Ontology-based hydroelectric computable knowledge unit modeling and description method and device

Abstract

The invention discloses an ontology-based hydroelectric computable knowledge unit modeling and description method and device, and relates to the field of knowledge graph unit modeling. The method comprises the steps of collecting body hydroelectric data; obtaining a preset Lagrange modeling operator, and generating a knowledge unit modeling element in combination with the ontology hydroelectric data; forming a knowledge unit model by the ontology hydroelectric data and the knowledge unit modeling elements; and describing ontology hydropower information based on the knowledge unit model to obtain a description file. The system correspondingly comprises an acquisition module, an operator module, a composition module, a description module and the like. According to the method, the modeling elements are generated through the Lagrangian modeling operator, the model is trained and optimized in combination with historical data, the problem that application scenes cannot be flexibly deployed and a large amount of data cannot be processed in a hydroelectric system operation description method in the prior art is solved, the flexibility and data processing capacity of hydroelectric system knowledge modeling are improved, and the method is suitable for popularization and application. The method can be widely applied to the analysis and decision-making field of hydroelectric systems.

Description

Technical Field

[0001] This invention relates to the field of knowledge graph unit modeling technology, specifically to an ontology-based method and apparatus for modeling and describing computable hydropower knowledge units. Background Technology

[0002] With the continuous development of intelligent technology, people are using more and more intelligent devices in their lives, work, and study. The use of intelligent technology has improved people's quality of life and increased their learning and work efficiency.

[0003] Currently, when constructing knowledge graphs, the computational process in the hydropower system operation typically links the system description scheme with the execution results to allow for the checking and correction of different schemes. However, existing hydropower system operation description methods often rely on fixed algorithms to judge system operating parameters and implement fixed handling strategies based on the judgment results. This fails to meet the requirements of flexible deployment scenarios and processing large volumes of data. No effective solution has yet been proposed to address these issues. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and apparatus for modeling and describing computable knowledge units of hydropower based on ontology, which solves the technical problem that the execution of existing hydropower system operation description methods often involves judging system operation parameters through fixed algorithms and implementing fixed disposal strategies based on the judgment results, which cannot meet the requirements of flexible deployment application scenarios and processing large amounts of data.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for modeling and describing computable knowledge units of hydropower based on ontology, including: Collect the hydropower data of the site; Obtain preset Lagrange modeling operators, and generate knowledge unit modeling elements using Lagrange modeling operators and ontology hydropower data; The knowledge unit model is obtained by performing composition operations on the ontological hydropower data and knowledge unit modeling elements. The ontology's hydropower information is described based on the knowledge unit model, resulting in a description file.

[0006] The aforementioned process of obtaining a preset Lagrange modeling operator and generating knowledge unit modeling elements using the Lagrange modeling operator and ontological hydropower data includes: Based on the operational scenario information of the ontological hydropower data, a preset Lagrange modeling operator is generated; According to the formula Generate the knowledge unit modeling element; where P is the knowledge unit modeling element, x and y are binary ontology hydropower data, lge is a preset Lagrange modeling operator matched according to scene information, and n is the number of elements in the ontology hydropower data.

[0007] The ontological hydropower data is defined as the raw hydropower data collected by the unit or system. The Lagrange modeling operator is defined as an auxiliary operator that assigns a certain weight value to the hydropower data. The knowledge unit modeling element is defined as the feature variable in the knowledge unit training process.

[0008] The aforementioned operational scenario information includes: the operating environment data of the main hydropower system and the Lagrange full operator matching matrix.

[0009] After constructing the knowledge unit model by combining ontological hydropower data and knowledge unit modeling elements as described above, the method also includes: Collect historical datasets of the hydropower scenario; The knowledge unit model is trained based on historical datasets to obtain a mature knowledge unit model.

[0010] The aforementioned historical datasets for collecting hydropower scenarios include: Determine the scope of historical data collection for the hydropower scenario, covering hydropower system data under different operating conditions; Historical data within the specified range is collected at preset time intervals to form an initial historical dataset. Data cleaning was performed on the initial historical dataset to remove abnormal and duplicate data, resulting in the final ontology hydropower scenario historical dataset.

