A one-stop solution and system for smart water conservancy system based on model supermarket

Abstract

The present invention discloses a one-stop solution and system for a smart water conservancy system based on a model supermarket, which relates to the field of computer technology, including intelligent identification and matching to the most suitable model algorithm combination; allowing dynamic computing resource allocation and elastic expansion; the model operation platform packages the algorithm and the required information into products, and puts them on the shelves in the model supermarket for consumers to choose; consumers choose the algorithm combination according to their needs, and the producer of the model support platform performs production and processing; an interactive interface is provided, allowing consumers to customize the algorithm combination according to specific needs by dragging and configuring components; the model supermarket optimizes the algorithm parameters according to satisfaction feedback and historical usage frequency, and improves the operating efficiency of the product. The present invention realizes the comprehensive optimization and intelligentization of the one-stop solution for the smart water conservancy system, improves the efficiency, flexibility and service quality of the system, and has high practical and commercial value.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a one-stop solution and system for a smart water conservancy system based on a model supermarket. Background Art

[0002] The smart water conservancy system contains multiple subsystems (hydrology, water quality, water conservancy projects, water resources, etc.). Each subsystem has its own professional models, such as water pollution diffusion model, water pollution source tracing model, flood forecasting model, flood control scheduling model, etc. Take the flood forecasting model as an example, its algorithm can be subdivided into API, Xin'anjiang, lumped Xin'anjiang model, three water source Xin'anjiang model, etc., about 70 large and small algorithms. Bringing together the models of all subsystems in smart water conservancy will be a huge algorithm model management system. How to make model management more efficient and provide better user experience is a very difficult challenge.

[0003] We need to solve a large number of complex machine computing, machine learning and operations optimization problems, including situational awareness, parameter optimization, algorithm running time prediction, etc. At the same time, we need to strike a balance between experience, efficiency and cost. Summary of the invention

[0004] In view of the above problems existing in the operation of model management, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to provide a powerful and easy-to-use model support platform learning platform to assist researchers of various subsystem models and help the subsystems get rid of tedious engineering development.

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

[0007] On the first aspect, an embodiment of the present invention provides a one-stop solution for a smart water conservancy system based on a model supermarket, which includes: automatically parsing the natural language requirement description of the subsystem by adopting natural language processing technology, intelligently identifying and matching the most suitable model algorithm combination; introducing a cloud computing-based resource management strategy to allow dynamic computing resource allocation and elastic expansion; the model operation platform packages the algorithm and the required information into a product, and puts it on the model supermarket for consumers to choose; consumers select the algorithm combination according to their needs, and the producer of the model support platform produces and processes it; an interactive interface is provided, allowing consumers to customize the algorithm combination according to specific needs by dragging and configuring components; the model supermarket optimizes the algorithm parameters according to satisfaction feedback and historical usage frequency to improve the operating efficiency of the product.

[0008] As a preferred solution of the one-stop solution of the smart water conservancy system based on the model supermarket described in the present invention, the automatic analysis of the natural language demand description of the subsystem, intelligent identification and matching to the most suitable model algorithm combination includes the following steps: the user of the subsystem inputs the demand description through the interface, and the system pre-processes the collected natural language description; extracts features from the processed text, applies the deep learning algorithm to identify the intent of the feature-based demand description, and determines which category the user's demand belongs to, as follows:

[0009]

[0010] Where N is the number of documents in the document set, M is the number of features in the feature set, di represents the i-th document in the document set, and f 词 (di) and f 特 (di) respectively represent the frequency of occurrence of words and features in document di, W 词 and W 特 They represent the weights of vocabulary and features respectively; according to the demand areas of the smart water conservancy system, a series of demand classifications are defined in advance; according to the results of the demand classification, the model algorithm associated with the classification is retrieved from the database of the model supermarket.

[0011] As a preferred solution of the one-stop solution of the smart water conservancy system based on the model supermarket of the present invention, the intelligent identification and matching of the most suitable model algorithm combination also includes: if there are multiple candidate algorithms, further using the specific information in the user demand description, a secondary screening is performed to screen out the most suitable algorithm combination, as follows:

[0012]

[0013] Among them, R is the score of the final selected algorithm combination, n is the number of candidate algorithm combinations in the classification results, m is the number of conditions in the user requirement description, and P i Indicates the matching degree of the classification results of each candidate algorithm combination, Q i represents the weight of the jth condition in the user demand description, W ij It indicates the matching degree of the i-th algorithm in the candidate algorithm combination to the j-th condition.

