CN116629550B - Water environment supervision method and scheduling operation system based on cloud computing - Google Patents
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Abstract
The application provides a water environment supervision method and a dispatching operation system based on cloud computing, which relate to the technical field of intelligent supervision, and the method comprises the following steps: the method comprises the steps of reading a historical data set of a historical data base of a monitored water environment, then carrying out external environment feature identification and internal biological feature identification, constructing a mapping relation between water environment information and a scheduling control result based on the identification result, carrying out real-time water quality monitoring by a temperature monitoring device to obtain a temperature data acquisition result, carrying out scheduling control result matching by a water quality monitoring device based on the mapping relation to obtain a water quality monitoring result, and controlling the scheduling control device to carry out water environment monitoring and optimizing after the matching result is obtained, so that the technical problems that the water environment and the state of the cultured living being cannot be accurately adapted in time in the prior art, and the cultured living being cannot safely and healthily grow are solved, the monitoring and the adjusting of the water environment are realized, and the survival rate of the cultured living being is improved.
Description
Technical Field
The application relates to the technical field of intelligent supervision, in particular to a water environment supervision method and a dispatching operation system based on cloud computing.
Background
The water environment refers to the environment in which water is formed, distributed and transformed in the natural world. Refers to the population surrounding space and the water body which can directly or indirectly influence the life and development of human beings, and the general of various natural factors and related social factors of the normal functions of the water body. With the continuous improvement of the living standard of people, the demand of people for seafood products, especially fish, is increasing, the area and the yield of freshwater aquaculture in China are in the first place in the world, under such a large background, the improvement of freshwater fish aquaculture can have profound influence on the aquaculture industry in China, the healthy growth of fish is not separated from a high-quality water environment, the rapid development of the current social industry is a certain damage to the environment, especially the pollution to water sources is more serious, the pollution of the water sources not only threatens the physical health of people, but also seriously hinders the healthy development of the aquaculture.
In the prior art, the water environment monitoring management cannot be accurately and timely carried out, so that the water environment and the state of the cultured organisms cannot be accurately matched, and the cultured organisms cannot grow safely and healthily.
Disclosure of Invention
The application provides a water environment supervision method and a scheduling operation system based on cloud computing, which are used for solving the technical problems that in the prior art, water environment monitoring management cannot be accurately and timely carried out, so that the water environment and the state of cultured organisms cannot be accurately adapted, and the cultured organisms cannot grow safely and healthily.
In view of the problems, the application provides a water environment supervision method and a dispatching operation system based on cloud computing.
In a first aspect, the present application provides a water environment supervision method based on cloud computing, the method comprising: connecting a historical database for monitoring the water environment, and reading a historical data set of the monitored water environment; performing external environment characteristic identification and internal biological characteristic identification on the historical data set, and constructing a mapping relation between water environment information and a scheduling control result based on an identification result; acquiring temperature data of the monitored water environment through the temperature monitoring device to obtain a temperature data acquisition result; the water quality monitoring device is used for carrying out real-time water quality monitoring to obtain a water quality monitoring result; taking the temperature data acquisition result and the water quality monitoring result as matching characteristics, and carrying out scheduling control result matching based on the mapping relation to obtain a matching result; and controlling the scheduling control device to perform water environment supervision optimization based on the matching result.
In a second aspect, the application provides a cloud computing-based water environment supervision and scheduling operation system, which comprises: the reading module is used for connecting a historical database for monitoring the water environment and reading a historical data set for monitoring the water environment; the identification module is used for carrying out external environment characteristic identification and internal biological characteristic identification on the historical data set, and constructing a mapping relation between water environment information and a scheduling control result based on an identification result; the monitoring module is used for acquiring temperature data of the monitored water environment through the temperature monitoring device to obtain a temperature data acquisition result; the water quality monitoring module is used for carrying out real-time water quality monitoring through the water quality monitoring device to obtain a water quality monitoring result; the matching feature module is used for taking the temperature data acquisition result and the water quality monitoring result as matching features, and carrying out scheduling control result matching based on the mapping relation to obtain a matching result; and the optimizing module is used for controlling the scheduling control device to perform water environment supervision optimization based on the matching result.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
the application provides a water environment supervision method based on cloud computing, relates to the technical field of intelligent supervision, and solves the technical problems that in the prior art, water environment and a farmed organism state cannot be accurately adapted and the farmed organism cannot grow safely and healthily due to the fact that water environment monitoring management cannot be accurately and timely carried out, and the supervision and adjustment of the water environment are realized, so that the survival rate of the farmed organism is improved.
