Data acquisition device for capital construction period of open-source swan pumped storage power station

Abstract

The invention discloses a data acquisition device for an open-source swan pumped storage power station in an infrastructure period, and relates to the technical field of water conservancy and hydropower engineering monitoring, and the data acquisition device comprises a sensing acquisition module which collects dam body monitoring signals based on an open-source swan lightweight kernel driving sensor group, converts the monitoring signals into a digital signal sequence, and transmits the digital signal sequence to a data processing module; performing time domain filtering processing on the digital signal sequence to generate a monitoring data frame; the edge processing module analyzes the monitoring data frame, extracts characteristic parameters, compares the characteristic parameters with a threshold interval to generate a state identifier, and constructs a monitoring data packet based on the state identifier; the cooperative transmission module is used for distributing the monitoring data packets to corresponding priority queues according to the state identifiers on the basis of the open source gap distributed soft bus, and executing data packet transmission according to a priority sequence; and the data management module analyzes the type identifier in the monitoring data packet, and routes the data packet to a corresponding database according to the type identifier to execute classified storage. And the efficiency and reliability of pumped storage power station capital construction period data acquisition are improved.

Description

Technical Field

[0001] This invention relates to the field of water conservancy and hydropower engineering monitoring technology, and in particular to an open-source HarmonyOS pumped storage power station data acquisition device during the infrastructure construction phase. Background Technology

[0002] During the infrastructure construction phase of pumped storage power stations, continuous monitoring of various physical parameters such as dam deformation, seepage, and stress is necessary to ensure construction safety and project quality. Traditional data acquisition devices typically employ dedicated hardware and closed system architectures, resulting in high equipment costs, difficult maintenance, and incompatible data formats between different manufacturers, hindering unified management. Existing acquisition systems mostly use a timed full-data upload mode, lacking intelligent judgment capabilities regarding the status of monitored data. This leads to a large amount of normal data consuming communication bandwidth, while abnormal data cannot be prioritized for processing.

[0003] Furthermore, traditional data acquisition devices have limited data processing capabilities, and raw signals are often directly uploaded to the central platform for analysis, increasing the burden on network transmission and the pressure on the central server. In terms of data storage, existing systems lack effective classification mechanisms, resulting in the mixed storage of different types of monitoring data. This not only reduces query efficiency but also hinders subsequent data analysis and application. These technical limitations restrict the efficiency and reliability of data acquisition during the infrastructure construction phase of pumped storage power stations, making it difficult to meet the demands of large-scale engineering construction for real-time monitoring and intelligent management.

[0004] Therefore, there is an urgent need for an open-source data acquisition device for the infrastructure construction phase of a HarmonyOS pumped storage power station. Summary of the Invention

[0005] This invention provides an open-source HarmonyOS pumped storage power station data acquisition device for the infrastructure construction phase, in order to solve the aforementioned problems existing in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: An open-source HarmonyOS-based data acquisition device for the infrastructure construction phase of a pumped storage power station includes: The sensing and acquisition module is used to drive the sensor group to collect monitoring signals of the dam body based on the open-source HarmonyOS lightweight kernel, convert the monitoring signals into digital signal sequences, and perform time-domain filtering on the digital signal sequences to generate monitoring data frames. The edge processing module is used to parse the monitoring data frame to extract feature parameters, compare the feature parameters with the threshold range to generate a status identifier, and construct a monitoring data packet based on the status identifier. The collaborative transmission module is used to allocate monitoring data packets to corresponding priority queues based on status identifiers and execute data packet transmission in priority order, based on the open-source HarmonyOS distributed soft bus. The data management module is used to parse the type identifier in the monitoring data packets and route the data packets to the corresponding database for classification and storage based on the type identifier.

[0007] Furthermore, the sensing and acquisition module includes: The sensor driver submodule is used to send sampling commands to the sensor group through the open-source HarmonyOS device driver framework to trigger signal acquisition, and to receive the analog voltage signals output by the sensors to generate multiple analog signal streams. The signal conversion submodule is used to input multiple analog signal streams into the analog-to-digital converter to generate binary digital signals, and to add channel identifiers to the digital signals to generate a sequence of digital signals with identifiers. The filtering submodule is used to perform a moving average operation on the labeled digital signal sequence to obtain the filtered value, and then assemble the filtered value with the channel identifier to generate a monitoring data frame.

