CN112883581B - Hydropower station production control processing method and system - Google Patents
Hydropower station production control processing method and system Download PDFInfo
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Abstract
The embodiment of the application provides a hydropower station production control processing method and system, wherein edge computing interface equipment monitors the running state of hydropower station field equipment according to production process data to obtain running state statistical information, and calculates the production process data according to a big data computing model issued by an edge cloud platform to control the hydropower station field equipment. On the other hand, the edge cloud platform can perform state monitoring on the running state statistical information according to a state monitoring model issued by the central cloud platform so as to perform edge control on the hydropower station field device through the edge computing interface device. Therefore, the real-time performance and the agility of the edge computing interface equipment close to the data acquisition side are fully considered, network congestion can be improved to a certain extent, the safety production capacity is improved, the edge cloud computing is utilized to analyze and mine production process data, and the personnel efficiency is improved.
Description
Technical Field
The application relates to the technical field of hydropower stations, in particular to a hydropower station production control processing method and system.
Background
The existing hydropower station production control system is constructed by field devices, a Programmable Logic Controller (PLC), an industrial Ethernet and a man-machine interface unit, so that the automation of the production process is realized, and the working intensity of operators is reduced. However, with the operation of the unit, the accumulation of a large amount of field production data easily causes network congestion and affects safety production, and advanced intelligent technology cannot be used for data formation analysis and mining, so that the personnel efficiency is improved.
Disclosure of Invention
Based on the defects of the existing design, the method and the system for hydropower station production control processing are provided, the real-time performance and the agility of the edge computing interface equipment close to the data acquisition side are fully considered, network congestion can be improved to a certain extent, the safety production capacity is improved, the production process data are analyzed and mined by utilizing edge cloud computing, and the personnel efficiency is improved.
According to a first aspect of the present application, there is provided a hydropower station production control processing method applied to a hydropower station production control processing system, where the hydropower station production control processing system includes a hydropower station field device, an edge computing interface device, an edge cloud platform, and a center cloud platform, and the method includes:
the hydropower station field device sends the production process data of the hydropower station field to the edge computing interface device;
the edge computing interface device monitors the running state of the hydropower station field device according to the production process data to obtain running state statistical information, sends the running state statistical information to the edge cloud platform, computes the production process data according to a big data computing model issued by the edge cloud platform, and sends a control instruction to the hydropower station field device according to a computing result;
the edge cloud platform carries out state monitoring on the running state statistical information according to a state monitoring model issued by the central cloud platform, and sends an edge control instruction to the edge computing interface device according to a state detection result, so that the edge computing interface device sends the edge control instruction to the hydropower station field device;
and the central cloud platform sends the state monitoring model and the big data computing model obtained by training to the edge cloud platform, and configures the edge computing service of the edge cloud platform.
In a possible implementation manner of the first aspect, the step of monitoring, by the edge computing interface device, the operation state of the field device of the hydropower station according to the production process data to obtain the operation state statistical information includes:
analyzing the production process data in each production monitoring period respectively, and determining the state information of the hydropower station field equipment in each state dimension;
and summarizing the state information of the hydropower station field equipment in each state dimension to obtain the running state statistical information of the hydropower station field equipment.
In a possible implementation manner of the first aspect, the step of calculating the production process data according to a big data calculation model issued by the edge cloud platform and sending a control instruction to the hydropower station field device according to a calculation result includes:
calculating the operation data of each production process in the production process data according to the big data calculation model to obtain operation difference data between the operation data of each production process and the operation data of the production process under the corresponding big data calculation model and difference items corresponding to the operation difference data;
and acquiring a control instruction matched with the operation difference data from a control instruction database corresponding to the difference project, and sending the control instruction to the hydropower station field equipment.
