CN113052250A - Decision support method, system, device and medium based on meteorological disaster - Google Patents
Decision support method, system, device and medium based on meteorological disaster Download PDFInfo
- Publication number
- CN113052250A CN113052250A CN202110350023.5A CN202110350023A CN113052250A CN 113052250 A CN113052250 A CN 113052250A CN 202110350023 A CN202110350023 A CN 202110350023A CN 113052250 A CN113052250 A CN 113052250A
- Authority
- CN
- China
- Prior art keywords
- fuzzy
- decision
- information
- decision support
- meteorological
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/02—Computing arrangements based on specific mathematical models using fuzzy logic
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Automation & Control Theory (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Fuzzy Systems (AREA)
- Algebra (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Biomedical Technology (AREA)
- Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a decision support method, a decision support system, a decision support device and a decision support medium based on meteorological disasters, wherein the decision support method comprises the following steps: acquiring meteorological information and decision information, performing fuzzification processing on the meteorological information and the decision information respectively, and generating a fuzzy rule according to a fuzzy decision tree; inputting a problem, analyzing the problem to generate an analysis result, matching the analysis result with the fuzzy rule, and respectively generating decision information and forecasting disaster influence. According to the invention, through carrying out data mining and decision tree analysis on the meteorological information and the decision information and inputting the rules into the database, a user can conveniently obtain the decision information through a client, and carry out final decision and measure generation through background analysis data, effective rules can be effectively and quickly excavated, and a decision is generated from the effective rules, so that effective decision support is provided for effective disaster prevention and reduction measures adopted by governments and related departments.
Description
Technical Field
The invention relates to the field of risk analysis of power systems, in particular to a decision support method, a decision support system, a decision support device and a decision support medium based on meteorological disasters.
Background
In recent years, the global environment destruction problem is increasingly prominent, the climate change is obvious, the occurrence frequency of various natural disaster events is continuously increased, and the influence of the climate change and the meteorological conditions on the safe operation of electric power facilities and power grids is increasingly prominent. In severe weather, the frequency of fault tripping of the power grid due to wind-blown foreign matters and lightning stroke is increased, and the possibility of power failure in a large area still exists. The power equipment is used as an important public infrastructure and is an important foundation for guaranteeing the livelihood of people and promoting the development of the economic society. According to data statistics of power departments, natural disasters become the second major factor influencing safe and stable operation of a power system, and are only second to self-failure of equipment. From 2009 to the present, in the power grid faults caused by climate reasons in the global scope, the influence is over 10 thousands of people for 28 times, and the total number of the power grid large-area power failure accidents is 56%.
The promotion of the national strong smart grid strategy puts higher requirements on the dispatching and operating work of regional power grids. In order to realize safe and economic power grid dispatching operation, a dispatcher needs to pay attention to the characteristics and the operation state of primary and secondary equipment of a power grid, and also needs to pay attention to the real-time condition of weather of each geographic area, such as the current weather, temperature, precipitation, wind speed, lightning strike amount and the like of each power supply area of each transformer substation, the operation condition of the power grid is analyzed by utilizing the information, the safety and the reliability of the power grid in a period of time in the future are predicted, monitored and analyzed, and the preparation for possible power grid faults is prepared.
Due to the fact that power enterprises are insufficient in cognition of meteorological information value and insufficient in attention degree of meteorological information fineness, fusion of meteorological information and power grid monitoring is not deep, potential value of meteorological information is not fully excavated, and work aiming at reducing power grid equipment meteorological risk is lack of efficient management.
At present, the meteorological forecast monitoring in China is covered very densely, the accuracy and the resolution of meteorological forecast are higher and higher, and a good information sharing mechanism is established between an electric power department and a meteorological department. The power department receives a large amount of weather forecast information from the weather department in different time periods every day. If a method and means for effectively analyzing and managing the meteorological information are lacked, the meteorological disaster risk is difficult to be identified and prevented quickly and accurately, and the response speed of an electric power department to severe weather is reduced. Therefore, it is necessary to incorporate professional meteorological information into the power grid monitoring and early warning system, and perform meteorological information-power grid fault fusion and power grid risk prediction according to the meteorological information.