[0008] Furthermore, the edge processing module includes: The feature extraction submodule is used to parse the monitoring data frame to obtain the current sampled value and the historical sampled value, and calculate the ratio of the sampled value difference to the time interval to generate the change rate feature parameter; The interval comparison submodule is used to compare the change rate feature parameter with the upper and lower limits of the threshold, and determine the interval in which the parameter is located based on the comparison result to generate an interval identifier. The identifier generation submodule is used to generate corresponding status identifiers based on interval identifiers and write the status identifiers into data frames to construct monitoring data packets.

[0009] Furthermore, the cooperative transmission module includes: The queue allocation submodule is used to read the status identifier of the monitoring data packet and insert the data packet into the corresponding priority queue according to the status identifier type. The transmission execution submodule is used to establish a communication link through a distributed soft bus virtual driver and read data packets from the queue in priority order to perform transmission.

[0010] Furthermore, the filtering submodule includes: The buffer management unit is used to store digital signal sequences into the circular buffer corresponding to the channel, thereby realizing the cyclic storage of sampled data; The mean calculation unit is used to read consecutive sampling points from the buffer, perform a summation operation, and divide the sum by the number of sampling points to obtain the filtered value. The frame assembly unit is used to assemble the channel identifier, filter value and timestamp into a monitoring data frame according to the frame format.

[0011] Furthermore, the interval comparison submodule includes: The threshold reading unit is used to query the corresponding upper and lower limit values ​​from the threshold table according to the parameter type. The numerical comparison unit is used to calculate the difference between the characteristic parameter and the upper and lower limits, and to determine the parameter range based on the sign of the difference. The result output unit is used to generate the corresponding interval identifier output based on the parameter interval.

[0012] Furthermore, the data management module includes: The type identification submodule is used to parse the channel identifier of the data packet, determine the sensor type based on the channel range mapping, and generate a type identifier. The storage routing submodule is used to query the routing table based on the type identifier to obtain the target database address; The write execution submodule is used to establish a database connection and write data packet information as records to the target database.

[0013] Furthermore, the type recognition submodule includes: The field extraction unit is used to locate and read the channel identifier field in the data packet according to the frame format. The range determination unit is used to match the channel identifier with the sensor channel range to determine the type; The type output unit is used to generate the corresponding type identifier output based on the matching result.

[0014] Compared with the prior art, the present invention has the following advantages: This invention performs signal filtering and feature extraction directly at the acquisition end, moving raw data processing to the edge device. This reduces the amount of data to be transmitted and the computational burden on the central platform, improving the overall system's response efficiency. Automatically allocating transmission priorities based on the status of monitored data ensures that abnormal or warning data is uploaded first, which is of practical value for security monitoring scenarios requiring rapid response. Routing and sharding storage according to data type improves data retrieval speed and ease of subsequent analysis compared to hybrid storage methods, facilitating the development of specialized analysis strategies for different monitoring objects. Employing the open-source HarmonyOS kernel and distributed soft bus technology reduces dependence on specific hardware platforms, enabling the system to flexibly connect to different types of sensor devices and allowing for functional expansion according to engineering needs, thus enhancing the applicability and lifespan of the solution. Attached Figure Description

[0015] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a structural diagram of a data acquisition device for an open-source HarmonyOS pumped storage power station during the infrastructure construction phase, as described in an embodiment of the present invention. Figure 2 This is a structural diagram of the sensing and acquisition module in an embodiment of the present invention. Detailed Implementation