In a possible implementation manner of the first aspect, the step of performing, by the edge cloud platform, state monitoring on the running state statistical information according to a state monitoring model issued by the center cloud platform, and sending an edge control instruction to the edge computing interface device according to a state detection result includes:
acquiring state change information of each operation state from the operation state statistical information;
calculating the fitness of target state change information corresponding to each to-be-controlled category in a to-be-controlled category set corresponding to hydropower station field equipment and the state change information of each operating state according to the state monitoring model, wherein the state change information comprises state change time corresponding to state change and a state change rate corresponding to the state change time, the to-be-controlled category set comprises a plurality of to-be-controlled categories, the fitness of the target state change information corresponding to the to-be-controlled category and the state change information of each operating state is calculated according to the historical state change rate corresponding to a plurality of target time intervals of the target state change information, and then the fitness of the target state change information corresponding to the to-be-controlled category and the current state change rate corresponding to the state change information of each operating state is obtained according to the difference between each historical state change rate and the current state change rate corresponding to the state change information of each operating state, and the duration corresponding to each target time interval is matched with the state change time;
obtaining an edge control instruction corresponding to the state change information of each operation state according to the fitness of each category to be controlled and the state change information of each operation state;
and sending an edge control instruction corresponding to the state change information of each running state to the edge computing interface equipment.
In a possible implementation manner of the first aspect, the fitness of the target state change information and the state change information of each operating state is calculated by:
obtaining a plurality of target time intervals of the target state change information according to the state change time matching;
acquiring a historical state change rate corresponding to each target time interval;
according to the difference between the historical state change rate and the current state change rate, screening a selected historical state change rate matched with the current state change rate from the historical state change rate;
and calculating the fitness of the target state change information and the state change information of each running state according to the quantity of the selected historical state change rates and the quantity of the historical state change rates.
In a possible implementation manner of the first aspect, the current state change rate corresponding to the state change information of each operating state includes a set state change abnormal rise degree and a set state change abnormal decrease degree, and the calculating of the fitness of the target state change information and the state change information of each operating state includes:
obtaining historical state change rates corresponding to a plurality of target time intervals of the target state change information;
calculating to obtain the fitness of a first dimensionality of the target state change information and the state change information of each running state according to the difference between the target historical state change abnormal pull-up degree and the set state change abnormal pull-up degree in the historical state change rate;
screening out a second dimension matching abnormal change attenuation degree which is smaller than the set state change abnormal change attenuation degree from the historical state change abnormal change attenuation degrees, wherein the first dimension and the second dimension are opposite dimensions;
calculating to obtain a target abnormal movement weakening degree according to the number of the matched abnormal movement weakening degrees of the second dimension and the number of the historical state change rates;
acquiring a second-dimension matching influence factor corresponding to the state change transaction attenuation degree;
calculating the fitness of the second dimension of the target state change information and the state change information of each running state according to the matching influence factor of the second dimension and the target abnormal change attenuation degree;
and calculating the fitness of the target state change information and the state change information of each running state according to the fitness of the first dimension and the fitness of the second dimension.
In a possible implementation manner of the first aspect, the step of calculating a first-dimension fitness of the target state change information and the state change information of each operating state according to a difference between each target historical state change abnormal pull-up degree in the historical state change rate and the set state change abnormal pull-up degree includes:
screening out a matching abnormal movement pull-up degree of a first dimension which is greater than the set state change abnormal movement pull-up degree from the target historical state change abnormal movement pull-up degree;
calculating to obtain a target abnormal movement pull-up degree according to the number of the matched abnormal movement pull-up degrees of the first dimension and the number of the historical state change rates;
acquiring a first-dimension matching influence factor corresponding to the state change abnormal pull-up degree;
and calculating the fitness of the first dimension of the target state change information and the state change information of each running state according to the matching influence factor of the first dimension and the target abnormal change pull-up degree.
According to a second aspect of the present application, there is also provided a hydropower station production control processing system, which includes a hydropower station field device, an edge computing interface device, an edge cloud platform, and a center cloud platform;
the hydropower station field device is used for sending production process data of a hydropower station field to the edge computing interface device;
the edge computing interface device is used for monitoring the running state of the hydropower station field device according to the production process data to obtain running state statistical information, sending the running state statistical information to the edge cloud platform, computing the production process data according to a big data computing model issued by the edge cloud platform, and sending a control instruction to the hydropower station field device according to a computing result;
the edge cloud platform is used for carrying out state monitoring on the running state statistical information according to a state monitoring model issued by the central cloud platform and sending an edge control instruction to the edge computing interface device according to a state detection result, so that the edge computing interface device sends the edge control instruction to the hydropower station field device;
the central cloud platform is used for sending the state monitoring model and the big data computing model obtained through training to the edge cloud platform and configuring edge computing services of the edge cloud platform.