[0011] The aforementioned training of the knowledge unit model based on historical datasets yields mature knowledge unit models, including: The historical dataset is divided into a training set, a validation set, and a test set. The training set is used for model training, the validation set is used to tune model parameters, and the test set is used to evaluate model performance. The knowledge unit model is trained using the training set, and the hyperparameters of the model are adjusted in real time using the validation set during the training process. The trained model is tested using a test set. When the model's performance metrics reach a preset threshold, the model is determined to be a mature knowledge unit model.

[0012] An ontology-based modeling and description device for computable hydropower knowledge units includes: The data acquisition module is used to collect the hydropower data of the main body; The operator module is used to obtain preset Lagrange modeling operators and generate knowledge unit modeling elements through the Lagrange modeling operators and the ontology hydropower data; The composition module is used to perform composition operations on ontological hydropower data and knowledge unit modeling elements to obtain a knowledge unit model. The description module is used to describe the ontology's hydropower information based on the knowledge unit model, and to obtain a description file.

[0013] The above operator module includes: The generation unit is used to generate preset Lagrange modeling operators based on the operational scenario information of the ontological hydropower data. Calculation unit, used to calculate according to formula Generate knowledge unit modeling elements, where P is a knowledge unit modeling element, x and y are binary ontology hydropower data, and lge is a preset Lagrange modeling operator matched based on scene information.

[0014] The above-mentioned operational scenario information includes: the operating environment data of the main hydropower system and the Lagrange full operator matching matrix.

[0015] The above-mentioned device also includes: The data acquisition unit is used to collect historical datasets of the hydropower scenario. The training unit is used to train the knowledge unit model based on the historical dataset to obtain a mature knowledge unit model. The storage unit is used to store the collected ontological hydropower data, historical datasets, knowledge unit models, and description files.

[0016] The aforementioned data acquisition unit includes: The scope determination subunit is used to determine the historical data collection scope of the main hydropower scenario, and the collection scope covers hydropower system data under different operating conditions. The data acquisition subunit is used to collect historical data within the range at preset time intervals to form an initial historical dataset. The data cleaning subunit is used to clean the initial historical dataset, remove abnormal and duplicate data, and obtain the final ontology hydropower scenario historical dataset.

[0017] The present invention discloses a method and apparatus for modeling and describing computable knowledge units of hydropower based on ontology. This method involves: collecting ontology hydropower data; acquiring a preset Lagrange modeling operator; generating knowledge unit modeling elements using the Lagrange modeling operator and the ontology hydropower data; performing a composition operation on the ontology hydropower data and the knowledge unit modeling elements to obtain a knowledge unit model; and describing the ontology hydropower information based on the knowledge unit model to obtain a description file. This method solves the technical problem that existing hydropower system operation description methods often rely on fixed algorithms to judge system operating parameters and implement fixed handling strategies based on the judgment results, which cannot meet the requirements of flexible deployment application scenarios and processing large volumes of data. Attached Figure Description

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of an ontology-based method for modeling and describing computable hydropower knowledge units according to an embodiment of the present invention; Figure 2 This is a structural block diagram of an ontology-based hydropower computable knowledge unit modeling and description system according to an embodiment of the present invention; Figure 3 This is a block diagram of a terminal device for performing the method according to an embodiment of the present invention; Figure 4 It is a storage unit according to an embodiment of the present invention for holding or carrying program code that implements the method according to the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] According to embodiments of the present invention, method embodiments are provided. It should be noted that the steps shown in the flowcharts in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0023] Example 1: Figure 1 This is a flowchart of a method for modeling and describing computable hydropower knowledge units based on an ontology according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps: Step S102: Collect the main body's hydropower data. In practical applications, various data during the operation of the hydropower system can be collected in real time through sensors, data acquisition devices, etc., such as the operating parameters of the hydropower equipment, the flow data and pressure data of the hydropower system, etc. These data together constitute the main body's hydropower data.

[0024] Specifically, when collecting hydropower data, a distributed real-time data acquisition algorithm is used, such as based on the MQTT protocol, with a data acquisition frequency of 1 time / second. The acquisition nodes include the hydropower station unit control unit and the sensor network (water temperature, water pressure, and flow sensors). The acquired data is initially cached and converted in format through edge computing nodes, such as converting industrial bus data into JSON format.

