CN110765726A - Intelligent generation system of energy network planning map - Google Patents
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Abstract
An intelligent generation system and method of an energy grid planning map comprises the following steps: the system comprises a multi-element text analysis module, an energy channel analysis module, a planning structure intelligent construction module, a planning result quantitative evaluation module, an equipment resource library matching module, a business rule association module, an equipment resource library and a business rule library, wherein the multi-element text analysis module, the energy channel analysis module, the planning structure intelligent construction module, the planning result quantitative evaluation module, the equipment resource library matching module, the business rule association module, the equipment resource library and the business rule library are positioned on a data layer.
Description
Technical Field
The invention relates to a technology in the field of semantic information processing, in particular to an intelligent generation system of an energy network planning map based on text information as a composition.
Background
The energy network planning design is an important foundation for energy network construction and electric power engineering field construction, wherein an energy network planning diagram is an indispensable part in electric power system analysis and engineering calculation, and integrates a network structure, a tidal current result and a graph of a power grid system together, so that equipment connection, equipment parameter setting and simulation operation results of the system can be directly checked.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an intelligent generation system and method of an energy network planning map, which are used for analyzing, mining, analyzing and modeling information in a plurality of texts, automatically planning and evaluating an electric power system by using an intelligent algorithm, and finally obtaining the planning map and a simulation operation data packet of the electric power system.
The invention is realized by the following technical scheme:
the invention relates to an intelligent generation system of an energy network planning map, which comprises: the system comprises a multi-source text analysis module, an energy channel analysis module, a planning structure intelligent construction module, a planning result quantitative evaluation module, an equipment resource matching module, a business rule association module, an equipment resource library and a business rule library, wherein the multi-source text analysis module, the energy channel analysis module, the planning structure intelligent construction module, the planning result quantitative evaluation module, the equipment resource matching module, the business rule association module are positioned on a service layer, and: the multi-source text analysis module performs mechanical word segmentation and structural analysis on the text to obtain pre-selection equipment information, equipment connection and parameter setting constraint conditions, an energy efficiency target formula, an evaluation standard formula and reference data; the equipment resource library matching module is connected with the equipment resource library to perform equipment key completion information, automatically add items to the new equipment and match the approximate equipment according to the requirement parameters; the business rule association module is connected with the business rule base to perform equipment constraint binding, energy efficiency constraint binding and formula-variable-value tree persistence, and outputs the result in a quantitative evaluation stage to provide a basis for evaluation of a planning result; the energy channel analysis module is respectively connected with the equipment resource management module and the business rule management module, performs load disassembly and analysis of each channel of electricity, heat, gas and cold according to the plant station equipment list to obtain single-channel equipment connection and energy flow information, outputs the single-channel equipment connection and energy flow information to the planning structure intelligent construction module, and the planning structure intelligent construction module obtains the equipment connection and energy flow information according to the input plant station equipment list and planning target of each channel and according to the equipment connection information and the energy flow information, under the limiting condition of planning constraint, automatic planning operation is carried out through a DMST (dynamic edge-weight ratio minimum spanning tree) algorithm and a multi-target genetic algorithm to obtain a preliminary planning design drawing and power system data of simulated operation, and a planning result quantitative evaluation module obtains the adaptation standard of the current energy network from a service rule base, evaluates the whole planning result in terms of design and obtains an evaluation report.
The multi-source text information is as follows: equipment information, equipment connection constraint conditions, an energy efficiency target formula, benchmark data, an evaluation formula and the benchmark data which are expected to be constructed in the power system; the multi-source text information takes the form of, but is not limited to, a structured txt text file.
The device resource library comprises the existing detailed information of the device, including device parameters, energy efficiency upper limit, device connection limit and device port limit, and can avoid redundant input operation in the device information input stage.
Preferably, in the running process of the model, when no target equipment information is checked, the equipment resource library stores the current equipment information into the database in the form of newly added items.
The completion information includes: and checking and completing the equipment parameters according to the model, storing the model information into a resource library when data is not inquired according to the equipment model, and inquiring the equipment model with the most approximate parameters within a set data threshold range when the data is input without the model and only the parameters.
