River water quality detection method and system based on artificial intelligence

Abstract

The embodiment of the invention provides a river water quality detection method and system based on artificial intelligence, and the method comprises the steps: obtaining the field collection data of a river water quality detection system as regional water quality monitoring samples, and carrying out the water quality state label prediction of the regional water quality monitoring samples through a shared learning network; the accuracy and the efficiency of water quality detection are obviously improved. Specifically, a deep learning network with the best training effect is selected from a plurality of training entities participating in linkage network parameter learning as a shared learning network, so that the reliability and high precision of water quality state label prediction data are ensured. Furthermore, parameter learning is performed on the target water quality evaluation model according to the prediction data, and an optimized target application program is determined, so that more accurate water quality state label prediction data can be generated. According to the method, not only is the intelligent level of water quality detection improved, but also personal errors and time cost possibly existing in a traditional water quality detection method are effectively reduced.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and more specifically, to a river water quality detection method and system based on artificial intelligence. Background Art

[0002] With the rapid development of industrialization and urbanization, river water pollution is becoming increasingly serious, posing a huge threat to the ecological environment and human health. In order to effectively monitor and evaluate river water quality, traditional water quality testing methods mainly rely on manual sampling and laboratory analysis. However, this method is not only time-consuming and labor-intensive, but also greatly affected by human factors, making it difficult to ensure the accuracy and timeliness of the test results. Summary of the invention

[0003] In view of this, the purpose of this application is to provide a river water quality detection method and system based on artificial intelligence.

[0004] In conjunction with the first aspect of the present application, a river water quality detection method based on artificial intelligence is provided, which is applied to a river water quality detection system based on artificial intelligence, and the method comprises: Acquire regional water quality monitoring samples, where the regional water quality monitoring samples are field-collected data from a river water quality detection system, and the river water quality detection system is any training entity that performs linkage network parameter learning; Predicting water quality status labels of the regional water quality monitoring samples based on a shared learning network to generate water quality status label prediction data, wherein the shared learning network is obtained by the river water quality detection system from other training entities participating in the linkage network parameter learning, and the shared learning network is a deep learning network with the best training effect among other training entities; Based on the water quality status label prediction data, parameter learning is performed on the target water quality assessment model, and a target application corresponding to the target water quality assessment model for completing parameter learning is determined; The regional water quality monitoring samples are loaded into the target application corresponding to the target water quality assessment model that has completed parameter learning, and optimized water quality status label prediction data is generated.

[0005] In a possible implementation of the first aspect, performing parameter learning on a target water quality assessment model based on the water quality status tag prediction data and determining a target application program corresponding to the target water quality assessment model for which parameter learning is completed specifically includes: Based on the water quality status label prediction data, parameter learning is performed on a target water quality assessment model to generate a first water quality detection network in which parameter learning is completed, and the target water quality assessment model performs deep learning based on the water quality status label prediction data; According to the first on-site verification collected data, the first water quality detection network that has completed parameter learning is detected to generate first water quality status detection data, wherein the first on-site verification collected data is water quality parameter collection data in the on-site water quality parameter collection data sequence of the river water quality detection system; If the first water quality status detection data meets the target requirements, the first water quality detection network that has completed parameter learning is output as a target application program that has completed parameter learning corresponding to the target water quality assessment model.

[0006] In a possible implementation of the first aspect, the shared learning network is obtained by the river water quality detection system from other training entities participating in the linkage network parameter learning, specifically including: Get the most recent iteration network of each other training entity; According to the second on-site verification collected data, each most recent iteration network is tested to generate second water quality status detection data, wherein the second on-site verification collected data is part of the on-site collected data of the river water quality detection system; According to the second water quality status detection data, the deep learning network with the best training effect in each most recent iteration network is output as the shared learning network.

[0007] In a possible implementation of the first aspect, performing parameter learning on a target water quality assessment model based on the water quality status label prediction data to generate a first water quality detection network in which parameter learning is completed specifically includes: Acquire the remaining on-site collected data; the remaining on-site collected data is part or all of the on-site collected data except the second on-site verification collected data; The water quality status label prediction data and the remaining field collected data are used as target sample learning data to perform parameter learning on the target water quality assessment model.