[0014] As a preferred solution for the one-stop solution of the smart water conservancy system based on the model supermarket described in the present invention, the introduction of a cloud computing-based resource management strategy allows dynamic computing resource allocation and elastic expansion, including the following steps: evaluating current and expected computing resource requirements; selecting one or more suitable cloud computing platforms based on the results of the resource demand assessment; automatic configuration of computing resources; combining containerization technology to achieve rapid deployment and isolation of the algorithm operating environment to adapt to the real-time changes in the demand for computing resources of different algorithms; monitoring and analyzing costs, identifying unnecessary resource consumption, and optimizing to reduce costs.

[0015] As a preferred solution of the one-stop solution of the smart water conservancy system based on the model supermarket described in the present invention, wherein: the evaluation of the current and expected computing resource requirements includes the following steps: collecting computing resource usage data of the current system or algorithm; analyzing the resource usage of the current system or algorithm under different conditions; performing statistics on each subsystem or algorithm to determine the computing resources required at different stages; considering various factors of the algorithm, evaluating the actual demand for computing resources; predicting the future usage of the system or algorithm, and formulating corresponding resource planning plans based on the current and expected computing resource requirements:

[0016]

[0017] Among them, E is the resource planning plan, U represents the number of users, which directly represents the number of users here, D represents the data scale, which also directly represents the data scale, T is the time factor, which represents the time range of resource planning, and C represents the computing resource demand, which directly represents the demand for computing resources here.

[0018] As a preferred solution for the one-stop solution of the smart water conservancy system based on the model supermarket described in the present invention, the consumer selects the algorithm combination according to the demand, and the producer of the model support platform carries out production and processing, including the following steps: the model support platform will automatically allocate the required computing resources according to the algorithm combination selected by the consumer; the platform will configure the corresponding software environment according to the requirements of the algorithm; after the configuration is completed, the platform will start the operation process of the algorithm and monitor the operation status of the algorithm; after the calculation is completed, the platform will generate the results and provide them to the consumer for evaluation.

[0019] As a preferred solution for the one-stop solution of the smart water conservancy system based on the model supermarket described in the present invention, the optimization of the algorithm parameters based on satisfaction feedback and historical usage frequency includes the following steps: tracking the usage frequency and performance indicators of the algorithm; analyzing the collected data to identify the most popular and best-performing algorithms and parameter settings; adjusting the algorithm parameters according to the analysis results to implement an automated adjustment mechanism; redeploying the optimized algorithm to the model supermarket and continuing to collect user feedback on the optimized algorithm.

[0020] On the second aspect, in order to further solve the problems existing in the operation of model management, the embodiment of the present invention provides a one-stop solution system for smart water conservancy systems based on a model supermarket, which includes: a data acquisition module, which is used to monitor the parameters of each load in real time and transmit the data to the system; an intelligent analysis module, which automatically parses the natural language requirement description of the subsystem by adopting natural language processing technology, and intelligently identifies and matches the most suitable model algorithm combination; a remote communication module, which is used to establish a communication connection with a remote monitoring center to realize data uploading and the reception and sending of remote control instructions; a cloud computing resource management module, which introduces a cloud computing-based resource management strategy, allows dynamic computing resource allocation and elastic expansion, and ensures the stability and reliability of system operation; a model operation platform, which packages the algorithm and the required information into a product, and puts it on the model supermarket for consumers to choose, so as to realize the operation and management of the algorithm; an interactive interface module, which provides an interactive interface, allowing consumers to customize the algorithm combination according to specific needs by dragging and configuring components, so as to facilitate user operation and management.

[0021] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the one-stop solution for a smart water system based on a model supermarket as described in the first aspect of the present invention.

[0022] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the one-stop solution for a smart water conservancy system based on a model supermarket as described in the first aspect of the present invention.

