage module to read the longitude and latitude coordinates of all subordinate nodes, substitutes the coordinates of each node into a two-variable quadratic inequality function to perform algebraic calculations, determines whether the coordinate points fall within the geometric boundary based on the calculation results, selects node device identifiers that meet the mathematical constraints from the network list, and establishes a dynamic topology set of the event influence domain.

9. The intelligent acquisition and processing method for ecological monitoring data according to claim 1, characterized in that, The method for creating the independent object storage bucket is as follows: Collect the current system's nanosecond-level timestamp value and perform a hash combination operation with the master node's hardware serial number to generate a unique event identifier string. Divide a continuous blank storage area in the underlying disk array of object storage, map the identifier to a storage read / write index, and establish an independent object storage bucket.

10. The intelligent acquisition and processing method for ecological monitoring data according to claim 1, characterized in that, Sending a write path redirection instruction to the storage controller to aggregate and write subsequent node data streams to an independent object storage bucket named with that label includes: Based on the independent object storage bucket, a write path redirection instruction is sent to the storage controller, the register status of the affected nodes in the topology set is traversed and retrieved, a control message containing the target physical address and direct memory access parameters is constructed, the write process pointing to the regular database is forcibly suspended and the data transmission pointer is corrected. Activate the direct memory access channel of each monitoring node, aggregate and write the data stream of subsequent nodes to an independent object storage bucket, align dissolved oxygen and conductivity values ​​according to millisecond-level timestamp values, and encapsulate multiple heterogeneous signals into the same continuous logical storage space in a parallel pipeline mode to obtain a multi-source heterogeneous spatiotemporal co-track data storage space.

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