[0008] In a possible implementation manner of the first aspect, obtaining the most recent iterative network of each other training entity specifically includes: Acquire a network index sequence; the network index sequence is used to record the neural network information run by each training entity participating in the linkage network parameter learning, and the neural network information includes a network iteration identifier and a network index information; Based on the network iteration identifier, determining the most recent iteration network of each other training entity; Based on the network index information of the most recent iteration network of each other training entity, the most recent iteration network of each other training entity is obtained.

[0009] In a possible implementation of the first aspect, the target water quality assessment model includes the neural network to be optimized in the river water quality detection system; the target application includes an optimized neural network corresponding to the neural network to be optimized; and the method further includes: The neural network to be optimized is tested based on the first on-site verification and collection data to generate third water quality status test data.

[0010] In a possible implementation of the first aspect, the method further includes: Acquire first on-site collected data, perform parameter learning on the target water quality assessment model, and generate a second water quality detection network in which parameter learning is completed; the first on-site collected data is part or all of the water quality parameter collection data in the on-site water quality parameter collection data sequence of the river water quality detection system; Based on the first on-site verification and collection data, the second water quality detection network that has completed parameter learning is detected to generate fourth water quality status detection data.

[0011] In a possible implementation of the first aspect, the method further includes: The iteration identifier and network index information of the target application are saved in a network index sequence; the network index sequence is used to store the neural network information run by each training entity for linkage network parameter learning; the neural network information includes a network iteration identifier and network index information.

[0012] In combination with the second aspect of the present application, a river water quality detection system based on artificial intelligence is provided, wherein the river water quality detection system based on artificial intelligence includes a machine-readable storage medium and a processor, wherein the machine-readable storage medium stores machine-executable instructions, and when the processor executes the machine-executable instructions, the river water quality detection system based on artificial intelligence implements the aforementioned river water quality detection method based on artificial intelligence.

[0013] In conjunction with the third aspect of the present application, a computer-readable storage medium is provided, in which computer-executable instructions are stored. When the computer-executable instructions are executed, the aforementioned artificial intelligence-based river water quality detection method is implemented.

[0014] In combination with any of the above aspects, the embodiment of the present application obtains the on-site collected data of the river water quality detection system as regional water quality monitoring samples, and uses a shared learning network to predict the water quality status labels of these regional water quality monitoring samples, thereby significantly improving the accuracy and efficiency of water quality detection. Specifically, by selecting the deep learning network with the best training effect as a shared learning network from multiple training entities participating in the linkage network parameter learning, the reliability and high precision of the water quality status label prediction data are ensured. Furthermore, based on these predicted data, the target water quality assessment model is parameter learned, and the optimized target application is determined, so that more accurate water quality status label prediction data can be generated. This method not only improves the level of intelligence of water quality detection, but also effectively reduces the human errors and time costs that may exist in traditional water quality detection methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained by combining these drawings without paying creative work.

[0016] Figure 1 A schematic flow chart of the artificial intelligence-based river water quality detection method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0019] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0020] Figure 1 The flowchart of the river water quality detection method based on artificial intelligence provided by the embodiment of the present application is shown. It should be understood that in other embodiments, the order of some steps in the river water quality detection method based on artificial intelligence of this embodiment can be shared with each other based on actual needs, or some steps can be omitted or maintained. The details of the river water quality detection method based on artificial intelligence include: Step S110, obtaining regional water quality monitoring samples, wherein the regional water quality monitoring samples are field-collected data of a river water quality detection system, and the river water quality detection system is any training entity that performs linkage network parameter learning.

[0021] In this embodiment, in a large water resources management system, the server is responsible for the data analysis and model training related work of the entire river water quality detection system. Assume that there are multiple river water quality detection systems distributed in different regions. These detection systems have the ability to collect water quality data on site and serve as training entities for executing linkage network parameter learning. For example, on a large river that flows through multiple cities, river water quality detection systems for three cities A, B, and C are set up from upstream to downstream. Each detection system contains a series of sensors that can collect multiple water quality parameters such as water temperature, pH value, dissolved oxygen content, chemical oxygen demand (COD), biochemical oxygen demand (BOD), etc.