[0023] The beneficial effects of the present invention are as follows: the present invention realizes automatic matching from demand to model algorithm, allowing users to conveniently select an algorithm combination that suits their needs, thereby improving the intelligence level of the system and user experience, and ultimately achieving the beneficial effects of improving system utilization efficiency and meeting user personalized needs; accurately identifying and classifying user needs through deep learning algorithms provides a reliable basis for subsequent algorithm selection, thereby improving the intelligence level of the system and the accuracy of algorithm matching, and further improving the efficiency of the system and user satisfaction; the present invention realizes comprehensive optimization and intelligence of the one-stop solution of the smart water conservancy system, improves the efficiency, flexibility and service quality of the system, and provides users with more convenient and personalized water conservancy management services, with high practical and commercial value. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0025] Figure 1 This is a scene diagram of the one-stop solution for the smart water system based on the model supermarket in Example 1. DETAILED DESCRIPTION

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0027] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0028] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0029] Example 1

[0030] Reference Figure 1 , which is the first embodiment of the present invention, and provides a one-stop solution for a smart water conservancy system based on a model supermarket, comprising the following steps:

[0031] S1: By adopting natural language processing (NLP) technology, the natural language requirement description of the subsystem is automatically parsed, and the most suitable model algorithm combination is intelligently identified and matched.

[0032] Among them, users of the subsystem input their own demand descriptions through the interface. These descriptions are written in natural language, such as "a model that can predict the reservoir water level next month is needed"; this input can be implemented in various forms, such as text box input, voice input, etc.

[0033] The collected natural language descriptions are preprocessed, including steps such as text cleaning (removing meaningless filler words, punctuation marks, etc.), word standardization (synonym replacement, stem extraction, etc.), etc.

[0034] Extract features from the processed text and apply deep learning algorithms to identify the intent of the characterized demand description, that is, determine which category the user's demand belongs to, as follows:

[0035]

[0036] Where N is the number of documents in the document set, M is the number of features in the feature set, di represents the i-th document in the document set, and f 词 (di) and f 特 (di) respectively represent the frequency of occurrence of words and features in document di, W 词 and W 特 Represent the weights of vocabulary and features respectively.

[0037] According to the demand areas of the smart water conservancy system, a series of demand categories are defined in advance, such as "water level prediction", "water quality analysis", "resource allocation", etc.

[0038] According to the result of the demand classification, the model algorithm associated with the classification is retrieved from the database of the model supermarket.

[0039] If there are multiple candidate algorithms, further use the specific information in the user's requirement description, such as accuracy requirements, running time constraints, and other conditions to screen out the most suitable algorithm combination, as follows:

[0040]

[0041] Among them, R is the score of the final selected algorithm combination, n is the number of candidate algorithm combinations in the classification results, m is the number of conditions in the user requirement description, and P i Indicates the matching degree of the classification results of each candidate algorithm combination, Q i represents the weight of the jth condition in the user demand description, W ijIt indicates the matching degree of the i-th algorithm in the candidate algorithm combination to the j-th condition.

[0042] The comprehensive performance of the final selected algorithm combination is evaluated by comprehensively considering the matching degree of the classification results of each candidate algorithm combination and the weight of each condition.

[0043] S2: Introduce cloud computing-based resource management strategies to allow dynamic computing resource allocation and elastic expansion.

[0044] S2.1: Assess current and anticipated computing resource requirements.

[0045] This involves analyzing the computing resources required by various subsystems or algorithms at different stages, as well as the length of time and intensity of resource usage.

[0046] Specifically, collect computing resource usage data of the current system or algorithm, including CPU usage, memory usage, storage requirements and other information; analyze the resource usage of the current system or algorithm under different conditions (such as different data volumes, different parameter settings); conduct statistics on each subsystem or algorithm to determine the computing resources required at different stages, including CPU, memory, storage, etc.; consider factors such as the complexity of the algorithm, the amount of data, and computationally intensive operations, and evaluate its actual demand for computing resources; gain an in-depth understanding of the computational complexity of the algorithm, such as time complexity, space complexity, etc.

[0047] Predict the future usage of the system or algorithm, taking into account possible growth trends, the increase in the number of users, the expansion of data scale, etc., and formulate corresponding resource planning plans based on current and expected computing resource requirements:

[0048]

[0049] Among them, E is the resource planning plan, U represents the number of users, which directly represents the number of users here, D represents the data scale, which also directly represents the data scale, T is the time factor, which represents the time range of resource planning, and C represents the computing resource demand, which directly represents the demand for computing resources here.

[0050] S2.2: Based on the results of the resource requirements assessment, select one or more appropriate cloud computing platforms.

[0051] Different cloud providers may differ in price, service quality, available resource types, etc., so you need to consider all factors when choosing.

[0052] S2.3: Automated configuration of computing resources.

[0053] This includes automatically starting or stopping virtual machine instances based on demand, adjusting resource scale (such as number of CPU cores, memory size), allocating storage space, etc.