Disclosure of Invention
In order to solve the problems, the invention provides a decision support method, a decision support system, a decision support device and a decision support medium based on meteorological disasters, which are mainly used for solving the problem that the power system cannot fully mine meteorological information for managing power grid equipment.
In order to solve the above technical problem, a first aspect of the present invention provides a decision support method based on meteorological disasters, including:
acquiring meteorological information and decision information, respectively carrying out fuzzification processing on the meteorological information and the decision information, and generating a fuzzy rule according to a fuzzy decision tree;
inputting a problem, analyzing the problem to generate an analysis result, matching the analysis result with the fuzzy rule to generate decision information and forecast disaster influence respectively.
In a second aspect, the present invention provides a decision support system based on meteorological disasters, including:
the management terminal is used for acquiring meteorological information and decision information, respectively carrying out fuzzification processing on the meteorological information and the decision information, and generating a fuzzy rule according to a fuzzy decision tree;
and the user side is used for inputting the problems, analyzing the problems and generating an analysis result, matching the analysis result with the fuzzy rule, and respectively generating decision information and forecasting the disaster influence.
A third aspect of the invention provides a meteorological disaster based decision support apparatus, comprising a memory and a processor, wherein,
the memory is used for storing executable program codes;
the processor is coupled with the memory;
the processor calls the executable program codes stored in the memory to execute the decision support method based on the meteorological disaster.
A fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for executing the above decision support method based on meteorological disasters when the computer instructions are called.
The invention has the beneficial effects that: based on expert decision support technology and fuzzy rough set theory, through data mining and decision tree analysis of meteorological information and decision information and inputting rules into a database, a user can conveniently obtain decision information through a client, and final decision and measure generation is carried out through background analysis data, effective rules can be effectively and quickly mined out, and decisions are generated from the effective rules, so that effective decision support is provided for effective disaster prevention and reduction measures adopted by governments and related departments.
Drawings
FIG. 1 is a schematic flow chart illustrating a meteorological disaster-based decision support method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a client according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a decision support system based on meteorological disasters according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of a decision support device based on a meteorological disaster according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the following detailed description of the present invention is provided with reference to the accompanying drawings and detailed description. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings.
Example one
The embodiment provides a decision support method based on meteorological disasters, which comprises a management terminal and a client terminal.
As shown in fig. 1, the management terminal obtains weather information and decision information, performs fuzzification processing on the weather information and the decision information respectively, and generates a fuzzy rule according to a fuzzy decision tree;
the management end of the system is responsible for the warehousing management of knowledge and the warehousing management of rules. The management of knowledge mainly comprises meteorological information and decision information, wherein the meteorological information comprises real-time temperature, real-time humidity, real-time wind power and real-time rainfall, and the decision information comprises a plan, use expenses, users, final economy and personnel loss.
Because the decision tree algorithm of the invention is based on the fuzzy information system, and the current knowledge base is still a real-value database, the fuzzy processing is carried out on the knowledge base, and the fuzzy processing algorithm can be realized by using a fuzzy C mean algorithm (FCM), which is improved on a hard C mean clustering algorithm.
The fuzzification process (FCM algorithm) comprises the following steps:
s11, setting the number of clusters as c, c is more than 1 and less than n, the fuzzy index as m, m is more than or equal to 1 and less than or equal to infinity, and controlling the fuzzy degree of the classification matrix U, wherein the larger the value of m is, the larger the fuzzy degree presented by the obtained final clustering result is, and if the value of m is smaller, the smaller the fuzzy degree of the finally obtained result is. In practical application, m is preferably in the range of (1.5-2.5), and m is generally 2. Randomly setting various centers V, setting the convergence precision epsilon to be more than 0, and setting the iteration number k to be 0.