[0025] Step S104: Obtain the preset Lagrange modeling operator, and generate knowledge unit modeling elements using the Lagrange modeling operator and the ontology hydropower data. Specifically, first, the preset Lagrange modeling operator is generated based on the operational scenario information of the ontology hydropower data, where the operational scenario information includes the ontology hydropower system operational environment data and the Lagrange full operator matching matrix; then, according to the formula... Generate knowledge unit modeling elements, where P is the knowledge unit modeling element, x and y are binary ontology hydropower data, lge is the preset Lagrange modeling operator matched according to scene information, and n is the number of elements of the ontology hydropower data.

[0026] Specifically, the preset Lagrange operator can be obtained by performing a preset value matching operation through the preset Lagrange operator-entity hydropower data matrix, such as...

[0027] Lagrange operators are matched to different (x, y) element arrays, and then used for subsequent calculations and derivations. Furthermore, the binary ontology hydropower data can be defined as dividing the hydropower data into 1 and 1+x. In thread x, different combinations of (x, y) can be simulated using a MATLAB simulation port to fully express the comprehensiveness of the ontology hydropower data. The calculation logic of the modeling element P uses the product-interleaved element method of the Lagrange operator to assign different weight values ​​to different ontology hydropower data, so as to use more accurate and usable comprehensive data during model training.

[0028] Step S106 involves performing a composition operation on the ontology hydropower data and knowledge unit modeling elements to obtain a knowledge unit model. This composition operation can employ appropriate algorithms and model building methods based on actual needs, organically combining the ontology hydropower data with the knowledge unit modeling elements to form a knowledge unit model that reflects the operational characteristics of the hydropower system.

[0029] Specifically, when constructing a knowledge unit model, the following knowledge unit algorithms can be input into a simulation system similar to MATLAB for simulation training.

[0030] This algorithm constructs a knowledge unit model by combining ontology hydropower data with knowledge unit modeling elements. The specific steps are as follows: Step 1: Data Preparation Input ontology hydropower data, which describes the concepts, attributes, relationships, and constraints in the hydropower field in a structured form. Simultaneously, input predefined knowledge unit modeling elements, including but not limited to: classes, properties, relationships, and instances.

[0031] Step 2: Data Analysis Parse the input hydropower data and extract the following elements: a. Concepts: These represent entity types in the field of hydropower, such as "hydropower station" or "generator".

[0032] b. Attributes: These describe the characteristics of a concept, such as the "installed capacity" and "annual power generation" of a "hydropower station".

[0033] c. Relations: These indicate the connection between concepts, such as the "inclusion" relationship between "hydropower station" and "generator".

[0034] d. Constraints: These represent the constraints between concepts, attributes, or relationships. For example, a hydroelectric power station must contain at least one generator.

[0035] Step 3: Modeling Element Matching Match the concepts, attributes, relationships, and constraints extracted in step 2 with the knowledge unit modeling elements: a. Match each concept to a class.

[0036] b. Match each property to a property and determine the class to which that property belongs.

[0037] c. Match each relation to a relation and determine the source and target classes of that relation.

[0038] d. Convert the constraints into constraint representations in the knowledge unit model, such as through rules or axioms.

[0039] Step 4: Model Building Using the knowledge unit modeling elements matched in step 3, construct a knowledge unit model: a. Create classes: Create a class for each concept and organize the class hierarchy (if there is inheritance).

[0040] b. Add attributes: Add corresponding attributes to each class and set the data type and constraints of the attributes (such as value range, cardinality, etc.).

[0041] c. Establish relationships: Based on the relationship definition, establish relationships between classes and set the properties of the relationships (such as symmetry, transitivity, etc.).

[0042] d. Instantiation: Create an instance of the class based on the instance data in the ontology's hydropower data, and assign values ​​to the instance.

[0043] e. Add constraints: Add constraints to the model in the form of rules or axioms.

[0044] Step 5: Model Validation and Optimization The constructed knowledge unit model is validated, including: a. Consistency check: Check for contradictions in the classes, attributes, and relationships in the model.

[0045] b. Integrity check: Check whether the model fully represents the information in the ontological hydropower data.

[0046] Finally, the model is optimized based on the validation results, including adjusting the class hierarchy, attribute definitions, and relationship definitions, to ensure the accuracy and effectiveness of the model.