[0016] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0017] The embodiments of the present invention provide, as follows Figure 1 As shown, an open-source HarmonyOS pumped storage power station data acquisition device during the infrastructure construction phase includes: The sensing and acquisition module is used to drive the sensor group to collect monitoring signals of the dam body based on the open-source HarmonyOS lightweight kernel, convert the monitoring signals into digital signal sequences, and perform time-domain filtering on the digital signal sequences to generate monitoring data frames. The edge processing module is used to parse the monitoring data frame to extract feature parameters, compare the feature parameters with the threshold range to generate a status identifier, and construct a monitoring data packet based on the status identifier. The collaborative transmission module is used to allocate monitoring data packets to corresponding priority queues based on status identifiers and execute data packet transmission in priority order, based on the open-source HarmonyOS distributed soft bus. The data management module is used to parse the type identifier in the monitoring data packets and route the data packets to the corresponding database for classification and storage based on the type identifier.

[0018] The working principle and beneficial effects of the above technical solution are as follows: When the device is running, the sensing and acquisition module starts the open-source HarmonyOS lightweight kernel driver program, sends acquisition commands to the sensor group to trigger the acquisition of dam monitoring signals. The analog signals output by the sensor group are converted into digital signal sequences by analog-to-digital converters. After high-frequency noise is removed by a time-domain filtering algorithm, the sequence forms a monitoring data frame. The edge processing module receives the data frame and extracts key feature parameters. By comparing the parameter values ​​with preset threshold ranges, a corresponding status identifier is generated according to the range type in which the parameter falls, and the status identifier is encapsulated into the data frame to form a monitoring data packet. The collaborative transmission module calls the open-source HarmonyOS distributed soft bus interface, reads the status identifier of the data packet, and allocates it to high, medium, and low priority queues. Data packets are retrieved in descending order of queue priority and transmitted. The data management module receives the arriving data packet, parses the type identifier field, queries the routing mapping table based on the type identifier to determine the target database, establishes a connection, and writes the data packet content into the corresponding database to complete the classified storage.

[0019] In another embodiment, such as Figure 2 As shown, the sensing and acquisition module includes: The sensor driver submodule is used to send sampling commands to the sensor group through the open-source HarmonyOS device driver framework to trigger signal acquisition, and to receive the analog voltage signals output by the sensors to generate multiple analog signal streams. The signal conversion submodule is used to input multiple analog signal streams into the analog-to-digital converter to generate binary digital signals, and to add channel identifiers to the digital signals to generate a sequence of digital signals with identifiers. The filtering submodule is used to perform a moving average operation on the labeled digital signal sequence to obtain the filtered value, and then assemble the filtered value with the channel identifier to generate a monitoring data frame.

[0020] The working principle and beneficial effects of the above technical solution are as follows: The sensor driver submodule sends a command data packet containing sampling parameters to the sensor group. The command includes a control command to trigger sampling. After receiving the sampling command, the sensor group converts the physical quantities of the dam body into corresponding analog voltage signals and outputs them. The sensor driver submodule simultaneously receives analog voltage signals from different sensors through multiple signal acquisition channels, forming multiple parallel analog signal streams, each corresponding to a specific monitoring point.

[0021] The signal conversion submodule sends the received multiple analog signal streams to the analog-to-digital converter (ADC) circuit. The ADC samples and quantizes each analog signal, converting continuously changing analog voltage values ​​into discrete binary digital codes. After conversion, the signal conversion submodule assigns a unique channel identifier code to each converted digital signal, which is used to distinguish different signal sources. The signal conversion submodule associates and binds the digital signal values ​​with the corresponding channel identifier codes, generating a digital signal sequence with source information to ensure that subsequent processing stages can accurately identify the source of each signal.

[0022] The filtering submodule employs a moving average algorithm, extracting a series of consecutive sampling points from the digital signal sequence and performing an arithmetic average on the values ​​of these sampling points. The formula for calculating the moving average is: ; in, Indicates the filtered output value. This indicates the number of sampling points involved in the averaging calculation. Indicates the first The values ​​of each sampling point.

[0023] The filtering process effectively suppresses random noise components in the signal. The filtering submodule extracts the calculated filter value and assembles it with the channel identifier and sampling time timestamp information of the signal according to a predetermined data frame format. The assembly process arranges the fields in the order of frame header, channel identifier, filter value, timestamp, and check bit, ultimately generating a complete monitoring data frame.