The load disassembling and analyzing comprises the following steps: splitting electricity, heat, gas and cold from the total load, respectively analyzing the equipment connection and energy flow conditions of a single channel, and recording in a one-to-one correspondence form of energy outflow equipment-energy inflow equipment-energy data; the record is taken but not limited to being saved in a temporary txt file under a specified directory in the computer.
The device constraint binding means: obtaining the constraint requirement of the power system, such as the number P of the inlet and outlet ports of the plant station equipment, by analyzing the input planning constraint filei≤PimaxType and direction, upper limit of energy flow of energy transmission pipeline
Energy flow, excess capacity limit Ri≤RimaxAnd the like. Wherein P isiIndicating the number of ports, P, currently used by the deviceimaxRepresents the current device port ceiling, SijRepresenting the energy flow conduit load, S, connecting the ith to the jth deviceijmaxRepresenting the upper load limit, R, of the power flow conduit connecting the ith device to the jth deviceiRepresents the excess capacity, R, of the ith capacity equipmentimaxRepresents the upper limit of the excess capacity of the ith capacity equipment.
The energy efficiency constraint binding means that: and limiting the energy efficiency of the capacity, the energy consumption, the energy storage and the like according to the energy efficiency formulas input in the model and in a user-defined mode.
The formula-variable-value tree persistence refers to: resolving formulas, variables and values, and performing associated storage, such as: one formula corresponds to a plurality of variables, and one variable can correspond to a plurality of values under different conditions. Therefore, the three are stored in a tree structure in an associated manner.
The business rule association module preferably further performs formula level query: when the formula, the variable and the value are taken out of the database, the formula, the variable and the value are inquired layer by layer in a recursive mode, a data structure is established, and according to preset conditions of an energy network, for example: and the geographic position, the weather, the historical energy efficiency data and the like are used as retrieval bases, different planning design evaluation algorithms are called according to different conditions, and a calculation basis is provided in a quantitative evaluation stage.
The service rule base is as follows: storing the business rules in a form of a data table according to a formula, a variable and a value, wherein the form of the data table specifically comprises the following steps: formula-variable and variable-condition-value correspond to the memory table, respectively.
The planning objective describes the overall planning objective of the power system, and specifically comprises a support multi-objective function consisting of a single or multiple indexes:
wherein: cTargetRepresenting the calculated combined target value, omega, of multiple targetsiRepresenting the weight occupied by each targetTiRepresenting individual objective functions, e.g. target T of expected capacity1Energy target T2Energy storage target T3Budget target T4And n represents the number of planning targets.
The DMST combined multi-target genetic algorithm specifically comprises the following steps:
a. carrying out hierarchical classification on plant station equipment;
b. mapping the solution space to a coding space;
c. generating an initial population by using a dynamic edge weight ratio minimum spanning tree algorithm;
d. matching an energy flow distribution algorithm and a capacity adjustment algorithm;
e. fitness value calculation, fitness function f (x)i)＝1/CTarget；
f. Calculating cross variation;
g. selecting excellent filial generation individuals to enter the next iteration, and simultaneously recording the optimal filial generation individuals and storing;
i. and iterating to the end to obtain a final result.
The evaluation report comprises: overall economic index, comprehensive energy utilization efficiency, fire efficiency, pollutant emission index, environmental social index and safety and reliability.
The assessment report is output to a specified computer directory in a pdf form, but not limited to the pdf form.
The power system data is stored in a form of SVG (scalable vector graphics) and txt and is output to a specified computer directory.
The system is further provided with a planning result output module which outputs the planned topological structure, energy transmission pipeline marking and equipment graphic model representation of the power system in the form of an SVG topological wiring diagram according to the composition and simulation calculation data of the evaluation module; and outputting the plant station equipment construction data, the energy flow simulation calculation data and the equipment operation setting parameters in the form of data packets.
The invention relates to an intelligent generation method of an energy network planning graph of the system, which obtains support data by performing text mechanical word segmentation, structural analysis and formula analysis on multi-source text data, corrects and completes equipment resource libraries and service rule libraries through database association after plant station equipment parameters, equipment constraints, planning constraints, energy efficiency targets and evaluation standards, automatically generates the planning graph and a simulation operation data packet by combining energy channel analysis and service rule constraints and utilizing a dynamic edge weight ratio minimum spanning tree algorithm and a multi-target genetic algorithm, and finally outputs a planning result after quantitative algorithm evaluation.