Based on any one of the above aspects, in the embodiment provided by the application, the edge computing interface device monitors the operation state of the hydropower station field device according to the production process data to obtain the operation state statistical information, and calculates the production process data according to the big data computing model issued by the edge cloud platform to control the hydropower station field device. On the other hand, the edge cloud platform can perform state monitoring on the running state statistical information according to a state monitoring model issued by the central cloud platform so as to perform edge control on the hydropower station field device through the edge computing interface device. Therefore, the real-time performance and the agility of the edge computing interface equipment close to the data acquisition side are fully considered, network congestion can be improved to a certain extent, the safe production capacity is improved, the data in the production process are analyzed and mined by utilizing edge cloud computing, and the personnel efficiency is improved.
Drawings
To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic diagram illustrating an application scenario of a hydropower station production control processing system provided by an embodiment of the application;
fig. 2 is a schematic flow diagram illustrating a method for controlling and processing hydropower station production provided by an embodiment of the application;
fig. 3 illustrates a component schematic diagram of an electronic device for implementing the hydropower station field devices, the edge computing interface devices, the edge cloud platform, and the center cloud platform illustrated in fig. 1 according to an embodiment of the present application.
Detailed Description
In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some of the embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. In addition, one skilled in the art, under the guidance of the present disclosure, may add one or more other operations to, or remove one or more operations from, the flowchart.
Fig. 1 is a schematic diagram illustrating an application scenario of a hydropower station production control processing system 10 according to an embodiment of the application. In this embodiment, the hydroelectric production control processing system 10 may include a hydroelectric field device 100, an edge computing interface device 200, an edge cloud platform 300, and a central cloud platform 400.
Compared with the conventional hydropower station, the embodiment of the application omits a production bottom layer control system, the edge computing interface device 200 is adopted to complete production process monitoring on the hydropower station field device 100 (such as but not limited to a field sensor, a motor, a valve actuator, etc.), and the edge computing interface device 200 can perform operations such as storage, processing, control, model reasoning, algorithm calculation, etc. on production process data.
The edge computing interface device 200 may communicate with the edge cloud platform 300 via a 5G network device, and may monitor the hydropower station field devices 100 even when the 5G network fails.
The edge cloud platform 300 may perform bidirectional wireless communication with the edge computing interface device 200 through a 5G network, complete specific service applications including real-time production monitoring, protection interlocking and automatic control, and complete management of an edge cloud.
The central cloud platform 400 may complete centralized training of various algorithm models through data transmission and model transmission with the edge cloud platform 300, issue the algorithm models and related data, and deploy and manage edge computing services for the edge cloud platform 300.
It will be appreciated that in other possible embodiments, the hydroelectric power plant production control treatment system 10 may comprise only a portion of the components shown in fig. 1, or may comprise additional components.
Fig. 2 illustrates an interactive flow diagram of a method of generating control processing for a hydroelectric power plant according to an embodiment of the present disclosure, which may be performed by the system 10 of fig. 1. It should be understood that in other embodiments, the sequences of some steps in the hydropower plant production control processing method of the embodiment may be interchanged according to actual needs, or some steps may be omitted or deleted. The detailed steps of the hydropower station production control processing method are described below.
At step S110, the hydropower site device 100 transmits the production process data of the hydropower site to the edge computing interface device 200.
In this embodiment, the production process data of the hydropower station site may be data detected by various detection devices, such as site sensors, motors, valve actuators, and the like, such as sensor data, motor operation data, valve actuator data, and the like.
Step S120, the edge computing interface device 200 monitors the operation state of the field device 100 of the hydropower station according to the production process data to obtain operation state statistical information, sends the operation state statistical information to the edge cloud platform 300, computes the production process data according to a big data computing model issued by the edge cloud platform 300, and sends a control instruction to the field device 100 of the hydropower station according to a computation result.
Step S130, the edge cloud platform 300 performs state monitoring on the running state statistical information according to the state monitoring model issued by the central cloud platform 400, and sends an edge control instruction to the edge computing interface device 200 according to the state detection result, so that the edge computing interface device 200 sends the edge control instruction to the power station field device 100.
Step S140, the central cloud platform 400 sends the state monitoring model and the big data computing model obtained by training to the edge cloud platform 300, and configures the edge computing service of the edge cloud platform 300.