[0047] Step S108: Describe the ontology hydropower information based on the knowledge unit model to obtain a description file. The knowledge unit model provides a detailed description of the hydropower system's operating status, parameter relationships, and other information. The generated description file can be used for subsequent hydropower system analysis and decision-making.

[0048] Optionally, after step S106, the method further includes: collecting historical datasets of the ontological hydropower scenario; training the knowledge unit model based on the historical datasets to obtain a mature knowledge unit model.

[0049] The ontological hydropower data is defined as the raw hydropower data collected by the unit or system. The Lagrange modeling operator is defined as an auxiliary operator that assigns a certain weight value to the hydropower data. The knowledge unit modeling element is defined as the feature variable in the knowledge unit training process.

[0050] The process of collecting historical datasets for the hydropower scenario includes: determining the scope of historical data collection for the hydropower scenario, which covers hydropower system data under different operating conditions, such as normal operating conditions, fault operating conditions, and high-load operating conditions; collecting historical data within the scope at preset time intervals, such as hourly or daily, to form an initial historical dataset; cleaning the initial historical dataset by using an outlier detection algorithm to remove outlier data and removing duplicate data through data comparison to obtain the final historical dataset for the hydropower scenario.

[0051] Training the knowledge unit model based on the historical dataset to obtain a mature knowledge unit model involves: dividing the historical dataset into a training set, a validation set, and a test set according to a preset ratio, such as 7:2:1. The training set is used for model training, the validation set is used to adjust model parameters, and the test set is used to evaluate model performance. The knowledge unit model is trained using the training set. During the training process, the training effect of the model is monitored in real time through the validation set, and the hyperparameters of the model, such as the learning rate and the number of iterations, are adjusted according to the validation results. The trained model is then tested using the test set, and performance indicators such as accuracy and recall are used for evaluation. When the model performance indicators reach a preset threshold, the model is determined to be a mature knowledge unit model.

[0052] The above embodiments solve the technical problems of existing hydropower system operation description methods, such as the inability to flexibly deploy application scenarios and process large amounts of data.

[0053] Example 2 Figure 2 This is a structural block diagram of an ontology-based hydropower computable knowledge unit modeling and description system according to an embodiment of the present invention, such as... Figure 2 As shown, the system includes: The data acquisition module 20 is used to collect hydropower data from the hydropower system. This module can connect to various data acquisition devices to obtain real-time hydropower data during the system's operation and transmit the collected data to subsequent modules.

[0054] Operator module 22 is used to obtain a preset Lagrange modeling operator and generate knowledge unit modeling elements using the Lagrange modeling operator and the ontology hydropower data. Operator module 22 includes a generation unit and a calculation unit. The generation unit generates the preset Lagrange modeling operator based on the operation scenario information of the ontology hydropower data, and the calculation unit calculates the Lagrange modeling operator according to the formula... Generate knowledge unit modeling elements, where P is a knowledge unit modeling element, x and y are binary ontology hydropower data, and lge is a preset Lagrange modeling operator matched based on scene information.

[0055] The composition module 24 is used to perform composition operations on the ontology hydropower data and knowledge unit modeling elements to obtain a knowledge unit model. The composition module 24 uses specific algorithms and logic to integrate the ontology hydropower data and knowledge unit modeling elements to construct the knowledge unit model.

[0056] The description module 26 is used to describe the ontology hydropower information based on the knowledge unit model to obtain a description file. The description module 26 generates a detailed description file based on the knowledge unit model according to a preset format and specifications, providing a basis for relevant analysis and decision-making in the hydropower system.

[0057] Optionally, the operational scenario information includes: the operational environment data of the main hydropower system and the Lagrange full operator matching matrix.

[0058] Optionally, the system further includes: a data acquisition unit for acquiring historical datasets of the ontological hydropower scenario; a training unit for training the knowledge unit model based on the historical dataset to obtain a mature knowledge unit model; and a storage unit for storing the acquired ontological hydropower data, historical datasets, knowledge unit models, and description files.

[0059] Furthermore, the data acquisition unit includes a scope determination subunit, a data acquisition subunit, and a data cleaning subunit. The scope determination subunit determines the historical data acquisition scope of the hydropower scenario, which covers hydropower system data under different operating conditions; the data acquisition subunit acquires historical data within the scope at preset time intervals to form an initial historical dataset; the data cleaning subunit cleans the initial historical dataset, removing abnormal and duplicate data to obtain the final historical dataset of the hydropower scenario.