S12, substituting the cluster number and the fuzzy index into a membership iterative formula to calculate a membership matrix U(k+1)，
Ix＝{(i，j)|xj＝vx，1≤i≤c}；
S13: substituting the membership degree matrix into a clustering center iterative formula to calculate various centers V(k+1)，
Repeating S12 and S13 until the following termination condition is satisfied,
||V(k)-V(k-1)||＜ε，k≥1；
wherein u isijIs the membership degree of the ith sample belonging to the jth clustering center, v is the data set and the clustering center matrix, k is the iteration number, epsilon is the convergence precision, I is the sample point set, xiAre sample points.
For example, if the attribute of temperature is to be fuzzified, the real-time temperature data set can be divided into five categories of "severe cold, comfortable, hot, and intense heat" by using a fuzzy mean algorithm, and the membership of each data can be directly obtained from the final membership matrix, and the specific algorithm is as above. These degrees of membership may correspond exactly to fuzzy degrees of membership in a fuzzy information system. Correspondingly, other meteorological attributes and decision attributes can be fuzzified, and finally a fuzzy information system can be obtained.
Just fuzzy information systems are not enough for the user to make decisions, and the final decision is still dependent on the generation of fuzzy rules. Fuzzy rules can be generated through a decision tree algorithm, and a decision tree induction algorithm based on a fuzzy rough set can effectively mine the fuzzy rules in a fuzzy information system, so that the algorithm is used as a rule generation algorithm in the system.
The client inputs the problems, analyzes the problems and generates an analysis result, and matches the analysis result with the fuzzy rule to generate decision information and forecast disaster influence respectively.
The user side is mainly used for data query and decision product generation. Data querying can be easily implemented with database querying techniques. The decision-making product mainly comprises two parts: content of decisions and predicted impact. These can be obtained by a rule matching algorithm. The rule matching algorithm of the fuzzy decision tree in the patent is mainly a fuzzy ID3 learning algorithm, the fuzzy ID3 algorithm is an extension of a common ID3 induction algorithm, and unlike the common algorithm, in the fuzzy algorithm, fuzzy entropy based on probability distribution is used as an extension attribute selection standard.
The matching operation employs a fuzzy ID3 learning algorithm. The fuzzy ID3 learning algorithm includes the following steps:
s21, selecting the extended attribute;
selecting the extended attribute comprises the steps of:
s211, for each attribute A(k)，1≤k≤n，
To A(k)Each attribute value T ofi (k)，1≤i≤mkCalculate it relative to the class
J is more than or equal to 1 and less than or equal to m, and relative frequency
M (A) represents the sum of all membership of the fuzzy set,
to A(k)Each attribute value T ofi (k)，1≤i≤mkCalculating its fuzzy classification entropy:
s212, for A(k)Each attribute value 1 is not less than k and not more than n, and the fuzzy classification entropy is calculated as follows:
s213, selecting EkThe attribute which is the minimum value is taken as an extended attribute;
wherein, A is an attribute value, T is a fuzzy subset, E is a fuzzy classification entropy, k is iteration times, i is a sample number, j is a jth clustering center number, n is the number of all elements in a data set, k is the iteration times, M is the sum of all membership degrees representing the fuzzy set, and M is the clustering center number.
S22, segmenting the fuzzy sample set;
the segmentation fuzzy sample set specifically comprises the following steps: and when the node truth of the fuzzy decision tree is smaller than a preset threshold value, segmenting the fuzzy sample set, taking elements in fuzzy segmentation as sub-fuzzy sets, recursively calculating the average fuzzy classification entropy of the sub-fuzzy sets, and selecting the expansion nodes until a final fuzzy decision tree is generated.
S23, derive all fuzzy decision rules by all paths to leaf nodes on the fuzzy decision tree. In the algorithm of the decision tree, the fuzzy decision tree is closer to the real world than the common decision tree induction algorithm, so the algorithm has wider application space and greater development.
The method is based on expert decision support technology and fuzzy rough set theory, data mining and decision tree analysis are carried out on meteorological information and decision information (historical coping measures, early warning plans, disaster-resistant and reduction resources and the like), rules are recorded into a database, a user can conveniently obtain the decision information through a client, final decision and measure generation is carried out through background analysis data, effective rules can be effectively and quickly mined, and decisions are generated from the rules, so that effective decision support is provided for effective disaster-resistant and reduction measures adopted by governments and related departments.