Based on the above design, the edge computing interface device 200 monitors the operation state of the hydropower station field device 100 according to the production process data to obtain the operation state statistical information, and computes the production process data according to the big data computing model issued by the edge cloud platform 300 to control the hydropower station field device 100. On the other hand, the edge cloud platform 300 may perform state monitoring on the running state statistical information according to a state monitoring model issued by the central cloud platform 400, so as to perform edge control on the hydropower station field device 100 through the edge computing interface device 200. Therefore, the real-time performance and the agility of the edge computing interface device 200 close to the data acquisition side are fully considered, network congestion can be improved to a certain extent, the safety production capacity is improved, the edge cloud computing is utilized to analyze and mine production process data, and the personnel efficiency is improved.
In one possible embodiment, considering that there are different production monitoring periods in a particular hydropower plant production process, and in order to perform the decentralized arrangement for different state dimensions, for step S120, it may be embodied by the following exemplary sub-steps S121 and S122, which are described in detail below.
Substep S121, analyzing the production process data during each production monitoring period, respectively, determines status information for the hydropower station field device 100 in each status dimension.
In substep S122, status information of the field device 100 of the hydropower station in each status dimension is summarized to obtain statistical information of the operating status of the field device 100 of the hydropower station.
For example, the production monitoring period can be flexibly set according to an actual production plan, and is not limited in detail herein. As another example, a state dimension may be, but is not limited to, an internal control state dimension, an external control state dimension, and the like.
In one possible embodiment, with respect to step S120, to further adjust the operating state of the hydropower field device 100 so that the hydropower field device 100 is continuously within the normal operating state range, the following exemplary substep S123 and substep S124 can be embodied, as described in detail below.
And a substep S123 of calculating the operation data of each production process in the production process data according to the big data calculation model to obtain operation difference data between the operation data of each production process and the operation data of the production process under the corresponding big data calculation model and difference items corresponding to the operation difference data.
In substep S124, a control command matching the operational difference data is obtained from the control command database corresponding to the difference item, and the control command is sent to the plant field device 100.
In this embodiment, the operation difference data may be data information in which a parameter difference between the operation data of each production process and the operation data of the production process under the corresponding big data calculation model is greater than a set parameter difference, and the difference item may be a statistical item to which a data parameter type of the operation difference data belongs.
In this embodiment, the control instruction database may be pre-configured with control instructions matching different operation difference data under each statistical item, so that the control instructions matching the operation difference data may be obtained from the control instruction database corresponding to the determined difference item, and then the hydropower station field device 100 may be controlled, so that the operating state of the hydropower station field device 100 may be adjusted, so that the hydropower station field device 100 may be continuously in the normal operating state range.
For example, taking the detection data of the valve actuator as an example, assuming that the difference item corresponding to the operation difference data is a period data item of an execution control switch of the valve actuator, the operation difference data may be period difference data, and then the control instruction matching the period difference data may be obtained from a control instruction database corresponding to the period data item of the execution control switch.
In one possible embodiment, with respect to step S130, to further adjust the operating state of the hydropower field device 100 so that the hydropower field device 100 is continuously within the normal operating state range, the following exemplary sub-step S131-sub-step S134 may be embodied, as described in detail below.
In the substep S131, the state change information of each operation state is acquired from the operation state statistical information.
And a substep S132 of calculating the fitness of the target state change information corresponding to each to-be-controlled category in the to-be-controlled category set corresponding to the hydropower station field device 100 and the state change information of each operating state according to the state monitoring model.
In this embodiment, the state change information may include state change time corresponding to the state change and a state change rate corresponding to the state change time, the set of categories to be controlled may include a plurality of categories to be controlled, fitness of the target state change information corresponding to the category to be controlled and the state change information of each operating state is obtained by obtaining historical state change rates corresponding to a plurality of target time intervals of the target state change information, and then calculating according to a difference between each historical state change rate and a current state change rate corresponding to the state change information of each operating state, and a duration corresponding to each target time interval is matched with the state change time.
And a substep S133, obtaining an edge control command corresponding to the state change information of each operating state according to the fitness of each to-be-controlled category and the state change information of each operating state.
In sub-step S134, an edge control command corresponding to the state change information of each operation state is sent to the edge computing interface device 200.
In one possible embodiment, the fitness of the target state change information to the state change information of each operating state may be calculated in such a way that the sub-step S132 may be implemented by the following further sub-steps, which are described in detail below.
And a substep S1321 of obtaining a plurality of target time intervals of the target state change information according to the state change time matching.