[0022] The server communicates with these river water quality detection systems. Taking the river water quality detection system in City A as an example, its on-site sensors will collect comprehensive parameters of the river water quality in the area at regular intervals (for example, every hour). The collected data includes the water temperature of 20 degrees Celsius, pH value of 7.2, dissolved oxygen content of 8mg / L, COD of 20mg / L, BOD of 5mg / L, etc. at a specific location in the river in the area at a certain moment. These data as a whole constitute a regional water quality monitoring sample.

[0023] The server obtains such regional water quality monitoring samples from the river water quality detection system in City A through a network communication protocol (such as TCP / IP protocol). Similarly, the server can also obtain their respective regional water quality monitoring samples from the river water quality detection systems in City B and City C. These samples obtained from different river water quality detection systems will serve as basic data for subsequent analysis and processing, and will be used for water quality status analysis, model training and other operations in the entire linkage network parameter learning process.

[0024] Step S120, predicting the water quality status labels of the regional water quality monitoring samples based on the shared learning network to generate water quality status label prediction data, wherein the shared learning network is obtained by the river water quality detection system from other training entities participating in the linkage network parameter learning, and the shared learning network is the deep learning network with the best training effect among other training entities.

[0025] In this water resource management scenario, multiple river water quality detection systems conduct linkage network parameter learning. Assume that in addition to the river water quality detection systems in cities A, B, and C mentioned above, the river water quality detection systems in cities D and E also participate in this linkage learning process.

[0026] Each river water quality detection system is constantly training its own deep learning network. Taking the river water quality detection system in City D as an example, a deep learning network was obtained by deep learning training on a large amount of historical water quality data collected locally. This network has been continuously iterated and optimized, and can predict the water quality status to a certain extent. Similarly, the river water quality detection system in City E also has its own deep learning network.

[0027] The server needs to obtain a shared learning network for the river water quality detection system in City A. First, the server obtains the most recent iteration network of each other training entity (here, the river water quality detection systems in City D and City E). For the river water quality detection system in City D, the server queries its network index sequence (this sequence records the neural network information run by the river water quality detection system in City D, including network iteration identifiers and network index information), and finds its most recent iteration network based on the network iteration identifier. Then, based on the network index information, the specific structure and parameters of this most recent iteration network are obtained. Similarly, the same operation is performed on the river water quality detection system in City E.

[0028] Next, the server needs to detect the most recent iteration network of the river water quality detection system in cities D and E based on the second on-site verification collection data to generate the second water quality status detection data. Assume that the second on-site verification collection data is part of the collection data selected from the on-site collection data of the river water quality detection system in city A, for example, water quality data at different time periods on a certain day. These data are respectively input into the most recent iteration network of the river water quality detection system in cities D and E to obtain the corresponding water quality status detection results, which are the second water quality status detection data.

[0029] Then, based on these second water quality status detection data, the server determines the deep learning network with the best training effect among the most recent iteration networks as the shared learning network. Assuming that after comparison, it is found that the most recent iteration network of the E city river water quality detection system has a higher accuracy rate and a lower misjudgment rate when performing water quality status detection on the second on-site verification collection data of City A, then this most recent iteration network of the E city river water quality detection system is determined as the shared learning network.

[0030] Finally, the server uses this determined shared learning network to predict the water quality status label of the regional water quality monitoring sample of the river water quality detection system in City A (obtained in the previous step S110). For example, the water temperature, pH value, dissolved oxygen content and other parameters in the regional water quality monitoring sample are input into the shared learning network, and the network undergoes a series of calculations (such as convolutional layer, pooling layer, fully connected layer, etc.), and finally outputs the water quality status label prediction data. This prediction data may be label information about whether the water quality is polluted, whether the degree of pollution is mild, moderate or severe, etc.

[0031] Step S130, performing parameter learning on a target water quality assessment model according to the water quality status tag prediction data, and determining a target application program corresponding to the target water quality assessment model that has completed parameter learning.