Drawings
FIG. 1 is a schematic flow chart of a water environment supervision method based on cloud computing;
fig. 2 is a schematic diagram of a flow for obtaining matching characteristics in a water environment supervision method based on cloud computing;
fig. 3 is a schematic diagram of a scheduling control correction flow in a water environment supervision method based on cloud computing;
fig. 4 is a schematic structural diagram of a water environment supervision and scheduling operation system based on cloud computing.
Reference numerals illustrate: the system comprises a reading module 1, an identification module 2, a monitoring module 3, a water quality monitoring module 4, a matching characteristic module 5 and an optimizing module 6.
Detailed Description
The application provides a water environment supervision method and a dispatching operation system based on cloud computing, which are used for solving the technical problems that the water environment and the state of the cultured organisms cannot be accurately adapted and the cultured organisms cannot grow safely and healthily because the water environment cannot be accurately and timely monitored and managed in the prior art.
Example 1
As shown in fig. 1, the embodiment of the application provides a water environment supervision method based on cloud computing, which is applied to a dispatching operation system, wherein the dispatching operation system is in communication connection with a temperature monitoring device, a water quality monitoring device and a dispatching control device, and the method comprises the following steps:
step S100: connecting a historical database for monitoring the water environment, and reading a historical data set of the monitored water environment;
specifically, the water environment supervision method based on cloud computing is applied to a dispatching operation system, and the dispatching operation system is in communication connection with a temperature monitoring device, a water quality monitoring device and a dispatching control device, wherein the temperature monitoring device, the water quality monitoring device and the dispatching control device are used for collecting water environment parameters.
Because the water environment is a water body which can directly or indirectly influence the life and development of human beings, the water environment needs to be monitored in real time, in order to achieve the supervision optimization of the water environment, firstly, a historical database connected with the monitored water environment needs to be subjected to data reading, the historical database connected with the monitored water environment can contain historical external environment data of the water environment, historical internal biological data of the water environment, water quality data of the water environment and the like, and the data in the historical database is read and integrated, so that a read historical data set for monitoring the water environment is obtained, and the supervision optimization of the water environment is realized for later period as an important reference basis.
Step S200: performing external environment characteristic identification and internal biological characteristic identification on the historical data set, and constructing a mapping relation between water environment information and a scheduling control result based on an identification result;
specifically, on the basis of the read historical data set, the data set is subjected to external environment characteristic identification of the water environment and internal biological characteristic identification of the water environment, namely the external environment characteristic refers to environment characteristics which affect the water environment in the environment where the current water environment is located, for example, whether the current external environment has drought or raining or not so as to affect the current water environment, the external environment characteristics are identified, the internal biological characteristics refer to the aquatic biological characteristics which affect the current water environment, such as internal organisms of farmed organisms, algae, plankton, benthos, aquatic plants and the like, the internal biological characteristics which affect the water environment are identified, the relation existing between the current water environment information and the scheduling control result is further mapped on the basis of the external environment characteristic identification result and the internal biological characteristic identification result, and therefore the mapping relation between the water environment information and the scheduling control result is obtained, and supervision optimization of the water environment is guaranteed.
Step S300: acquiring temperature data of the monitored water environment through the temperature monitoring device to obtain a temperature data acquisition result;
specifically, because the temperature of water changes along with the change of sunlight and air temperature, the temperature of the current water environment needs to be monitored in real time, namely, the temperature monitoring device connected with the scheduling operation system is used for collecting and monitoring the temperature data of the current water environment in real time, the monitored water environment temperature data can be between 4 ℃ and 25 ℃, so that the temperatures corresponding to the water environment are collected in real time, the temperature data collection result of the water environment is obtained, and the basis for monitoring, optimizing and tamping the water environment is realized for the follow-up.