In sub-step S1322, a history state change rate corresponding to each target time interval is obtained.
And a substep S1323 of screening a selected historical state change rate matched with the current state change rate from the historical state change rates according to the difference between the historical state change rate and the current state change rate.
And a substep S1324 of calculating the fitness of the target state change information and the state change information of each operating state according to the number of the selected historical state change rates and the number of the historical state change rates.
In another possible embodiment, the current state change rate corresponding to the state change information of each operating state may further include a set state change transaction pull-up degree and a set state change transaction pull-down degree, and the fitness of the target state change information and the state change information of each operating state may be calculated in such a way that the sub-step S132 may be implemented by the following further sub-steps, which are described in detail below.
In substep S1325, historical state change rates corresponding to a plurality of target time intervals of the target state change information are obtained.
And a substep S1326, calculating the fitness of the first dimension of the target state change information and the state change information of each running state according to the difference between the target historical state change abnormal pull-up degree and the set state change abnormal pull-up degree in the historical state change rate.
It should be noted that the differential pull-up degree may refer to a magnitude of the state change rate in the increasing process, and the differential decrease degree may refer to a magnitude of the state change rate in the decreasing process.
For example, the matching abnormal rise degree of the first dimension larger than the set state change abnormal rise degree can be screened from the target historical state change abnormal rise degrees, and then the target abnormal rise degree is obtained by calculation according to the number of the matching abnormal rise degrees of the first dimension and the number of the historical state change rates. For example, an added value of the number of matching anomaly pull-ups and the number of historical state change rates of the first dimension, or an equal-proportion value of the added value, may be used as the target anomaly pull-up.
On the basis, the matching influence factor of the first dimension corresponding to the state change abnormal rise degree can be obtained, and therefore the fitness of the first dimension of the state change information of the target and the state change information of each running state can be obtained through calculation according to the matching influence factor of the first dimension and the target abnormal rise degree. For example, the fitness of the first dimension of the target state change information and the state change information of each operating state may be obtained by taking the product of the matching influence factor of the first dimension and the target abnormal rise degree, or an equal-proportion numerical value of the product.
And a substep S1327 of screening out a second dimension matching abnormal movement reduction degree smaller than the set state change abnormal movement reduction degree from the historical state change abnormal movement reduction degrees, wherein the first dimension and the second dimension are opposite dimensions.
And a substep S1328, calculating to obtain a target abnormal change attenuation degree according to the number of the matched abnormal change attenuation degrees of the second dimension and the number of the historical state change rates.
And a substep S1329 of acquiring a second-dimension matching influence factor corresponding to the state change transaction attenuation degree.
And a substep S13291 of calculating a fitness of the second dimension of the target state change information and the state change information of each operation state based on the matching influence factor of the second dimension and the target abnormal change attenuation degree.
For example, the second dimension fitness of the target state change information and the state change information of each operating state may be obtained by taking the product of the matching influence factor of the second dimension and the target change reduction degree, or an equal-proportion numerical value of the product.
And a substep S13292, calculating the fitness of the target state change information and the state change information of each running state according to the fitness of the first dimension and the fitness of the second dimension.
For example, the fitness of the first dimension may be multiplied by the weight of the first dimension to obtain a first product, the fitness of the second dimension may be multiplied by the weight of the second dimension to obtain a second product, and then the sum of the first product and the second product, or an equal proportion value of the sum, may be used as the fitness of the target state change information and the state change information of each operating state.
Referring to fig. 3, a schematic diagram of components of an electronic device 500 provided in an embodiment of the present application for implementing the hydroelectric power plant field device 100, the edge computing interface device 200, the edge cloud platform 300, and the center cloud platform 400 shown in fig. 1 is shown, where the electronic device 500 may include a machine-readable storage medium 510 and a processor 520.
In this embodiment, the machine-readable storage medium 510 and the processor 520 are both located in the electronic device 500 and are separately located. However, it should be understood that the machine-readable storage medium 510 may also be separate from the electronic device 500 and accessible by the processor 520 through a bus interface. Alternatively, the machine-readable storage medium 510 may be integrated into the processor 520, such as a cache and/or general purpose registers.
Since the electronic device 500 provided in the embodiment of the present application is another implementation form of the method embodiment executed by the electronic device 500, and the electronic device 500 may be used to execute the method for controlling and processing hydropower station production provided in the method embodiment, reference may be made to the method embodiment for obtaining technical effects, and details are not repeated here.