[0032] In this scenario, assume that the target water quality assessment model in the river water quality detection system of City A is a model built based on a neural network. The initial structure of this model includes an input layer, several hidden layers, and an output layer. The input layer receives various water quality parameters, the hidden layer extracts and converts data features, and the output layer outputs the assessment results of the water quality status.

[0033] The server first performs parameter learning on the target water quality assessment model based on the water quality status label prediction data. For example, the server obtains the remaining field collection data, where the remaining field collection data is part or all of the field collection data of the river water quality detection system in City A, except for the second field verification collection data previously used to determine the shared learning network. Assuming that the second field verification collection data previously was data from a certain day, the remaining field collection data may be data from other dates.

[0034] The water quality status label prediction data and the remaining field collected data are used as target sample learning data to learn the parameters of the target water quality assessment model. During the learning process, the model adjusts the weights and biases and other parameters in the neural network based on the input target sample learning data through the back propagation algorithm. For example, if the water quality status label prediction data indicates that the water quality in a certain area is slightly polluted, and the dissolved oxygen content in the target sample learning data is low and the COD value is high, then the model will adjust the weight parameters related to dissolved oxygen and COD in the neural network based on this information, so that the model can more accurately reflect this relationship in subsequent predictions.

[0035] Then, the server detects the first water quality detection network that has completed parameter learning (here, the first water quality detection network is generated by the target water quality assessment model after deep learning based on the water quality status label prediction data) based on the first on-site verification collection data to generate the first water quality status detection data. The first on-site verification collection data is the water quality parameter collection data in the on-site water quality parameter collection data sequence of the river water quality detection system in City A. Assume that the first on-site verification collection data is the latest set of data collected, including parameters such as water temperature, pH value, and dissolved oxygen content. These data are input into the first water quality detection network that has completed parameter learning. The network outputs the detection result of the water quality status after calculation. This result is the first water quality status detection data.

[0036] If the first water quality status detection data meets the target requirements, for example, the accuracy of the predicted water quality status matching the actual water quality detection results (obtained through more accurate laboratory analysis or other high-precision detection methods) reaches the set threshold (such as 90%), then the server will output the first water quality detection network that has completed parameter learning as the target application that has completed parameter learning corresponding to the target water quality assessment model. This target application will be used for more accurate assessment and prediction of the water quality of the river in City A in the future.

[0037] Step S140, loading the regional water quality monitoring sample into the target application program corresponding to the target water quality assessment model that has completed parameter learning, and generating optimized water quality status label prediction data.

[0038] In this step, the server has determined the target application corresponding to the target water quality assessment model that has completed parameter learning. The server again obtains the regional water quality monitoring sample of the river water quality detection system in City A (similar to the regional water quality monitoring sample in step S110, but may be newly collected data).

[0039] Load this regional water quality monitoring sample into the target application. For example, the water temperature in the regional water quality monitoring sample is 22 degrees Celsius, the pH value is 7.3, the dissolved oxygen content is 7 mg / L, the COD is 18 mg / L, and other parameters are input as input data into the target application (that is, the neural network structure corresponding to the target water quality assessment model that completes parameter learning).

[0040] The neural network structure in the target application starts processing these input data. The input layer receives these water quality parameters, and then passes through the hidden layer for feature extraction and transformation. In the hidden layer, different neurons calculate the input data according to the previously learned weights and bias parameters. For example, the neuron related to dissolved oxygen content calculates the value of 7mg / L according to the previously adjusted weight parameters. After being processed by multiple hidden layers, the data finally reaches the output layer.

[0041] The output layer outputs optimized water quality status label prediction data based on the previous training results. This optimized prediction data may be more accurate than the previous one (the water quality status label prediction data in step S120). For example, the previous prediction may simply divide the water quality status into polluted and unpolluted, but now after being processed by the target application, it can more accurately predict that the water quality is slightly polluted and the main pollution factor is slightly low dissolved oxygen content, etc. More detailed water quality status label prediction data. Such optimized data is of great significance for accurately assessing river water quality and taking targeted governance measures.