[0024] In another embodiment, the edge processing module includes: The feature extraction submodule is used to parse the monitoring data frame to obtain the current sampled value and the historical sampled value, and calculate the ratio of the sampled value difference to the time interval to generate the change rate feature parameter; The interval comparison submodule is used to compare the change rate feature parameter with the upper and lower limits of the threshold, and determine the interval in which the parameter is located based on the comparison result to generate an interval identifier. The identifier generation submodule is used to generate corresponding status identifiers based on interval identifiers and write the status identifiers into data frames to construct monitoring data packets.

[0025] The working principle and beneficial effects of the above technical solution are as follows: After receiving the monitoring data frame, the feature extraction submodule first parses the format structure of the data frame and reads the current sampling value from the data field. Simultaneously, the feature extraction submodule reads the historical sampling values ​​of the same monitoring point stored in the previous sampling period from the local cache area. The feature extraction submodule calculates the difference between the current sampling value and the historical sampling value; this difference reflects the change in the monitoring parameter between two sampling intervals. The feature extraction submodule obtains the time interval between two samplings, divides the change by the time interval, and obtains the rate of change of the monitoring parameter. The formula for calculating the rate of change is: ; in, Represents the characteristic parameter of rate of change. Indicates the change in sampled values. Indicates time interval, This is the current sampled value. These are historical sample values. and These represent the corresponding sampling times. The rate of change parameter reflects the dynamic trend of the dam's condition.

[0026] The interval comparison submodule reads a pre-set threshold interval parameter table from the configuration memory. The threshold table contains upper and lower threshold limits set for different types of monitoring parameters. Based on the type attribute of the feature parameter, the interval comparison submodule searches the threshold table for matching threshold entries and extracts the corresponding upper and lower limit values. The submodule then compares the feature parameter value with the upper and lower threshold limits respectively, determining the interval range within which the feature parameter falls based on the comparison results. Finally, based on the positional relationship between the parameter and the threshold, the interval comparison submodule generates an interval identifier representing the interval it is in, indicating whether the monitoring parameter is in a safe, warning, or danger zone.

[0027] The identifier generation submodule receives the interval identifier information output by the interval comparison submodule and executes the status identifier generation logic according to the type of interval identifier. When the interval identifier indicator parameter is in the safe interval, the identifier generation submodule generates a normal status identifier; when the interval identifier indicator parameter is in the warning interval, it generates a warning status identifier; and when the interval identifier indicator parameter is in the danger interval, it generates an abnormal status identifier. The identifier generation submodule locates the reserved status field in the original monitoring data frame and writes the generated status identifier into that field. After the status identifier is integrated with the original data frame, a monitoring data packet containing status information is formed. This data packet retains the original monitoring data and adds status assessment information derived from analysis and judgment.

[0028] In another embodiment, the cooperative transmission module includes: The queue allocation submodule is used to read the status identifier of the monitoring data packet and insert the data packet into the corresponding priority queue according to the status identifier type. The transmission execution submodule is used to establish a communication link through a distributed soft bus virtual driver and read data packets from the queue in priority order to perform transmission.

[0029] The working principle and beneficial effects of the above technical solution are as follows: The queue allocation submodule reads the status identifier field in the monitoring data packet, determines the urgency of the data packet based on the identifier type, and inserts data packets of different urgency levels into the tail positions of the high, medium, and low priority queues, respectively. The transmission execution submodule establishes a communication link with the receiving end through a distributed soft bus virtual driver, reads data packets sequentially from the head of the queue according to priority, encapsulates the data packets into transmission frames, and sends them to the target node. After receiving an acknowledgment response, the transmitted data packets are removed from the queue.

[0030] In another embodiment, the filtering submodule includes: The buffer management unit is used to store digital signal sequences into the circular buffer corresponding to the channel, thereby realizing the cyclic storage of sampled data; The mean calculation unit is used to read consecutive sampling points from the buffer, perform a summation operation, and divide the sum by the number of sampling points to obtain the filtered value. The frame assembly unit is used to assemble the channel identifier, filter value and timestamp into a monitoring data frame according to the frame format.