Step S400: the water quality monitoring device is used for carrying out real-time water quality monitoring to obtain a water quality monitoring result;
specifically, in the process of monitoring and optimizing the water environment, the current water environment water quality is firstly required to be obtained, the current water environment water quality is monitored in real time by a water quality monitoring device which is in communication connection with a scheduling operation system, the water quality monitoring can be a process of monitoring and measuring the types of pollutants, the concentration and the change trend of various pollutants in the current water environment and evaluating the water quality condition, so that a water quality monitoring result corresponding to the current water environment is obtained, and a foundation is laid for monitoring and optimizing the water environment for subsequent realization.
Step S500: taking the temperature data acquisition result and the water quality monitoring result as matching characteristics, and carrying out scheduling control result matching based on the mapping relation to obtain a matching result;
specifically, based on the temperature data acquisition result acquired by the temperature monitoring device and the water quality monitoring result acquired by the water quality monitoring device, the temperature data acquisition result is used as a matching feature, namely, multi-node temperature extraction is performed on the temperature data acquisition result, temperature change feature extraction is performed on the extracted plurality of node temperatures, the matching feature is correspondingly obtained, further, the mapping relation of the external environment feature identification, the internal biological feature identification and the scheduling control result is called, the scheduling control mapping result is corrected based on relevant technicians, so that a matching model is trained on the basis of the corrected scheduling control mapping result, the obtained matching feature is finally input into the trained matching model, the matching result is correspondingly output, and the effect of limiting the implementation of the supervision optimization of the water environment is achieved.
Step S600: and controlling the scheduling control device to perform water environment supervision optimization based on the matching result.
Specifically, in order to achieve the purpose of optimizing the current water environment while monitoring, firstly, a scheduling control device in communication connection with a scheduling operation system is controlled according to the obtained matching result based on the matching result output by the matching model, namely, the external environment and internal living beings of the current water environment are subjected to monitoring scheduling control according to the mapping relation between the water environment and the scheduling control result based on the temperature and the water quality of the current water environment, so that the water environment is always in a normal temperature and water quality range, and the effect of monitoring and optimizing the water environment is achieved.
Further, as shown in fig. 2, step S500 of the present application further includes:
step S510: performing multi-node temperature extraction on the temperature data acquisition result to obtain a multi-node temperature extraction result;
step S520: reading node spacing, and extracting temperature change characteristics according to the node spacing and the multi-node temperature;
step S530: acquiring external environment temperature data, and generating a predicted temperature influence value based on a temperature change value of the external environment temperature data;
step S540: performing feature correction on the temperature change feature extraction result based on the predicted temperature influence value;
step S550: and obtaining the matching characteristic according to the correction characteristic.
Specifically, in order to accurately obtain the influence of temperature on the water environment, the temperature of the water environment needs to be extracted through a temperature data acquisition result connected with the system, namely, the temperatures corresponding to different moments of the water environment are extracted through nodes with time as a unit, so that the multi-node temperature extraction result is correspondingly obtained, the node spacing can be the node with time as a unit according to the node spacing, the current node spacing is one hour according to one hour, meanwhile, the change characteristics of temperature change is carried out according to the set node spacing and the plurality of node temperatures corresponding to the current nodes, the external environment temperature data of the current water environment is acquired as a basis, the environment temperature change predicted by weather forecast can be contained in the external environment temperature data, so that the predicted temperature influence value is correspondingly generated, the characteristic result of the temperature change is correspondingly corrected, namely, the place where the predicted temperature influence value and the temperature change characteristic are not consistent is corrected, the corrected characteristic is finally recorded as a matching characteristic, and the supervision optimization of the water environment is carried out in the later stage.
Further, step S530 of the present application includes:
step S531: obtaining a time identifier of the external environment temperature data, and classifying the data according to the time identifier to obtain a stored external environment temperature data set and a predicted external environment temperature data set;
step S532: performing node correspondence on the stored external environment temperature dataset based on the time identifier and the node distance, and performing environment influence characteristic analysis according to a corresponding result to obtain environment temperature influence value characteristics and environment temperature influence node characteristics;
step S533: and comparing the stored external environment temperature data set with the predicted external environment temperature data set, and generating the predicted temperature influence value based on the comparison result and the environment temperature influence value characteristic and the environment temperature influence node characteristic.