[0042] Based on the above steps, the embodiment of the present application obtains the on-site collected data of the river water quality detection system as regional water quality monitoring samples, and uses a shared learning network to predict the water quality status labels of these regional water quality monitoring samples, thereby significantly improving the accuracy and efficiency of water quality detection. Specifically, by selecting the deep learning network with the best training effect as a shared learning network from multiple training entities participating in the linkage network parameter learning, the reliability and high precision of the water quality status label prediction data are ensured. Furthermore, based on these predicted data, the target water quality assessment model is parameter learned, and the optimized target application is determined, so that more accurate water quality status label prediction data can be generated. This method not only improves the level of intelligence of water quality detection, but also effectively reduces the human errors and time costs that may exist in traditional water quality detection methods.

[0043] In a possible implementation, the parameter learning of the target water quality assessment model is performed based on the water quality status tag prediction data, and the target application corresponding to the target water quality assessment model for completing the parameter learning is determined, specifically including: Based on the water quality status label prediction data, parameter learning is performed on the target water quality assessment model to generate a first water quality detection network that has completed parameter learning. The target water quality assessment model performs deep learning based on the water quality status label prediction data.

[0044] Based on the first on-site verification and collection data, the first water quality detection network that has completed parameter learning is detected to generate first water quality status detection data. The first on-site verification and collection data is the water quality parameter collection data in the on-site water quality parameter collection data sequence of the river water quality detection system.

[0045] If the first water quality status detection data meets the target requirements, the first water quality detection network that has completed parameter learning is output as a target application program that has completed parameter learning corresponding to the target water quality assessment model.

[0046] In a possible implementation, the shared learning network is obtained by the river water quality detection system from other training entities participating in the linkage network parameter learning, specifically including: Get the most recent iteration network for each other training entity.

[0047] Based on the second on-site verification collected data, each most recent iteration network is detected to generate second water quality status detection data, and the second on-site verification collected data is part of the on-site collected data of the river water quality detection system.

[0048] According to the second water quality status detection data, the deep learning network with the best training effect in each most recent iteration network is output as the shared learning network.

[0049] In a possible implementation, the step of performing parameter learning on the target water quality assessment model based on the water quality status label prediction data to generate a first water quality detection network that has completed parameter learning specifically includes: Obtain the remaining on-site collected data, which are part or all of the on-site collected data except the second on-site verification collected data.

[0050] The water quality status label prediction data and the remaining field collected data are used as target sample learning data to perform parameter learning on the target water quality assessment model.

[0051] In a possible implementation manner, obtaining the most recent iterative network of each other training entity specifically includes: Obtain a network index sequence. The network index sequence is used to record the neural network information run by each training entity participating in the linkage network parameter learning, and the neural network information includes a network iteration identifier and network index information.

[0052] Based on the network iteration identifier, the most recent iteration network of each other training entity is determined.

[0053] Based on the network index information of the most recent iteration network of each other training entity, the most recent iteration network of each other training entity is obtained.

[0054] In a possible implementation, the target water quality assessment model includes the neural network to be optimized in the river water quality detection system. The target application includes an optimized neural network corresponding to the neural network to be optimized. The method further includes: The neural network to be optimized is tested based on the first on-site verification and collection data to generate third water quality status test data.

[0055] In a possible implementation, the method further includes: The first on-site collected data is obtained, and the target water quality assessment model is parameter-learned to generate a second water quality detection network that has completed parameter learning. The first on-site collected data is part or all of the water quality parameter collection data in the on-site water quality parameter collection data sequence of the river water quality detection system.

[0056] Based on the first on-site verification and collection data, the second water quality detection network that has completed parameter learning is detected to generate fourth water quality status detection data.

[0057] In a possible implementation, the method further includes: The iteration identifier and network index information of the target application are saved in a network index sequence. The network index sequence is used to store the neural network information run by each training entity for linkage network parameter learning. The neural network information includes the network iteration identifier and the network index information.

[0058] In the above embodiments, the artificial intelligence-based river water quality detection system for executing the above method embodiments has at least one processor, a control module (chip set) coupled to at least one of the (at least one) processors, a memory coupled to the control module, a non-volatile memory (NVM) / storage device coupled to the control module, at least one load / output device coupled to the control module, and a network interface coupled to the control module.