[0054] Specifically, the calculation of automatic resource configuration is as follows:

[0055]

[0056] Among them, S represents the system's automated configuration score, VM is the number of virtual machine instances, CPU represents the number of CPU cores, and M m Indicates the memory size, S0 indicates the storage space size, and T0 is the time factor, which represents the time range of the automatic configuration. By normalizing with time, it reflects the possible configuration change trend.

[0057] S2.4: Combine containerization technology to achieve rapid deployment and isolation of the algorithm running environment to adapt to the real-time changes in the demand for computing resources of different algorithms.

[0058] It allows applications to be deployed and managed quickly and consistently in different cloud computing environments, while containerization can improve resource utilization and application portability.

[0059] S2.5: Monitor and analyze costs, identify unnecessary resource consumption, and optimize to reduce costs.

[0060] S3: The model running platform packages the algorithm and its required resources, parameters and other information into a product (algorithm list) and puts it on the model supermarket for consumers (subsystems) to choose.

[0061] Developers need to upload their own algorithm packages and related information according to the requirements of the Model Supermarket, including algorithm descriptions, usage guidelines, performance indicators, etc.

[0062] After uploading, the platform will conduct a certain review process to ensure that the uploaded content complies with the platform regulations and does not contain malicious code.

[0063] After passing the review, the algorithm will be officially listed in the model supermarket, and users can search and use these algorithms. Developers will regularly release updated versions of the algorithm based on user feedback and algorithm update requirements to ensure the effectiveness and security of the algorithm.

[0064] S4: Consumers select algorithm combinations (products) based on their needs, and the producers of the model support platform (i.e., computer hardware and software resources) carry out production and processing.

[0065] Preferably, the model support platform will automatically allocate the required computing resources based on the algorithm combination selected by the consumer, including allocating sufficient CPU / GPU resources, memory space, and necessary storage resources.

[0066] The platform will also configure the corresponding software environment according to the requirements of the algorithm, including a specific version of the operating system, programming language interpreter or compiler, required library files, etc.

[0067] After the configuration is completed, the platform will start the algorithm's running process, which involves complex data processing, machine learning training and other steps; and the platform monitors the running status of the algorithm to ensure that the calculation process is stable and efficient.

[0068] After the calculation is completed, the platform will generate the results and provide them to consumers for evaluation.

[0069] S5: Provides an interactive interface that allows consumers (subsystems) to customize algorithm combinations according to specific needs by dragging and dropping and configuring components.

[0070] The interactive interface includes developing a library of algorithm components, each of which represents an algorithm or part of an algorithm; each component is designed with an intuitive icon and a brief description to help users quickly identify and select it.

[0071] Implement a drag-and-drop interface to allow users to drag components from the algorithm component library to the workspace. Components in the workspace can be reordered or deleted by dragging to adjust the algorithm flow; provide a configuration panel for each algorithm component to allow users to customize algorithm parameters, input and output, etc.; implement parameter input validation to ensure that the data format entered by the user is correct and prevent incorrect configuration.

[0072] The platform provides instant feedback, such as estimated operating efficiency and resource consumption, to help users optimize algorithm selection and configuration.

[0073] Consumers can provide satisfaction feedback, which is the gap between the expected and actual results of the calculation.

[0074] S6: Model Supermarket optimizes algorithm parameters based on satisfaction feedback and historical usage frequency to improve product operating efficiency.

[0075] S6.1: Track algorithm usage frequency and performance metrics, such as execution time, accuracy, etc.

[0076] S6.2: Analyze the collected data to identify the most popular and best-performing algorithms and parameter settings, and determine which parameters have the greatest impact on user satisfaction and algorithm performance.

[0077] S6.3: Adjust algorithm parameters according to the analysis results to implement an automated adjustment mechanism.

[0078] S6.4: Redeploy the optimized algorithm to the model supermarket and continue to collect user feedback on the optimized algorithm.

[0079] This embodiment also provides a one-stop solution system for a smart water conservancy system based on a model supermarket, including:

[0080] Data acquisition module, used to monitor the parameters of each load in real time and transmit the data to the system;

[0081] Intelligent analysis module, through the use of natural language processing technology, automatically parses the natural language requirement description of the subsystem, intelligently identifies and matches the most suitable model algorithm combination;

[0082] Remote communication module, used to establish a communication connection with the remote monitoring center to upload data and receive and send remote control instructions;

[0083] The cloud computing resource management module introduces a cloud computing-based resource management strategy, allowing dynamic computing resource allocation and elastic expansion to ensure the stability and reliability of system operation;

[0084] The model operation platform packages the algorithm and the required information into products and puts them on the model supermarket for consumers to choose from, thus realizing the operation and management of the algorithm.