Specifically, the time points corresponding to the obtained external environment temperature data are identified, then the temperature data of different conditions are classified according to the time identification of the obtained external environment temperature data, so that the existing external environment temperature data set and the predicted external environment temperature data set for predicting the external environment temperature are correspondingly obtained, further, on the basis of the obtained time identification, the obtained external environment temperature data set and the set time node interval are subjected to one-to-one corresponding operation of the temperature data and the nodes, the environmental influence characteristics of the water environment are analyzed according to the corresponding result after the corresponding operation, namely the environmental influence characteristics are extracted from the external environment temperature changes corresponding to different time nodes, the environmental influence value characteristics and the environmental temperature influence node characteristics are obtained, the greater the value in the environmental temperature influence value characteristics is, the degree of the corresponding influence environment temperature is higher, the degree of the corresponding influence temperature in the environmental temperature influence node characteristics is also different, meanwhile, the stored external environment temperature data set and the predicted external environment temperature data set are subjected to temperature comparison, namely the difference between the two temperatures is compared, and the environmental influence value and the corresponding environment influence value are integrated according to the obtained environmental influence value, and the obtained environmental influence value is obtained, and the important environmental influence value is obtained according to the monitoring and the monitoring effect is achieved, and the important period is achieved.
Further, as shown in fig. 3, step S700 of the present application further includes:
step S710: acquiring aquatic organism basic information of the monitored water environment, wherein the aquatic organism basic information comprises aquatic organism type information and aquatic organism growth state information;
step S720: carrying out big data call through the aquatic organism type information, and constructing a state change matching period;
step S730: carrying out real-time state prediction on the aquatic organism based on the aquatic organism basic information and the state change matching period to obtain a real-time state prediction result;
step S740: and taking the real-time state prediction result as an associated matching characteristic, and carrying out scheduling control correction on the scheduling control result.
Specifically, the aquatic organism basic information in the water environment is collected and monitored, the monitoring can be regular monitoring, namely, the aquatic organism in the water environment is monitored by taking 7 days as a period, the monitored aquatic organism basic information can contain aquatic organism type information and aquatic organism growth state information, the aquatic organism type information and aquatic organism growth state information contained in big data are matched and called, namely, growth prediction is carried out in each period according to regular monitoring results, so that the state change matching period is constructed, the aquatic organism is further predicted in real-time state on the basis of the state change matching period and the obtained aquatic organism basic information, and meanwhile, the aquatic organism is used as an associated matching feature to carry out scheduling control correction on a scheduling control result, so that the high efficiency of the water environment monitoring optimization is guaranteed.
Further, the step S500 of the present application further includes:
step S560: invoking the mapping relation among the external environment characteristic identifier, the internal biological characteristic identifier and the scheduling control result, and correcting the scheduling control mapping result based on manual work;
step S570: training to obtain a matching model based on the corrected scheduling control mapping result;
step S580: and inputting the matching characteristics into the matching model, and outputting to obtain the matching result.
Specifically, the mapping relation among the external environment characteristic identification of the water environment, the internal biological characteristic identification of the water environment and the scheduling control result of the water environment is called and extracted, the scheduling control mapping result is corrected by related technicians, namely, the current scheduling control mapping result is matched according to the processing result, if the matching degree is lower than 60%, the current scheduling control mapping result is properly adjusted, the matching model is constructed based on the corrected scheduling control mapping result, the matching model is obtained through training of a training data set and a supervision data set, and each group of training data in the training data set comprises the corrected scheduling control mapping result; the supervision data sets are supervision data corresponding to the training data sets one by one.
The construction process of the matching model comprises the following steps: and inputting each group of training data in the training data set into the matching model, outputting and supervising the matching model through the supervising data corresponding to the group of training data, finishing the current group of training when the output result of the matching model is consistent with the supervising data, finishing the training of all the training data in the training data set, and finishing the training of the matching model.
In order to ensure the accuracy of the matching model, the test data set may be used to perform the test processing of the matching model, for example, the test accuracy may be set to 85%, and if the test accuracy of the test data set satisfies 85%, the construction of the matching model is completed. And finally, inputting the matching characteristics into a matching model, and outputting a matching result.