Technical effects
Compared with the prior art, the invention adopts a continuously complemented energy network equipment resource library and a business rule library containing business rules under multiple scenes. The workload of drawing the planning diagram is reduced, the accuracy of the final result is ensured, the automation degree of energy network planning can be improved, and powerful support is provided for the implementation of subsequent power engineering.
Drawings
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a block diagram of the practice of the present invention.
Detailed Description
As shown in fig. 1, the present embodiment relates to an intelligent generation system of an energy grid planning map, which includes: the system comprises a multi-source text analysis module, an equipment resource library, an equipment resource matching module, a business rule library, a business rule association module, an energy channel analysis module, a planning structure intelligent construction module and a planning result quantitative evaluation module, wherein:
the multi-source text analysis module generates a capacity equipment list, an energy utilization equipment list and an energy storage equipment list and outputs the capacity equipment list, the energy utilization equipment list and the energy storage equipment list to the equipment resource library correlation module, and generates an equipment constraint file, an energy efficiency target file and an evaluation standard file and outputs the equipment constraint file, the energy efficiency target file and the evaluation standard file to the business rule correlation module.
The equipment resource library correlation module is internally provided with equipment parameter query completion, no query result self-adding item and parameter approximate matching query, when the equipment parameter query is carried out, equipment information corresponding to no equipment name is displayed, and the equipment resource library correlation module stores the currently transmitted equipment information as a new item into a database; and when no specific equipment name exists, the equipment resource library correlation module searches equipment approximately meeting the requirement according to the requirement parameters.
The device resource library comprises: the system comprises a capacity equipment information table, an energy utilization equipment information table, an energy storage equipment information table and an energy transmission equipment information table.
The business rule association module provides data support for the system in a mode of carrying out hierarchical query at the bottom layer, and the result obtained by the hierarchical query comprises the following steps: energy efficiency constraints, equipment constraints, and evaluation algorithms.
The business rule base comprises: the device connection limit table, formula-variable table, variable-condition-value table.
As shown in fig. 2, the present embodiment includes a presentation layer, a service layer, and a data layer, where: the presentation layer is positioned at the uppermost layer of the planning system and directly interacts with the service layer, transmits an externally input text file to the service layer, and receives a power system planning diagram returned by the service layer and a simulation data packet consisting of predicted operation data and an evaluation report; the service layer is used as the core content of the whole framework, receives the original data transmitted by the presentation layer, and transmits a power system planning result graph and a simulation operation data packet back to the original data; meanwhile, the system also interacts with a data layer at the bottom layer; the data layer is used for storing detailed physical parameters of equipment and pipelines used by the current planning design in an equipment resource library; and the service rule base stores energy efficiency constraint rules, energy efficiency formulas and basic parameters which are constructed for the planning results, and an evaluation algorithm and reference data thereof which are used for carrying out quantitative evaluation on the planning results.
The system comprises a multi-source text analysis module, a business rule association module, a resource library matching module, an energy channel analysis module, a planning structure intelligent construction module and a planning result quantitative evaluation module which are positioned in a service layer, wherein:
a. the multi-source text analysis module decomposes symbols, numerical values, formulas and the like in the text by using a text mechanical word segmentation mode, organizes and marks the symbols, the numerical values, the formulas and the like in a structured mode, and analyzes the connection, the equipment port and rated parameters of equipment; and analyzing the read formula and correlating according to formula-variable-value.
b. The database association module comprises an equipment resource library association unit and a business rule library association unit, wherein: the device resource library association unit receives the related data of the physical device, interacts with the device resource library, and plays the roles of automatically supplementing key parameters of the physical device, automatically building the library and selecting approximate devices according to the parameters; and the business rule base association unit is used for storing the reference data, the evaluation indexes, the calculation formulas and the like in a tree structure, inquiring in a hierarchical mode and matching the evaluation standards of the planning results according to specific conditions.