[0060] After receiving the historical dataset collected by the acquisition unit, the training unit divides it into a training set, a validation set, and a test set. The training set is used to train the knowledge unit model, the validation set is used to adjust the model's hyperparameters, and the test set is used to evaluate the model's performance. When the model's performance meets the standards, it is determined to be a mature knowledge unit model.

[0061] The storage unit uses appropriate storage media and storage methods to ensure the secure storage and convenient retrieval of various types of data and models.

[0062] The above embodiments solve the technical problems of existing hydropower system operation description systems, such as the inability to flexibly deploy application scenarios and process large amounts of data.

[0063] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein the program, when running, controls the device where the non-volatile storage medium is located to execute the ontology-based hydropower computable knowledge unit modeling and description method described above.

[0064] Specifically, the above method includes: collecting ontological hydropower data; obtaining a preset Lagrange modeling operator, and generating knowledge unit modeling elements using the Lagrange modeling operator and the ontological hydropower data; performing a composition operation on the ontological hydropower data and the knowledge unit modeling elements to obtain a knowledge unit model; and describing the ontological hydropower information according to the knowledge unit model to obtain a description file. Optionally, obtaining the preset Lagrange modeling operator and generating knowledge unit modeling elements using the Lagrange modeling operator and the ontological hydropower data includes: generating the preset Lagrange modeling operator based on the operational scenario information of the ontological hydropower data; and describing the ontological hydropower information according to the formula... The knowledge unit modeling elements are generated, where P is a knowledge unit modeling element, x and y are binary ontology hydropower data, and lge is a preset Lagrange modeling operator matched based on scene information. Optionally, the running scene information includes: ontology hydropower system operating environment data and a full Lagrange operator matching matrix. Optionally, after constructing the ontology hydropower data and knowledge unit modeling elements to obtain the knowledge unit model, the method further includes: collecting historical datasets of the ontology hydropower scene; training the knowledge unit model based on the historical dataset to obtain a mature knowledge unit model.

[0065] According to another aspect of the present invention, an electronic device is also provided, comprising a processor and a memory; the memory stores computer-readable instructions, and the processor is configured to execute the computer-readable instructions, wherein the computer-readable instructions, when executed, perform the ontology-based hydropower computable knowledge unit modeling and description method described above.

[0066] Specifically, the above method includes: collecting ontological hydropower data; obtaining a preset Lagrange modeling operator, and generating knowledge unit modeling elements using the Lagrange modeling operator and the ontological hydropower data; performing a composition operation on the ontological hydropower data and the knowledge unit modeling elements to obtain a knowledge unit model; and describing the ontological hydropower information according to the knowledge unit model to obtain a description file. Optionally, obtaining the preset Lagrange modeling operator and generating knowledge unit modeling elements using the Lagrange modeling operator and the ontological hydropower data includes: generating the preset Lagrange modeling operator based on the operational scenario information of the ontological hydropower data; and describing the ontological hydropower information according to the formula... The knowledge unit modeling elements are generated, where P is a knowledge unit modeling element, x and y are binary ontology hydropower data, and lge is a preset Lagrange modeling operator matched based on scene information. Optionally, the running scene information includes: ontology hydropower system operating environment data and a full Lagrange operator matching matrix. Optionally, after constructing the ontology hydropower data and knowledge unit modeling elements to obtain the knowledge unit model, the method further includes: collecting historical datasets of the ontology hydropower scene; training the knowledge unit model based on the historical dataset to obtain a mature knowledge unit model.

[0067] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0068] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0069] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0070] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0071] in addition, Figure 3 This is a schematic diagram of the hardware structure of a terminal device provided in an embodiment of this application. Figure 3 As shown, the terminal device may include an input device 30, a processor 31, an output device 32, a memory 33, and at least one communication bus 34. The communication bus 34 is used to realize communication connections between components. The memory 33 may include high-speed RAM memory, and may also include non-volatile memory (NVM), such as at least one disk storage device. The memory 33 may store various programs for performing various processing functions and implementing the method steps of this embodiment.

[0072] Optionally, the processor 31 may be implemented as a Central Processing Unit (CPU), Application Application Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic components. The processor 31 is coupled to the input device 30 and output device 32 via wired or wireless connections.