Further, step S570 of the present application includes:
step S571: constructing a compensation association module;
step S572: adding the compensation association module to the matching model;
step S573: when any scheduling control is completed, the compensation association module is used for carrying out feedback data acquisition of a preset window;
step S574: and performing model compensation on the matching model based on the acquisition result.
Specifically, in order to ensure the output accuracy of the matched model, a compensation association module is constructed, the compensation association module dynamically compensates the scheduling control mapping result in the matched model, so that the normal supervision optimization of the water environment is maintained, meanwhile, the constructed compensation association module is added to the constructed matched model, after any scheduling control in the scheduling control is completed, the preset window feedback data acquisition is performed through the constructed compensation association module, namely, the control result of the current scheduling control is subjected to the preset data feedback acquisition in the compensation association module, and finally, the corresponding model compensation is performed on the matched model through the acquisition result acquired through the preset window feedback data acquisition, so that the technical effect of providing reference for the supervision optimization of the water environment is achieved.
Further, step S580 of the present application includes:
step S581: dividing the water layer region of the monitored water environment to obtain multi-stage water layer water body temperature control response time;
step S582: generating a hierarchical water temperature control compensation parameter according to the multi-level water layer water temperature control response time;
step S583: and optimizing the matching result through the hierarchical water temperature control compensation parameter.
Specifically, because the temperatures and the temperature controls corresponding to the different water depths in the water environment are different, firstly, the area division of the water environment is carried out on the current monitored water environment according to the different water depths in the water environment, meanwhile, the temperatures of the different water layer areas and the temperature controls of the current water layer area are corresponding, the temperature control response time of the multi-level water layer water body is calculated and obtained according to the current water area temperature and the water area depth, further, the temperature control of the current level is controlled and compensated according to the calculated temperature control response time of the multi-level water layer water body, namely, if the temperature control of the current water layer does not reach the required temperature, the temperature compensation is carried out on the current water layer until the required temperature is reached, then the level water temperature control compensation parameter is generated, and finally, the matching result output by the matching model is optimized through the level water temperature control compensation parameter.
Example two
Based on the same inventive concept as the water environment supervision method based on cloud computing in the foregoing embodiment, as shown in fig. 4, the present application provides a water environment supervision and scheduling operation system based on cloud computing, where the system includes:
the reading module 1 is used for connecting a historical database for monitoring the water environment and reading a historical data set for monitoring the water environment;
the identification module 2 is used for carrying out external environment characteristic identification and internal biological characteristic identification on the historical data set, and constructing a mapping relation between water environment information and a scheduling control result based on an identification result;
the monitoring module 3 is used for acquiring temperature data of the monitored water environment through the temperature monitoring device, and acquiring a temperature data acquisition result;
the water quality monitoring module 4 is used for carrying out real-time water quality monitoring through the water quality monitoring device to obtain a water quality monitoring result;
the matching feature module 5 is used for taking the temperature data acquisition result and the water quality monitoring result as matching features, and carrying out scheduling control result matching based on the mapping relation to obtain a matching result;
and the optimizing module 6 is used for controlling the scheduling control device to perform water environment supervision optimization based on the matching result.
Further, the system further comprises:
the temperature extraction module is used for carrying out multi-node temperature extraction on the temperature data acquisition result to obtain a multi-node temperature extraction result;
the characteristic extraction module is used for reading the node spacing and extracting temperature change characteristics according to the node spacing and the multi-node temperature;
the influence value generation module is used for acquiring external environment temperature data and generating a predicted temperature influence value based on a temperature change value of the external environment temperature data;
the characteristic correction module is used for carrying out characteristic correction on the temperature change characteristic extraction result based on the predicted temperature influence value;
the first matching feature obtaining module is used for obtaining the matching features according to the correction features.