[0085] The interactive interface module provides an interactive interface that allows consumers to customize algorithm combinations according to specific needs by dragging and configuring components, making it easier for users to operate and manage.

[0086] This embodiment also provides a computer device, which is suitable for a one-stop solution for a smart water conservancy system based on a model supermarket, and includes: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the one-stop solution for a smart water conservancy system based on a model supermarket as proposed in the above embodiment.

[0087] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0088] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a one-stop solution for realizing a smart water conservancy system based on a model supermarket as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0089] In summary, the present invention realizes the automatic matching from demand to model algorithm, allowing users to easily choose the algorithm combination that suits their needs, improving the intelligence of the system and user experience, and ultimately achieving the beneficial effect of improving system efficiency and meeting user personalized needs; accurately identifying and classifying user needs through deep learning algorithms, providing a reliable basis for subsequent algorithm selection, thereby improving the intelligence of the system and the accuracy of algorithm matching, and further improving the efficiency of the system and user satisfaction; the present invention realizes the comprehensive optimization and intelligence of the one-stop solution of the smart water conservancy system, improves the efficiency, flexibility and service quality of the system, and provides users with more convenient and personalized water conservancy management services, with high practical and commercial value.

[0090] Example 2

[0091] Referring to Table 1, which is the second embodiment of the present invention, in order to further verify the advancement of the present invention, experimental simulation data of a one-stop solution for a smart water conservancy system based on a model supermarket is provided.

[0092] First, various data on the operation of the water conservancy system are collected, including demand description, computing resource usage, user volume, and data scale; according to the steps in the claims, a model supermarket system is built, and simulation experiments are carried out on the processes of automatic parsing of demand description, matching algorithm combination, and dynamic resource allocation.

[0093] In the experiment, based on the collected demand descriptions, preprocessing technology was used to process the text and extract features; a deep learning algorithm was used to identify the intent of the demand descriptions, and the associated model algorithms were retrieved from the model supermarket database based on the classification results; multiple secondary screenings were performed to select the most suitable algorithm combination based on the specific information of the user demand descriptions.

[0094] At the same time, we evaluated the current and expected computing resource requirements, selected a suitable cloud computing platform, and automated the configuration of computing resources. We also used containerization technology to achieve rapid deployment and isolation of the algorithm operating environment to adapt to the real-time changes in computing resource requirements of different algorithms.

[0095] As shown in the table, it contains the data collected during the experiment:

[0096] Table 1 Partial data during the experiment of the present invention

[0097]

[0098] It can be seen that in terms of the time for processing the demand description, our method has an average processing time of 5.5 seconds, which is improved compared with the processing time of the prior art, indicating that our natural language processing technology has higher efficiency; the present invention can more reasonably utilize computing resources and improve resource utilization.

[0099] In terms of the number of users and data scale, the system of the present invention can meet the needs of larger-scale users, and when the number of users and data scale increase, the algorithm operation efficiency score also improves, indicating that our system has good scalability and performance.

[0100] To sum up, the one-stop solution for the smart water conservancy system based on the model supermarket of the present invention has shown obvious advantages in the embodiments. By improving processing efficiency, rationally utilizing computing resources, and meeting the needs of large-scale users, it has brought significant innovation and practicality to the management and operation of the smart water conservancy system.