[0073] Optionally, the input device 30 may include various input devices, such as a user interface, a device interface, a programmable software interface, a camera, and a sensor. Optionally, the device interface may be a wired interface for data transmission between devices, or a hardware interface for data transmission between devices, such as a USB interface or a serial port. Optionally, the user interface may be a user-facing control button, a voice input device for receiving voice input, or a touch-sensing device for receiving user touch input, such as a touchscreen or touchpad. Optionally, the programmable software interface may be an entry point for users to edit or modify programs, such as a chip's input pin interface or input interface. Optionally, the transceiver may be a radio frequency transceiver chip with communication functions, a baseband processing chip, and a transceiver antenna. Audio input devices such as microphones can receive voice data. Output device 32 may include displays, speakers, and other output devices.

[0074] In this embodiment, the processor of the terminal device includes functions for executing the modules of the data processing device in each device. The specific functions and technical effects can be referred to in the above embodiments, and will not be repeated here.

[0075] Figure 4 This is a schematic diagram of the hardware structure of a terminal device provided in another embodiment of this application. Figure 4 Yes Figure 3 A specific implementation example in the implementation process. For example... Figure 4 As shown, the terminal device in this embodiment includes a processor 41 and a memory 42.

[0076] The processor 41 executes the computer program code stored in the memory 42 to implement the method in the above embodiments.

[0077] Memory 42 is configured to store various types of data to support operation on the terminal device. Examples of this data include instructions for any application or method operating on the terminal device, such as messages, pictures, videos, etc. Memory 42 may include random access memory, or RAM for short, and may also include non-volatile memory, such as at least one disk storage device.

[0078] Optionally, the processor 41 is located in the processing component 40. The terminal device may also include: a communication component 43, a power supply component 44, a multimedia component 45, an audio component 46, an input / output interface 47, and / or a sensor component 48. The specific components included in the terminal device are determined according to actual needs, and this embodiment does not limit this.

[0079] Processing component 40 typically controls the overall operation of the terminal device. Processing component 40 may include one or more processors 41 to execute instructions to complete all or part of the steps of the above-described method. Furthermore, processing component 40 may include one or more modules to facilitate interaction between processing component 40 and other components. For example, processing component 40 may include a multimedia module to facilitate interaction between multimedia component 45 and processing component 40.

[0080] Power supply component 44 provides power to various components of the terminal device. Power supply component 44 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the terminal device.

[0081] Multimedia component 45 includes a display screen that provides an output interface between a terminal device and a user. In some embodiments, the display screen may include a liquid crystal display (LCD) and a touch panel (TP). If the display screen includes a touch panel, the display screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation.

[0082] Audio component 46 is configured to output and / or input audio signals. For example, audio component 46 includes a microphone (MIC) configured to receive external audio signals when the terminal device is in an operating mode, such as a voice recognition mode. The received audio signals may be further stored in memory 42 or transmitted via communication component 43. In some embodiments, audio component 46 also includes a speaker for outputting audio signals.

[0083] Input / output interface 47 provides an interface between processing component 40 and peripheral interface modules, such as click wheels, buttons, etc. These buttons may include, but are not limited to, volume buttons, start buttons, and lock buttons.

[0084] Sensor assembly 48 includes one or more sensors for providing status assessments of various aspects of the terminal device. For example, sensor assembly 48 can detect the on / off state of the terminal device, the relative positioning of components, and the presence or absence of user contact with the terminal device. Sensor assembly 48 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact, including detecting the distance between the user and the terminal device. In some embodiments, sensor assembly 48 may also include a camera, etc.

[0085] Communication component 43 is configured to facilitate wired or wireless communication between the terminal device and other devices. The terminal device can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one embodiment, the terminal device may include a SIM card slot for inserting a SIM card, enabling the terminal device to log in to a GPRS network and establish communication with a server via the Internet.

[0086] As can be seen from the above, in Figure 4 The communication component 43, audio component 46, input / output interface 47, and sensor component 48 involved in the embodiment can all be used as... Figure 3 The implementation method of the input device in the embodiment.

[0087] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0088] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0089] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0090] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, optical disks, and other media capable of storing program code.