Further, the system further comprises:
the data classification module is used for obtaining time identification of the external environment temperature data, classifying the data according to the time identification, and obtaining a stored external environment temperature data set and a predicted external environment temperature data set;
the environment influence characteristic analysis module is used for carrying out node correspondence on the stored external environment temperature data set based on the time mark and the node distance, carrying out environment influence characteristic analysis according to a corresponding result, and obtaining an environment temperature influence value characteristic and an environment temperature influence node characteristic;
and the temperature comparison module is used for comparing the stored external environment temperature data set with the predicted external environment temperature data set, and generating the predicted temperature influence value based on a comparison result, the environment temperature influence value characteristic and the environment temperature influence node characteristic.
Further, the system further comprises:
the aquatic organism monitoring module is used for acquiring aquatic organism basic information of the monitored water environment, wherein the aquatic organism basic information comprises aquatic organism type information and aquatic organism growth state information;
the big data calling module is used for calling big data through the aquatic organism type information and constructing a state change matching period;
the aquatic organism real-time state prediction module is used for predicting the aquatic organism real-time state based on the aquatic organism basic information and the state change matching period to obtain a real-time state prediction result;
and the scheduling control correction module is used for taking the real-time state prediction result as an associated matching characteristic and carrying out scheduling control correction on the scheduling control result.
Further, the system further comprises:
the mapping relation module is used for calling the mapping relation among the external environment characteristic identifier, the internal biological characteristic identifier and the scheduling control result and correcting the scheduling control mapping result based on manual work;
the matching model obtaining module is used for obtaining a matching model based on the corrected scheduling control mapping result training;
and the input module is used for inputting the matching characteristics into the matching model and outputting and obtaining the matching result.
Further, the system further comprises:
the module construction module is used for constructing a compensation association module;
an adding module for adding the compensation association module to the matching model;
the scheduled window feedback data acquisition module is used for acquiring scheduled window feedback data through the compensation association module after any scheduling control is completed;
and the model compensation module is used for carrying out model compensation on the matching model based on the acquisition result.
Further, the system further comprises:
the water layer region dividing module is used for dividing the water layer region of the monitored water environment to obtain multi-stage water layer water body temperature control response time;
the compensation parameter module is used for generating a hierarchical water temperature control compensation parameter according to the multi-level water layer water temperature control response time;
and the matching result optimizing module is used for optimizing the matching result through the hierarchical water temperature control compensation parameter.
Through the foregoing detailed description of a water environment monitoring method based on cloud computing, those skilled in the art can clearly know a water environment monitoring method based on cloud computing and a scheduling operation system in this embodiment, and for the device disclosed in the embodiment, the description is relatively simple because it corresponds to the method disclosed in the embodiment, and relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (4)
1. The water environment supervision method based on cloud computing is characterized by being applied to a dispatching operation system, wherein the dispatching operation system is in communication connection with a temperature monitoring device, a water quality monitoring device and a dispatching control device, and the method comprises the following steps:
connecting a historical database for monitoring the water environment, and reading a historical data set of the monitored water environment;
performing external environment characteristic identification and internal biological characteristic identification on the historical data set, and constructing a mapping relation between water environment information and a scheduling control result based on an identification result;
acquiring temperature data of the monitored water environment through the temperature monitoring device to obtain a temperature data acquisition result;
the water quality monitoring device is used for carrying out real-time water quality monitoring to obtain a water quality monitoring result;
taking the temperature data acquisition result and the water quality monitoring result as matching characteristics, and carrying out scheduling control result matching based on the mapping relation to obtain a matching result;
controlling the scheduling control device to perform water environment supervision optimization based on the matching result; wherein the method comprises the following steps:
acquiring aquatic organism basic information of the monitored water environment, wherein the aquatic organism basic information comprises aquatic organism type information and aquatic organism growth state information;
carrying out big data call through the aquatic organism type information, and constructing a state change matching period;
carrying out real-time state prediction on the aquatic organism based on the aquatic organism basic information and the state change matching period to obtain a real-time state prediction result;
taking the real-time state prediction result as an associated matching feature, and carrying out scheduling control correction on the scheduling control result;
invoking the mapping relation among the external environment characteristic identifier, the internal biological characteristic identifier and the scheduling control result, and correcting the scheduling control mapping result based on manual work;
training to obtain a matching model based on the corrected scheduling control mapping result;
inputting the matching characteristics into the matching model, and outputting to obtain the matching result;
constructing a compensation association module;
adding the compensation association module to the matching model;
when any scheduling control is completed, the compensation association module is used for carrying out feedback data acquisition of a preset window;
performing model compensation on the matching model based on the acquisition result;
dividing the water layer region of the monitored water environment to obtain multi-stage water layer water body temperature control response time;
generating a hierarchical water temperature control compensation parameter according to the multi-level water layer water temperature control response time;
and optimizing the matching result through the hierarchical water temperature control compensation parameter.