The embodiments described above are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the application, but is merely representative of selected embodiments of the application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims. Moreover, all other embodiments that can be made available by a person skilled in the art without making any inventive step based on the embodiments of the present application shall fall within the scope of protection of the present application.
Claims (8)
1. A hydropower station production control processing method is applied to a hydropower station production control processing system, the hydropower station production control processing system comprises a hydropower station field device, an edge computing interface device, an edge cloud platform and a center cloud platform, and the method comprises the following steps:
the hydropower station field device sends the production process data of the hydropower station field to the edge computing interface device;
the edge computing interface device monitors the running state of the hydropower station field device according to the production process data to obtain running state statistical information, sends the running state statistical information to the edge cloud platform, computes the production process data according to a big data computing model issued by the edge cloud platform, and sends a control instruction to the hydropower station field device according to a computing result;
the edge cloud platform carries out state monitoring on the running state statistical information according to a state monitoring model issued by the central cloud platform, and sends an edge control instruction to the edge computing interface device according to a state detection result, so that the edge computing interface device sends the edge control instruction to the hydropower station field device;
the central cloud platform sends a state monitoring model and a big data computing model obtained through training to the edge cloud platform, and edge computing services of the edge cloud platform are configured;
the method for monitoring the running state statistical information by the edge cloud platform according to the state monitoring model issued by the central cloud platform and sending an edge control instruction to the edge computing interface device according to the state detection result comprises the following steps:
acquiring state change information of each operation state from the operation state statistical information;
calculating the fitness of target state change information corresponding to each to-be-controlled category in a to-be-controlled category set corresponding to hydropower station field equipment and the state change information of each operating state according to the state monitoring model, wherein the state change information comprises state change time corresponding to state change and a state change rate corresponding to the state change time, the to-be-controlled category set comprises a plurality of to-be-controlled categories, the fitness of the target state change information corresponding to the to-be-controlled category and the state change information of each operating state is calculated according to the historical state change rate corresponding to a plurality of target time intervals of the target state change information, and then the fitness of the target state change information corresponding to the to-be-controlled category and the current state change rate corresponding to the state change information of each operating state is obtained according to the difference between each historical state change rate and the current state change rate corresponding to the state change information of each operating state, and the duration corresponding to each target time interval is matched with the state change time;
obtaining an edge control instruction corresponding to the state change information of each operating state according to the fitness of each category to be controlled and the state change information of each operating state;
and sending an edge control instruction corresponding to the state change information of each running state to the edge computing interface equipment.
2. The method of claim 1, wherein the step of monitoring the operational status of the hydroelectric field devices by the edge computing interface device based on the production process data to obtain operational status statistics comprises:
analyzing the production process data in each production monitoring period respectively, and determining the state information of the hydropower station field equipment in each state dimension;
and summarizing the state information of the hydropower station field equipment in each state dimension to obtain the running state statistical information of the hydropower station field equipment.
3. The method for controlling and processing the hydropower station production according to claim 1, wherein the step of calculating the production process data according to a big data calculation model issued by the edge cloud platform and sending a control instruction to the hydropower station field device according to a calculation result comprises:
calculating the operation data of each production process in the production process data according to the big data calculation model to obtain operation difference data between the operation data of each production process and the operation data of the production process under the corresponding big data calculation model and difference items corresponding to the operation difference data;
and acquiring a control instruction matched with the operation difference data from a control instruction database corresponding to the difference project, and sending the control instruction to the hydropower station field equipment.
4. The method of claim 1, wherein the fitness of the target state change information to the state change information for each operating state is calculated by:
obtaining a plurality of target time intervals of the target state change information according to the state change time matching;
acquiring historical state change rates corresponding to the target time intervals;
screening a selected historical state change rate matched with the current state change rate from the historical state change rates according to the difference between the historical state change rate and the current state change rate;
and calculating the fitness of the target state change information and the state change information of each running state according to the quantity of the selected historical state change rates and the quantity of the historical state change rates.