[0059] The processor may include at least one single-core or multi-core processor, and the processor may include any combination of general-purpose processors or special-purpose processors (such as graphics processors, application processors, baseband processors, etc.). For some alternative implementations, the river water quality detection system based on artificial intelligence can be used as the gateway described in the embodiments of the present application. Based on electronic devices.

[0060] For some alternative embodiments, the artificial intelligence-based river water quality detection system may include at least one computer-readable medium (e.g., a memory or NVM / storage device) having instructions and at least one processor integrated with the at least one computer-readable medium and configured to execute instructions to implement a module to perform the actions described in the present disclosure.

[0061] For one embodiment, the control module may include any suitable interface controller to provide any suitable interface to at least one of the processor(s) and / or any suitable device or component in communication with the control module.

[0062] The control module may include a memory controller module to provide an interface to the memory. The memory controller module may be a hardware module, a software module, and / or a firmware module.

[0063] The memory may be used, for example, to load and store data and / or instructions for an artificial intelligence-based river water quality detection system. For one embodiment, the memory may include any suitable volatile memory, for example, a suitable DRAM.

[0064] For one embodiment, the control module may include at least one load-in / load-out controller to provide an interface to the NVM / storage device and the (at least one) load-in / load-out device.

[0065] For example, the NVM / storage device may be used to store data and / or instructions. The NVM / storage device may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable (at least one) non-volatile storage device (e.g., at least one hard disk drive (HDD), at least one compact disk (CD) drive, and / or at least one digital versatile disk (DVD) drive).

[0066] The NVM / storage device may include storage resources that are physically part of the device on which the artificial intelligence-based river water quality detection system is installed, or it may be accessible to the device without being part of the device. For example, the NVM / storage device may be accessed via (at least one) load / output device based on a network.

[0067] (At least one) load-in / output device may provide an interface for the artificial intelligence-based river water quality detection system to communicate with any other appropriate device, and the load-in / output device may include a communication component, a pinyin component, a sensor component, etc. The network interface may provide an interface for the artificial intelligence-based river water quality detection system to communicate based on at least one network, and the artificial intelligence-based river water quality detection system may wirelessly communicate with at least one component of a wireless network based on any of at least one wireless network priors and / or protocols, such as accessing a wireless network based on a communication prior.

[0068] For one embodiment, at least one of the (at least one) processor may be loaded together with the logic of at least one controller of the control module (e.g., a memory controller module). For one embodiment, at least one of the (at least one) processor may be loaded together with the logic of at least one controller of the control module to form a system level load. For one embodiment, at least one of the (at least one) processor may be fused on the same die with the logic of at least one controller of the control module. For one embodiment, at least one of the (at least one) processor may be fused on the same die with the logic of at least one controller of the control module to form a system on chip (SoC).

[0069] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, based on the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0070] An embodiment of the present invention discloses a computer-readable storage medium, which stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the river water quality detection method based on artificial intelligence described in the aforementioned embodiment.

[0071] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the river water quality detection method based on artificial intelligence described in the aforementioned embodiment.

[0072] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed over multiple modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. Those of ordinary skill in the art may understand and implement the present invention without creative effort.

[0073] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory or other optical disk storage, magnetic disk storage, magnetic tape storage, or any other medium that can be used to have or store data and is computer-readable.

[0074] Finally, it should be noted that what is disclosed above is only a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A river water quality detection method based on artificial intelligence, characterized in that: The method comprises: Acquire regional water quality monitoring samples, where the regional water quality monitoring samples are field-collected data from a river water quality detection system, and the river water quality detection system is any training entity that performs linkage network parameter learning; Predicting water quality status labels of the regional water quality monitoring samples based on a shared learning network to generate water quality status label prediction data, wherein the shared learning network is obtained by the river water quality detection system from other training entities participating in the linkage network parameter learning, and the shared learning network is a deep learning network with the best training effect among other training entities; Based on the water quality status label prediction data, parameter learning is performed on the target water quality assessment model, and a target application corresponding to the target water quality assessment model for completing parameter learning is determined; The regional water quality monitoring samples are loaded into the target application corresponding to the target water quality assessment model that has completed parameter learning, and optimized water quality status label prediction data is generated.