c. And the energy load analysis module is used for carrying out disassembly analysis on loads of the electric channel, the hot channel, the gas channel and the cold channel according to the plant equipment and the energy efficiency target data obtained by data preprocessing, and providing data support for subsequent planning.
d. And the planning structure intelligent construction module calculates the topological structure, the energy transmission pipeline connection, the simulation operation and the equipment setting parameters of the equipment by utilizing a multi-target genetic algorithm combined with a DMST (dynamic edge-weight ratio minimum spanning tree) algorithm according to the plant station equipment parameters, the energy channel load data, the planning constraint and the equipment constraint, and obtains a result.
e. A planning result quantitative evaluation module: and carrying out quantitative evaluation on the planning result according to the evaluation algorithm and the reference data in the service rule base.
Compared with the prior art, the embodiment respectively takes the reduction of the workload of planning and designing the power system and the accuracy and the effectiveness of the final planning result as the foothold, and designs the power system by taking the data input part, the drawing part and the evaluation part as the cut-in points.
The comparison of the technical indexes of the above work with the effects of the examples of similar products at home and abroad is shown in Table 1
TABLE 1 comparison of technical characteristics
Compared with the prior art, the embodiment simultaneously calculates and outputs the complete simulation operation data packet when the energy network planning diagram is automatically generated, and has reference value for power energy construction compared with the current similar products at home and abroad which only generate the planning diagram and part of calculated values; from the aspect of automation degree, the embodiment can support the whole processes of multi-source data import, data preprocessing and analysis, planning diagram construction and planning result evaluation by introducing the equipment database and the business rule base, has higher automation degree of generating the power planning diagram under the condition of ensuring accuracy, and has higher automation degree compared with the current similar products at home and abroad only realizing automatic construction of the planning diagram; from the result accuracy, because the data preprocessing module is added to perform analysis, matching and calculation to support default of device parameters, approximate matching of parameters, and self-increment of device parameter entries, the device resource information base is maintained to support data preprocessing, and a business rule base is also maintained to support storage and binding of information such as a custom calculation formula, constraint conditions, an energy efficiency target, reference data, and the like. Therefore, compared with similar results, the method has higher accuracy; as for the planning result, the planning result of the embodiment not only includes the planning map, but also simultaneously generates an energy efficiency report based on the planning map and an evaluation report of the planning result, and has more reference information for subsequent work.
The equipment resource library and the business rule library used for energy network planning in the components, and the equipment resource library matching module and the business rule association module corresponding to the equipment resource library and the business rule library are original in the invention, are never disclosed, and have different working modes from any existing literature records: the automation degree of the composition of the planning drawing is improved, repeated input of data of common equipment is avoided, and a formula calculation mode is more flexibly defined.
The technical details of matching the device resource library with the device resources are specifically as follows: and when only equipment parameters but not the equipment types and models are input, parameter fuzzy query is carried out according to a set parameter floating threshold value, and the most approximate equipment information is obtained.
The technical details of the association between the business rule base and the business rules specifically refer to: resolving formulas, variables and values, and performing associated storage, such as: one formula corresponds to a plurality of variables, one variable can correspond to a plurality of values under different conditions, the corresponding relation is stored in the business rule base by two tables respectively, when a certain formula is needed, the certain formula is read and constructed into a tree structure, the root node stores the formula, the child nodes store variable symbols, and the child nodes of the variables store specific values which can be obtained under the corresponding conditions; the restrictions on the device connection are stored in a device connection restriction table in the form of a device-port number-connectable device type, wherein the connectable device type is so as to be specified by the "word segmentation".
Compared with the prior art, the method has the advantages that the automation degree is higher in the whole process of text file analysis, calculation formula analysis, equipment parameter matching, planning rule matching, automatic composition, simulation calculation and planning result evaluation under the condition of ensuring the accuracy.