5. The method according to claim 1, wherein the current state change rate corresponding to the state change information of each operating state includes a set state change transaction pull-up degree and a set state change transaction pull-down degree, and the calculating step of the fitness of the target state change information and the state change information of each operating state includes:
acquiring historical state change rates corresponding to a plurality of target time intervals of the target state change information;
calculating to obtain the fitness of the target state change information and the first dimension of the state change information of each running state according to the difference between each target historical state change abnormal pull-up degree in the historical state change rate and the set state change abnormal pull-up degree;
screening out a second dimension matching abnormal change weakening degree which is smaller than the set state change abnormal change weakening degree from the historical state change abnormal change weakening degree, wherein the first dimension and the second dimension are opposite dimensions;
calculating to obtain a target abnormal movement weakening degree according to the number of the matched abnormal movement weakening degrees of the second dimension and the number of the historical state change rates;
acquiring a second-dimension matching influence factor corresponding to the state change transaction attenuation degree;
calculating the fitness of the second dimension of the target state change information and the state change information of each running state according to the matching influence factor of the second dimension and the target abnormal change attenuation degree;
and calculating the fitness of the target state change information and the state change information of each running state according to the fitness of the first dimension and the fitness of the second dimension.
6. The method according to claim 5, wherein the step of calculating the fitness of the target state change information to the first dimension of the state change information of each operating state based on the difference between the target historical state change variability pull-up and the set state change variability pull-up in the historical state change rate comprises:
screening out a matching abnormal movement pull-up degree of a first dimension which is greater than the set state change abnormal movement pull-up degree from the target historical state change abnormal movement pull-up degree;
calculating to obtain a target abnormal movement pull-up degree according to the number of the matched abnormal movement pull-up degrees of the first dimension and the number of the historical state change rates;
acquiring a first-dimension matching influence factor corresponding to the state change abnormal pull-up degree;
and calculating the fitness of the first dimension of the target state change information and the state change information of each running state according to the matching influence factor of the first dimension and the target abnormal change pull-up degree.
7. The hydropower station production control processing system is characterized by comprising hydropower station field equipment, edge computing interface equipment, an edge cloud platform and a center cloud platform;
the hydropower station field device is used for sending production process data of a hydropower station field to the edge computing interface device;
the edge computing interface device is used for monitoring the running state of the hydropower station field device according to the production process data to obtain running state statistical information, sending the running state statistical information to the edge cloud platform, computing the production process data according to a big data computing model issued by the edge cloud platform, and sending a control instruction to the hydropower station field device according to a computing result;
the edge cloud platform is used for carrying out state monitoring on the running state statistical information according to a state monitoring model issued by the central cloud platform and sending an edge control instruction to the edge computing interface device according to a state detection result, so that the edge computing interface device sends the edge control instruction to the hydropower station field device;
the central cloud platform is used for sending a state monitoring model and a big data computing model obtained by training to the edge cloud platform and configuring edge computing service of the edge cloud platform
Wherein the edge cloud platform is specifically configured to:
acquiring state change information of each operation state from the operation state statistical information;
calculating the fitness of target state change information corresponding to each to-be-controlled category in a to-be-controlled category set corresponding to hydropower station field equipment and the state change information of each operating state according to the state monitoring model, wherein the state change information comprises state change time corresponding to state change and a state change rate corresponding to the state change time, the to-be-controlled category set comprises a plurality of to-be-controlled categories, the fitness of the target state change information corresponding to the to-be-controlled category and the state change information of each operating state is calculated according to the historical state change rate corresponding to a plurality of target time intervals of the target state change information, and then the fitness of the target state change information corresponding to the to-be-controlled category and the current state change rate corresponding to the state change information of each operating state is obtained according to the difference between each historical state change rate and the current state change rate corresponding to the state change information of each operating state, and the duration corresponding to each target time interval is matched with the state change time;
obtaining an edge control instruction corresponding to the state change information of each operating state according to the fitness of each category to be controlled and the state change information of each operating state;
and sending an edge control instruction corresponding to the state change information of each running state to the edge computing interface equipment.
8. The hydropower station production control processing system of claim 7, wherein the edge cloud platform is specifically configured to:
obtaining a plurality of target time intervals of the target state change information according to the state change time matching;
acquiring historical state change rates corresponding to the target time intervals;
according to the difference between the historical state change rate and the current state change rate, screening a selected historical state change rate matched with the current state change rate from the historical state change rate;
and calculating the fitness of the target state change information and the state change information of each running state according to the quantity of the selected historical state change rates and the quantity of the historical state change rates.
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