As shown in fig. 2, the specific implementation is as follows:
(1) for each attribute in the weather information, a degree of membership Uij for each category in the fuzzy information system is calculated (step S12).
(2) And calculating the membership degree of the matching of the example and the condition part for each fuzzy classification rule, wherein the membership degree of the example belonging to a certain class is used as the membership degree of the example.
(3) If multiple rules are finally generated to classify this data instance into one class with different degrees of membership, then the highest degree of membership is taken as the final degree of membership.
(4) If the instance is divided into several different classes with different degrees of membership and an explicit decision needs to be made, the class with the highest degree of membership is taken as the final class.
Example two
A weather disaster based decision support system comprising:
the management terminal is used for acquiring the meteorological information and the decision information, respectively carrying out fuzzification processing on the meteorological information and the decision information, and generating a fuzzy rule according to a fuzzy decision tree;
and the user side is used for inputting the problems, analyzing the problems and generating an analysis result, matching the analysis result with the fuzzy rule, and respectively generating decision information and predicting disaster influence.
System construction: the system comprises a set of meteorological acquisition server, a set of dynamic early warning monitoring server, two sets of reverse physical isolation devices, a set of data bridging workstation, a visualization system switch and a set of data server, and is shown in figure 3.
The meteorological collection server acquires real-time meteorological information from a meteorological department in a safe IV area of the power system, and realizes the collection of original meteorological monitoring data, including corresponding meteorological geographic information data and meteorological basic data (temperature, wind speed and precipitation).
(1) The weather collection server transmits real-time weather information to a bridging workstation in a safe III area of the power system through a reverse physical isolation device.
(2) And the III-area bridging workstation transmits real-time meteorological information to the electric power dynamic early warning monitoring server in the electric power system safety I area through the reverse physical isolation device.
(3) The dynamic electric power early warning monitoring server analyzes meteorological data and electric power equipment and mainly comprises the following functional modules:
1) meteorological information analysis module
And analyzing and corresponding the geographic coordinate information of the meteorological information and the geographic coordinate information of the power equipment.
2) EMS data acquisition module
And acquiring power grid operation parameters including line tide, main transformer oil temperature, winding temperature, load and bus voltage from the EMS system.
3) Power grid dynamic early warning module
The safety analysis of different meteorological information power grids is realized, the monitoring of meteorological and power grid main information under different meteorological conditions is ensured, and power grid operation risk points are displayed.
According to weather conditions, the operation safety limit value of the power transmission line is automatically adjusted, attention to weak links of the power grid and remote guidance of site construction are enhanced when the wind speed is abnormal, and the control safety of the power grid is improved; when precipitation is abnormal, monitoring of affected equipment is enhanced, and abnormal treatment efficiency is improved; real-time intelligent warning is carried out on meteorological conditions which are not suitable for field operation, and the safety of the field operation and the safe operation of a power grid are ensured; the method for sorting the fault risks of the power grid equipment based on the meteorological information is used for realizing the fault probability sorting of the equipment, and is applied to monitoring the operation section of the power grid and static safety analysis.
EXAMPLE III
As shown in fig. 4, fig. 4 is a schematic structural diagram of a decision support device based on a meteorological disaster according to an embodiment of the present invention. As shown in fig. 4, the apparatus includes a memory 101 and a processor 102, wherein,
the memory 101 is used for storing executable program codes;
the processor 102 is coupled with the memory 101;
the processor 102 calls the executable program code stored in the memory 101 to execute the weather disaster based decision support method according to the first embodiment.
The Memory 101 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 101 includes a non-transitory computer-readable medium. The memory 101 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 101 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function, instructions for implementing the various method embodiments described above, and the like; the storage data area may store data created according to the use of the server, and the like.
Example four
In addition, the present application further discloses a computer-readable storage medium, which stores computer instructions for executing the meteorological disaster decision support method according to the first embodiment when the computer-readable storage medium runs on a computer.