[0101] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A one-stop solution for smart water conservancy system based on model supermarket, characterized by: include: By adopting natural language processing technology, the natural language requirement description of the subsystem is automatically parsed, and the most suitable model algorithm combination is intelligently identified and matched; Introducing cloud computing-based resource management strategies to allow dynamic computing resource allocation and elastic expansion; The model operation platform packages the algorithm and the required information into products and puts them on the model supermarket for consumers to choose from; Consumers select algorithm combinations based on their needs, and producers on the model support platform carry out production and processing; Provide an interactive interface that allows consumers to customize algorithm combinations according to specific needs by dragging and dropping and configuring components; Model Supermarket optimizes algorithm parameters based on satisfaction feedback and historical usage frequency to improve product operating efficiency; The automatic analysis of the natural language requirement description of the subsystem, intelligent identification and matching to the most suitable model algorithm combination includes the following steps: The user of the subsystem inputs the requirement description through the interface, and the system preprocesses the collected natural language description; Extract features from the processed text, apply deep learning algorithms to identify the intent of the characterized demand description, and determine which category the user's demand belongs to, as follows: Where N is the number of documents in the document set, M is the number of features in the feature set, di represents the i-th document in the document set, and f 词 (di) and f 特 (di) respectively represent the frequency of occurrence of words and features in document di, W 词 and W 特 Represent the weights of vocabulary and features respectively; According to the demand areas of the smart water system, a series of demand classifications are defined in advance; According to the result of the demand classification, the model algorithm associated with the classification is retrieved from the database of the model supermarket; The intelligent identification and matching of the most suitable model algorithm combination also includes: If there are multiple candidate algorithms, the specific information in the user's requirement description is used for secondary screening to select the most suitable algorithm combination, as follows: Among them, R is the score of the final selected algorithm combination, n is the number of candidate algorithm combinations in the classification results, m is the number of conditions in the user requirement description, and P i Indicates the matching degree of the classification results of each candidate algorithm combination, Q i represents the weight of the jth condition in the user demand description, W ij Indicates the matching degree of the i-th algorithm in the candidate algorithm combination to the j-th condition; The introduction of a cloud computing-based resource management strategy to allow dynamic computing resource allocation and elastic expansion includes the following steps: Assess current and anticipated computing resource requirements; Select one or more appropriate cloud computing platforms based on the results of the resource demand assessment; Automated configuration of computing resources; Combine containerization technology to achieve rapid deployment and isolation of algorithm operating environments to adapt to the real-time changes in computing resource requirements of different algorithms; Monitor and analyze costs, identify unnecessary resource consumption, and optimize to reduce costs; The step of evaluating current and expected computing resource requirements includes the following steps: Collect computing resource usage data of the current system or algorithm; Analyze resource usage of the current system or algorithm under different conditions; Compute statistics for each subsystem or algorithm to determine the computing resources required at different stages; Evaluate the actual demand for computing resources, taking into account various factors of the algorithm; Predict the future usage of the system or algorithm and develop corresponding resource planning plans based on current and expected computing resource requirements: Among them, E is the resource planning plan, U represents the number of users, which directly represents the number of users here, D represents the data scale, which also directly represents the data scale, T is the time factor, which represents the time range of resource planning, and C represents the computing resource demand, which directly represents the demand for computing resources here.

2. The one-stop solution for the smart water conservancy system based on the model supermarket as claimed in claim 1 is characterized by: The consumer selects an algorithm combination according to demand, and the producer of the model support platform performs production and processing, including the following steps: The model support platform will automatically allocate the required computing resources based on the algorithm combination selected by the consumer; The platform configures the corresponding software environment according to the requirements of the algorithm; After the configuration is completed, the platform will start the algorithm's operation process and monitor the algorithm's operation status; After the calculation is completed, the platform will generate the results and provide them to consumers for evaluation.

3. The one-stop solution for the smart water conservancy system based on the model supermarket as claimed in claim 2 is characterized by: The optimization of algorithm parameters according to satisfaction feedback and historical usage frequency includes the following steps: Track algorithm usage frequency and performance metrics; Analyze the collected data to identify the most popular and best performing algorithms and parameter settings; Adjust algorithm parameters according to analysis results to implement an automated adjustment mechanism; Redeploy the optimized algorithm to the model supermarket and continue to collect user feedback on the optimized algorithm.

4. A one-stop solution system for a smart water conservancy system based on a model supermarket, based on the one-stop solution system for a smart water conservancy system based on a model supermarket according to any one of claims 1 to 3, characterized in that: include: Data acquisition module, used to monitor the parameters of each load in real time and transmit the data to the system; Intelligent analysis module, through the use of natural language processing technology, automatically parses the natural language requirement description of the subsystem, intelligently identifies and matches the most suitable model algorithm combination; Remote communication module, used to establish a communication connection with the remote monitoring center to upload data and receive and send remote control instructions; The cloud computing resource management module introduces a cloud computing-based resource management strategy, allowing dynamic computing resource allocation and elastic expansion to ensure the stability and reliability of system operation; The model operation platform packages the algorithm and the required information into products and puts them on the model supermarket for consumers to choose from, thus realizing the operation and management of the algorithm. The interactive interface module provides an interactive interface that allows consumers to customize algorithm combinations according to specific needs by dragging and configuring components, making it easier for users to operate and manage.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the one-stop solution for the smart water system based on the model supermarket described in any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the one-stop solution for a smart water system based on a model supermarket as described in any one of claims 1 to 3 are implemented.

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