2. The method for detecting river water quality based on artificial intelligence according to claim 1, characterized in that: The step of performing parameter learning on a target water quality assessment model based on the water quality status tag prediction data and determining a target application program corresponding to the target water quality assessment model and completing parameter learning specifically includes: Based on the water quality status label prediction data, parameter learning is performed on a target water quality assessment model to generate a first water quality detection network in which parameter learning is completed, and the target water quality assessment model performs deep learning based on the water quality status label prediction data; According to the first on-site verification collected data, the first water quality detection network that has completed parameter learning is detected to generate first water quality status detection data, wherein the first on-site verification collected data is water quality parameter collection data in the on-site water quality parameter collection data sequence of the river water quality detection system; If the first water quality status detection data meets the target requirements, the first water quality detection network that has completed parameter learning is output as a target application program that has completed parameter learning corresponding to the target water quality assessment model.

3. The method for detecting river water quality based on artificial intelligence according to claim 1, characterized in that: The shared learning network is obtained by the river water quality detection system from other training entities participating in the linkage network parameter learning, specifically including: Get the most recent iteration network of each other training entity; According to the second on-site verification collected data, each most recent iteration network is tested to generate second water quality status detection data, wherein the second on-site verification collected data is part of the on-site collected data of the river water quality detection system; According to the second water quality status detection data, the deep learning network with the best training effect in each most recent iteration network is output as the shared learning network.

4. The method for detecting river water quality based on artificial intelligence according to claim 2 is characterized in that: The step of performing parameter learning on the target water quality assessment model based on the water quality status label prediction data to generate a first water quality detection network that has completed parameter learning specifically includes: Acquire the remaining on-site collected data; the remaining on-site collected data is part or all of the on-site collected data except the second on-site verification collected data; The water quality status label prediction data and the remaining field collected data are used as target sample learning data to perform parameter learning on the target water quality assessment model.

5. The method for detecting river water quality based on artificial intelligence according to claim 3 is characterized in that: The obtaining of the most recent iteration network of each other training entity specifically includes: Acquire a network index sequence; the network index sequence is used to record the neural network information run by each training entity participating in the linkage network parameter learning, and the neural network information includes a network iteration identifier and a network index information; Based on the network iteration identifier, determining the most recent iteration network of each other training entity; Based on the network index information of the most recent iteration network of each other training entity, the most recent iteration network of each other training entity is obtained.

6. The method for detecting river water quality based on artificial intelligence according to claim 2, characterized in that: The target water quality assessment model includes the neural network to be optimized in the river water quality detection system; the target application includes the optimized neural network corresponding to the neural network to be optimized; the method further includes: The neural network to be optimized is tested based on the first on-site verification and collection data to generate third water quality status test data.

7. The method for detecting river water quality based on artificial intelligence according to claim 2, characterized in that: The method further comprises: Acquire first on-site collected data, perform parameter learning on the target water quality assessment model, and generate a second water quality detection network that has completed parameter learning; the first on-site collected data is part or all of the water quality parameter collection data in the on-site water quality parameter collection data sequence of the river water quality detection system; Based on the first on-site verification and collection data, the second water quality detection network that has completed parameter learning is detected to generate fourth water quality status detection data.

8. The method for detecting river water quality based on artificial intelligence according to claim 2 is characterized in that: The method further comprises: The iteration identifier and network index information of the target application are saved in a network index sequence; the network index sequence is used to store the neural network information run by each training entity for linkage network parameter learning; the neural network information includes a network iteration identifier and network index information.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores machine executable instructions, and when the machine executable instructions are executed by a computer, the river water quality detection method based on artificial intelligence described in any one of claims 1 to 8 is implemented.

10. A river water quality detection system based on artificial intelligence, characterized in that: It includes a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are executed by a computer, the river water quality detection method based on artificial intelligence described in any one of claims 1 to 8 is implemented.

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