The foregoing embodiments may be modified in many different ways by those skilled in the art without departing from the spirit and scope of the invention, which is defined by the appended claims and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (8)
1. An intelligent generation system of an energy grid planning map, comprising: the system comprises a multi-source text analysis module, an energy channel analysis module, a planning structure intelligent construction module, a planning result quantitative evaluation module, an equipment resource matching module, a business rule association module, an equipment resource library and a business rule library, wherein the multi-source text analysis module, the energy channel analysis module, the planning structure intelligent construction module, the planning result quantitative evaluation module, the equipment resource matching module, the business rule association module are positioned on a service layer, and: the multi-source text analysis module performs mechanical word segmentation and structural analysis on the text to obtain pre-selection equipment information, equipment connection and parameter setting constraint conditions, an energy efficiency target formula, an evaluation standard formula and reference data; the equipment resource library matching module is connected with the equipment resource library to perform equipment key completion information, automatically add items to the new equipment and match the approximate equipment according to the requirement parameters; the business rule association module is connected with the business rule base to perform equipment constraint binding, energy efficiency constraint binding and formula-variable-value tree persistence, and provides a basis for planning result evaluation in a quantitative evaluation stage; the energy channel analysis module is respectively connected with the equipment resource management module and the service rule management module, performs load disassembly and analysis on each channel of electricity, heat, gas and cold according to a plant station equipment list to obtain single-channel equipment connection and energy flow information, and outputs the single-channel equipment connection and energy flow information to the planning structure intelligent construction module;
the multi-source text information is as follows: equipment information, equipment connection constraint conditions, an energy efficiency target formula, benchmark data, an evaluation formula and the benchmark data which are expected to be constructed in the power system;
the device resource library comprises the detailed information of the current existing device, including device parameters, energy efficiency upper limit, device connection limit and device port limit;
the equipment resource library is preferably used in the model operation process, and when no target equipment information is checked, the current equipment information is stored in the database in a form of newly added items;
the completion information includes: the equipment parameters are checked and supplemented according to the model, when data are not inquired according to the equipment model, the model information is stored in a resource library, when no model has only parameters, the equipment model with the most approximate parameters is inquired within a set data threshold range;
the DMST algorithm is combined with a multi-target genetic algorithm, and specifically comprises the following steps:
a. carrying out hierarchical classification on plant station equipment;
b. mapping the solution space to a coding space;
c. generating an initial population by using a dynamic edge weight ratio minimum spanning tree algorithm;
d. matching an energy flow distribution algorithm and a capacity adjustment algorithm;
e. fitness value calculation, fitness function f (x)i)＝1/CTarget；
f. Calculating cross variation;
g. selecting excellent filial generation individuals to enter the next iteration, and simultaneously recording the optimal filial generation individuals and storing;
i. iterating until the iteration is finished to obtain a final result;
the evaluation report comprises: overall economic index, comprehensive energy utilization efficiency, fire efficiency, pollutant emission index, environmental social index and safety and reliability.
2. The system of claim 1, wherein said load disassembly and analysis is: splitting electricity, heat, gas and cold from the total load, respectively analyzing the equipment connection and energy flow conditions of a single channel, and recording in a one-to-one correspondence form of energy outflow equipment-energy inflow equipment-energy data; the record is taken but not limited to being saved in a temporary txt file under a specified directory in the computer.
3. The system of claim 1, wherein the device constraint binding is: analyzing the input planning constraint file to obtain the constraint requirement of the power system, and determining the number P of the inlet and outlet ports of the plant station equipmenti≤PimaxType and direction, upper limit of energy flow of energy transmission pipeline
Energy flow, excess capacity limit Ri≤RimaxIn which P isiIndicating the number of ports, P, currently used by the deviceimaxRepresents the current device port ceiling, SijRepresenting the energy flow conduit load, S, connecting the ith to the jth deviceijmaxRepresenting the upper load limit, R, of the power flow conduit connecting the ith device to the jth deviceiRepresents the excess capacity, R, of the ith capacity equipmentimaxRepresents the upper limit of the excess capacity of the ith capacity equipment.
4. The system of claim 1, wherein the formula-variable-value tree persistence is: resolving the formula, the variable and the value, and performing associated storage, namely performing associated storage on the formula, the variable and the value in a tree structure;
the business rule association module carries out formula level inquiry: when the formula, the variable and the value are taken out of the database, inquiring layer by layer in a recursive mode and establishing a data structure; and taking the preset conditions of the energy network as a retrieval basis, calling different planning design evaluation algorithms according to different conditions, and providing a calculation basis in a quantitative evaluation stage.