2. The method of claim 1, wherein the method comprises:
performing multi-node temperature extraction on the temperature data acquisition result to obtain a multi-node temperature extraction result;
reading node spacing, and extracting temperature change characteristics according to the node spacing and the multi-node temperature;
acquiring external environment temperature data, and generating a predicted temperature influence value based on a temperature change value of the external environment temperature data;
performing feature correction on the temperature change feature extraction result based on the predicted temperature influence value;
and obtaining the matching characteristic according to the correction characteristic.
3. The method according to claim 2, wherein the method comprises:
obtaining a time identifier of the external environment temperature data, and classifying the data according to the time identifier to obtain a stored external environment temperature data set and a predicted external environment temperature data set;
performing node correspondence on the stored external environment temperature dataset based on the time identifier and the node distance, and performing environment influence characteristic analysis according to a corresponding result to obtain environment temperature influence value characteristics and environment temperature influence node characteristics;
and comparing the stored external environment temperature data set with the predicted external environment temperature data set, and generating the predicted temperature influence value based on the comparison result and the environment temperature influence value characteristic and the environment temperature influence node characteristic.
4. The utility model provides a water environment supervision dispatch operation system based on cloud calculates, its characterized in that, system and temperature monitoring device, water quality monitoring device, dispatch controlling means communication connection, the system includes:
the reading module is used for connecting a historical database for monitoring the water environment and reading a historical data set for monitoring the water environment;
the identification module is used for carrying out external environment characteristic identification and internal biological characteristic identification on the historical data set, and constructing a mapping relation between water environment information and a scheduling control result based on an identification result;
the monitoring module is used for acquiring temperature data of the monitored water environment through the temperature monitoring device to obtain a temperature data acquisition result;
the water quality monitoring module is used for carrying out real-time water quality monitoring through the water quality monitoring device to obtain a water quality monitoring result;
the matching feature module is used for taking the temperature data acquisition result and the water quality monitoring result as matching features, and carrying out scheduling control result matching based on the mapping relation to obtain a matching result;
the optimizing module is used for controlling the scheduling control device to conduct water environment supervision optimization based on the matching result;
the aquatic organism monitoring module is used for acquiring aquatic organism basic information of the monitored water environment, wherein the aquatic organism basic information comprises aquatic organism type information and aquatic organism growth state information;
the big data calling module is used for calling big data through the aquatic organism type information and constructing a state change matching period;
the aquatic organism real-time state prediction module is used for predicting the aquatic organism real-time state based on the aquatic organism basic information and the state change matching period to obtain a real-time state prediction result;
the scheduling control correction module is used for taking the real-time state prediction result as an associated matching characteristic and carrying out scheduling control correction on the scheduling control result;
the mapping relation module is used for calling the mapping relation among the external environment characteristic identifier, the internal biological characteristic identifier and the scheduling control result and correcting the scheduling control mapping result based on manual work;
the matching model obtaining module is used for obtaining a matching model based on the corrected scheduling control mapping result training;
the input module is used for inputting the matching characteristics into the matching model and outputting and obtaining the matching result;
the module construction module is used for constructing a compensation association module;
an adding module for adding the compensation association module to the matching model;
the scheduled window feedback data acquisition module is used for acquiring scheduled window feedback data through the compensation association module after any scheduling control is completed;
the model compensation module is used for carrying out model compensation on the matching model based on the acquisition result;
the water layer region dividing module is used for dividing the water layer region of the monitored water environment to obtain multi-stage water layer water body temperature control response time;
the compensation parameter module is used for generating a hierarchical water temperature control compensation parameter according to the multi-level water layer water temperature control response time;
and the matching result optimizing module is used for optimizing the matching result through the hierarchical water temperature control compensation parameter.
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