[0031] The working principle and beneficial effects of the above technical solution are as follows: After receiving the digital signal sequence, the buffer management unit stores the data into the corresponding circular buffer according to the channel identifier. When the buffer reaches its capacity limit, a circular overwrite strategy is used to replace the oldest data. The mean calculation unit continuously reads a preset number of sample point values ​​from the buffer, performs an accumulation and summation operation, and divides the result by the number of sample points to obtain the arithmetic mean, which is then used as the filtered output. The frame assembly unit creates a data frame structure, sequentially filling the channel identifier into the frame header field, the filtered value into the data field, and the current timestamp into the time field, thus completing the assembly of the monitoring data frame.

[0032] In another embodiment, the interval comparison submodule includes: The threshold reading unit is used to query the corresponding upper and lower limit values ​​from the threshold table according to the parameter type. The numerical comparison unit is used to calculate the difference between the characteristic parameter and the upper and lower limits, and to determine the parameter range based on the sign of the difference. The result output unit is used to generate the corresponding interval identifier output based on the parameter interval.

[0033] The working principle and beneficial effects of the above technical solution are as follows: The threshold reading unit locates the corresponding record in the threshold configuration table according to the type attribute of the feature parameter, and extracts the upper and lower threshold values ​​from the record. The numerical comparison unit subtracts the feature parameter value from the upper threshold to obtain the upper limit difference, and subtracts it from the lower threshold to obtain the lower limit difference. The specific interval of the parameter is determined by judging the positive or negative sign of the two differences. The result output unit generates and outputs interval identifiers that are above the upper limit, within the interval, or below the lower limit based on the comparison results.

[0034] In another embodiment, the data management module includes: The type identification submodule is used to parse the channel identifier of the data packet, determine the sensor type based on the channel range mapping, and generate a type identifier. The storage routing submodule is used to query the routing table based on the type identifier to obtain the target database address; The write execution submodule is used to establish a database connection and write data packet information as records to the target database.

[0035] The working principle and beneficial effects of the above technical solution are as follows: The type identification submodule parses the data packet to obtain the channel identifier field value, matches this value with the channel range corresponding to each sensor type, and generates the corresponding sensor type identifier based on the matching result. The storage routing submodule queries the routing table using the type identifier as an index to obtain the network address or storage path of the corresponding database. The write execution submodule establishes a database connection using the obtained address, extracts fields such as timestamp, location information, value, and status from the data packet, organizes these fields into records according to the table structure, and then performs an insertion operation to complete the data storage.

[0036] In another embodiment, the type identification submodule includes: The field extraction unit is used to locate and read the channel identifier field in the data packet according to the frame format. The range determination unit is used to match the channel identifier with the sensor channel range to determine the type; The type output unit is used to generate the corresponding type identifier output based on the matching result.

[0037] The working principle and beneficial effects of the above technical solution are as follows: The field extraction unit calculates the starting position and length of the channel identifier field according to the frame format definition of the data packet, and reads a fixed number of bytes of data from this position as the channel identifier value. The range judgment unit compares the channel identifier value with the channel range of each sensor type in turn to determine which sensor's channel range the value falls into. The type output unit generates the corresponding sensor type identifier based on the judgment result, such as displacement type, strain type, pressure type, or environmental type identifier, and outputs the type identifier for use by subsequent modules.

[0038] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from the spirit and scope of this invention.

Claims

1. A data acquisition device for the infrastructure construction phase of an open-source HarmonyOS pumped-storage power station, characterized in that, include: The sensing and acquisition module is used to drive the sensor group to collect monitoring signals of the dam body based on the open-source HarmonyOS lightweight kernel, convert the monitoring signals into digital signal sequences, and perform time-domain filtering on the digital signal sequences to generate monitoring data frames. The edge processing module is used to parse the monitoring data frame to extract feature parameters, compare the feature parameters with the threshold range to generate a status identifier, and construct a monitoring data packet based on the status identifier. The collaborative transmission module is used to allocate monitoring data packets to corresponding priority queues based on status identifiers and execute data packet transmission in priority order, based on the open-source HarmonyOS distributed soft bus. The data management module is used to parse the type identifier in the monitoring data packets and route the data packets to the corresponding database for classification and storage based on the type identifier.