5. The system of claim 1, wherein the business rule base is: storing the business rules in a form of a data table according to a formula, a variable and a value, wherein the form of the data table specifically comprises the following steps: formula-variable and variable-condition-value correspond to the memory table, respectively.
6. The system of claim 1, wherein the planning objective describes an overall planning objective of the power system, and is specifically a system that supports multiple objective functions consisting of one or more of the following:
wherein: cTargetRepresenting the calculated combined target value, omega, of multiple targetsiRepresenting the weight occupied by each target
TiRepresenting individual objective functions, e.g. target T of expected capacity1Energy target T2Energy storage target T3Budget target T4And n represents the number of planning targets.
7. The system as claimed in claim 1, wherein the system is further provided with a planning result output module which outputs the planned topological structure of the power system, the energy transmission pipeline label and the equipment graphic model representation in the form of SVG topological wiring diagram according to the composition and simulation calculation data of the evaluation module; and outputting the plant station equipment construction data, the energy flow simulation calculation data and the equipment operation setting parameters in the form of data packets.
8. An intelligent generation method of an energy network planning map based on the system of any one of the preceding claims, characterized in that support data is obtained by performing text mechanical word segmentation, structural analysis and formula analysis from multi-source text data, after plant station equipment parameters, equipment constraints, planning constraints, energy efficiency targets and evaluation standards are corrected and completed through database association, a planning map and a simulation operation data packet are automatically generated by combining energy channel analysis and service rule constraints and utilizing a dynamic edge weight ratio minimum spanning tree algorithm and a multi-target genetic algorithm, and finally a planning result is evaluated by a quantitative algorithm and output.
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Cited By (2)
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| CN112241838A (en) * | 2020-10-15 | 2021-01-19 | 四川省农业机械研究设计院 | Design system and method for large-scale beef cattle farm |
| CN113992519A (en) * | 2021-11-17 | 2022-01-28 | 国网天津市电力公司 | Power communication resource planning method capable of automatically calculating |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102254242A (en) * | 2011-06-10 | 2011-11-23 | 华北电力大学 | Planning network data system and fast intelligent generating method thereof |
| KR20160027481A (en) * | 2014-08-29 | 2016-03-10 | 주식회사 에코시안 | Building energy efficiency and service evaluation system, and processing method thereof |
| CN106021306A (en) * | 2016-05-05 | 2016-10-12 | 上海交通大学 | Ontology matching based case search system |
| CN109560968A (en) * | 2018-12-20 | 2019-04-02 | 中国电子科技集团公司第三十研究所 | A kind of the Internet resources intelligent planning and configuration method of dynamic strategy driving |
| CN109902289A (en) * | 2019-01-23 | 2019-06-18 | 汕头大学 | A kind of news video topic division method towards fuzzy text mining |
-
2019
- 2019-10-08 CN CN201910948867.2A patent/CN110765726B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102254242A (en) * | 2011-06-10 | 2011-11-23 | 华北电力大学 | Planning network data system and fast intelligent generating method thereof |
| KR20160027481A (en) * | 2014-08-29 | 2016-03-10 | 주식회사 에코시안 | Building energy efficiency and service evaluation system, and processing method thereof |
| CN106021306A (en) * | 2016-05-05 | 2016-10-12 | 上海交通大学 | Ontology matching based case search system |
| CN109560968A (en) * | 2018-12-20 | 2019-04-02 | 中国电子科技集团公司第三十研究所 | A kind of the Internet resources intelligent planning and configuration method of dynamic strategy driving |
| CN109902289A (en) * | 2019-01-23 | 2019-06-18 | 汕头大学 | A kind of news video topic division method towards fuzzy text mining |
Non-Patent Citations (1)
| Title |
|---|
| 刘煌煌;刘前进;陈柔伊;于力: "基于CYME软件的城市配电网规划仿真", 电力系统及其自动化学报, vol. 26, no. 10, pages 13 - 19 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112241838A (en) * | 2020-10-15 | 2021-01-19 | 四川省农业机械研究设计院 | Design system and method for large-scale beef cattle farm |
| CN113992519A (en) * | 2021-11-17 | 2022-01-28 | 国网天津市电力公司 | Power communication resource planning method capable of automatically calculating |
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