[0091] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for modeling and describing computable knowledge units of hydropower based on ontology, characterized in that, include: Collect the hydropower data of the site; Obtain a preset Lagrange modeling operator, and generate knowledge unit modeling elements using the Lagrange modeling operator and the ontology hydropower data; The ontological hydropower data and knowledge unit modeling elements are combined to obtain a knowledge unit model. The ontology hydropower information is described based on the knowledge unit model to obtain a description file.

2. The method for modeling and describing computable hydropower knowledge units based on ontology according to claim 1, characterized in that, The process of obtaining a preset Lagrange modeling operator and generating knowledge unit modeling elements using the Lagrange modeling operator and the ontology hydropower data includes: Based on the operational scenario information of the aforementioned hydropower data, a preset Lagrange modeling operator is generated; According to the formula Generate the knowledge unit modeling element; where P is the knowledge unit modeling element, x and y are binary ontology hydropower data, lge is a preset Lagrange modeling operator matched according to scene information, and n is the number of elements in the ontology hydropower data.

3. The method for modeling and describing computable hydropower knowledge units based on ontology according to claim 2, characterized in that, The operational scenario information includes: the operating environment data of the main hydropower system and the Lagrange full operator matching matrix.

4. The method for modeling and describing computable hydropower knowledge units based on ontology according to claim 1, characterized in that, After constructing the knowledge unit model by combining ontological hydropower data and knowledge unit modeling elements, the method further includes: Collect historical datasets of the hydropower scenario; The knowledge unit model is trained based on the historical dataset to obtain a mature knowledge unit model.

5. The method for modeling and describing computable hydropower knowledge units based on ontology according to claim 4, characterized in that, The historical dataset of the collected hydropower scene includes: Determine the scope of historical data collection for the hydropower scenario, covering hydropower system data under different operating conditions; Historical data within the specified range is collected at preset time intervals to form an initial historical dataset. Data cleaning was performed on the initial historical dataset to remove abnormal and duplicate data, resulting in the final ontology hydropower scenario historical dataset.

6. The method for modeling and describing computable hydropower knowledge units based on ontology according to claim 4, characterized in that, The process of training the knowledge unit model based on historical datasets to obtain a mature knowledge unit model includes: The historical dataset is divided into a training set, a validation set, and a test set. The training set is used for model training, the validation set is used to tune model parameters, and the test set is used to evaluate model performance. The knowledge unit model is trained using the training set, and the hyperparameters of the model are adjusted in real time using the validation set during the training process. The trained model is tested using a test set. When the model's performance metrics reach a preset threshold, the model is determined to be a mature knowledge unit model.

7. A device for modeling and describing computable hydropower knowledge units based on ontology, characterized in that, include: The data acquisition module is used to collect the hydropower data of the main body; The operator module is used to obtain a preset Lagrange modeling operator and generate knowledge unit modeling elements through the Lagrange modeling operator and the ontology hydropower data; The composition module is used to perform composition operations on the ontological hydropower data and knowledge unit modeling elements to obtain a knowledge unit model. The description module is used to describe the ontology's hydropower information based on the knowledge unit model, and obtain a description file.

8. The apparatus according to claim 7, characterized in that, The operator module includes: The generation unit is used to generate the preset Lagrange modeling operator based on the operational scenario information of the ontological hydropower data; Calculation unit, used to calculate according to formula Generate the knowledge unit modeling element, where P is the knowledge unit modeling element, x and y are binary ontology hydropower data, and lge is a preset Lagrange modeling operator matched according to scene information.

9. The apparatus according to claim 8, characterized in that, The operational scenario information includes: the operating environment data of the main hydropower system and the Lagrange full operator matching matrix.

10. The apparatus according to claim 7, characterized in that, The device further includes: The data acquisition unit is used to collect historical datasets of the hydropower scenario. The training unit is used to train the knowledge unit model based on the historical dataset to obtain a mature knowledge unit model. The storage unit is used to store the collected ontological hydropower data, historical datasets, knowledge unit models, and description files.

11. The apparatus according to claim 10, characterized in that, The acquisition unit includes: The scope determination subunit is used to determine the historical data collection scope of the main hydropower scenario, and the collection scope covers hydropower system data under different operating conditions. The data acquisition subunit is used to collect historical data within the range at preset time intervals to form an initial historical dataset. The data cleaning subunit is used to clean the initial historical dataset, remove abnormal and duplicate data, and obtain the final ontology hydropower scenario historical dataset.

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