2. The data acquisition device for the infrastructure construction phase of an open-source HarmonyOS pumped storage power station according to claim 1, characterized in that, The sensing and acquisition module includes: The sensor driver submodule is used to send sampling commands to the sensor group through the open-source HarmonyOS device driver framework to trigger signal acquisition, and to receive the analog voltage signals output by the sensors to generate multiple analog signal streams. The signal conversion submodule is used to input multiple analog signal streams into the analog-to-digital converter to generate binary digital signals, and to add channel identifiers to the digital signals to generate a sequence of digital signals with identifiers. The filtering submodule is used to perform a moving average operation on the labeled digital signal sequence to obtain the filtered value, and then assemble the filtered value with the channel identifier to generate a monitoring data frame.

3. The data acquisition device for the infrastructure construction phase of an open-source HarmonyOS pumped storage power station according to claim 1, characterized in that, The edge processing module includes: The feature extraction submodule is used to parse the monitoring data frame to obtain the current sampled value and the historical sampled value, and calculate the ratio of the sampled value difference to the time interval to generate the change rate feature parameter; The interval comparison submodule is used to compare the change rate feature parameter with the upper and lower limits of the threshold, and determine the interval in which the parameter is located based on the comparison result to generate an interval identifier. The identifier generation submodule is used to generate corresponding status identifiers based on interval identifiers and write the status identifiers into data frames to construct monitoring data packets.

4. The data acquisition device for the infrastructure construction phase of an open-source HarmonyOS pumped storage power station according to claim 1, characterized in that, The collaborative transmission module includes: The queue allocation submodule is used to read the status identifier of the monitoring data packet and insert the data packet into the corresponding priority queue according to the status identifier type. The transmission execution submodule is used to establish a communication link through a distributed soft bus virtual driver and read data packets from the queue in priority order to perform transmission.

5. The data acquisition device for the infrastructure construction phase of an open-source HarmonyOS pumped storage power station according to claim 2, characterized in that, The filtering submodule includes: The buffer management unit is used to store digital signal sequences into the circular buffer corresponding to the channel, thereby realizing the cyclic storage of sampled data; The mean calculation unit is used to read consecutive sampling points from the buffer, perform a summation operation, and divide the sum by the number of sampling points to obtain the filtered value. The frame assembly unit is used to assemble the channel identifier, filter value and timestamp into a monitoring data frame according to the frame format.

6. The data acquisition device for the infrastructure construction phase of an open-source HarmonyOS pumped storage power station according to claim 3, characterized in that, The interval comparison submodule includes: The threshold reading unit is used to query the corresponding upper and lower limit values ​​from the threshold table according to the parameter type. The numerical comparison unit is used to calculate the difference between the characteristic parameter and the upper and lower limits, and to determine the parameter range based on the sign of the difference. The result output unit is used to generate the corresponding interval identifier output based on the parameter interval.

7. The data acquisition device for the infrastructure construction phase of an open-source HarmonyOS pumped storage power station according to claim 1, characterized in that, The data management module includes: The type identification submodule is used to parse the channel identifier of the data packet, determine the sensor type based on the channel range mapping, and generate a type identifier. The storage routing submodule is used to query the routing table based on the type identifier to obtain the target database address; The write execution submodule is used to establish a database connection and write data packet information as records to the target database.

8. The data acquisition device for the infrastructure construction phase of an open-source HarmonyOS pumped storage power station according to claim 7, characterized in that, The type recognition submodule includes: The field extraction unit is used to locate and read the channel identifier field in the data packet according to the frame format. The range determination unit is used to match the channel identifier with the sensor channel range to determine the type; The type output unit is used to generate the corresponding type identifier output based on the matching result.

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