It will be understood by those skilled in the art that all or part of the steps in the methods of the embodiments described above may be implemented by instructions associated with hardware via a program, and the program may be stored in a computer-readable storage medium, where the storage medium includes Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM), or other Memory A tape memory, or any other medium readable by a computer that can be used to carry or store data.
The decision support method, system, device and medium based on meteorological disasters disclosed in the embodiments of the present application are introduced in detail, and specific examples are applied in the text to explain the principle and implementation of the present application, and the description of the embodiments is only used to help understand the method and core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A decision support method based on meteorological disasters is characterized by comprising the following steps:
acquiring meteorological information and decision information, performing fuzzification processing on the meteorological information and the decision information respectively, and generating a fuzzy rule according to a fuzzy decision tree;
inputting a problem, analyzing the problem to generate an analysis result, matching the analysis result with the fuzzy rule, and respectively generating decision information and forecasting disaster influence.
2. The weather disaster based decision support method of claim 1, wherein the weather information comprises real-time temperature, real-time humidity, real-time wind power and real-time rainfall, and the decision information comprises plans, use expenses, users and finally economic and personnel loss.
3. The weather disaster based decision support method of claim 1, wherein the obfuscation process comprises the steps of:
s11, setting the number of clusters as c, c is more than 1 and less than n, fuzzy index as m, m is more than or equal to 1 and less than or equal to infinity,
s12, substituting the cluster number and the fuzzy index into a membership iterative formula to calculate a membership matrix U(k+1)，
Ij＝{(i，j)|xj＝vi，1≤i≤c}；
S13: substituting the membership matrix into a clustering center iterative formula to calculate various centers V(k+1)，
Repeating S12 and S13 until the following termination condition is satisfied,
||V(k)-V(k-1)||＜ε，k≥1；
wherein u isijIs the membership degree of the ith sample belonging to the jth clustering center, v is the data set and the clustering center matrix, k is the iteration number, epsilon is the convergence precision, I is the sample point set, xiAre sample points.
4. The weather disaster based decision support method of claim 1, wherein the matching operation employs a fuzzy ID3 learning algorithm.
5. The weather disaster based decision support method of claim 4, wherein the fuzzy ID3 learning algorithm comprises the steps of:
s21, selecting the extended attribute;
s22, segmenting the fuzzy sample set;
s23, deriving all the fuzzy decision rules through all paths to leaf nodes on the fuzzy decision tree.
6. The weather disaster based decision support method according to claim 5, wherein the selecting extended attributes comprises the steps of:
s211, for each attribute A(k)，1≤k≤n，
To A(k)Each attribute value T ofi (k)，1≤i≤mkCalculate it relative to the class
J is more than or equal to 1 and less than or equal to m, and relative frequency
M (A) represents the sum of all membership of the fuzzy set,
to A(k)Each attribute value T ofi (k)，1≤i≤mkCalculating its fuzzy classification entropy:
s212, for A(k)Each attribute value 1 is not less than k and not more than n, and the fuzzy classification entropy is calculated as follows:
s213, selecting EkThe attribute which is the minimum value is taken as an extended attribute;
wherein A is an attribute value, T is a fuzzy subset, E is a fuzzy classification entropy, k is iteration times, i is a sample number, j is a jth clustering center number, n is the number of all elements in a data set, k is the iteration times, M is the sum of all membership degrees representing the fuzzy set, and M is the number of the clustering centers.
7. The weather disaster based decision support method according to claim 5, wherein the segmentation fuzzy sample set is specifically: and when the node truth of the fuzzy decision tree is smaller than a preset threshold value, segmenting a fuzzy sample set, taking elements in fuzzy segmentation as a sub-fuzzy set, recursively calculating the average fuzzy classification entropy of the sub-fuzzy set, and selecting an expansion node until a final fuzzy decision tree is generated.
8. A weather disaster based decision support system, comprising:
the management terminal is used for acquiring meteorological information and decision information, respectively carrying out fuzzification processing on the meteorological information and the decision information, and generating a fuzzy rule according to a fuzzy decision tree;
and the user side is used for inputting the problems, analyzing the problems and generating an analysis result, matching the analysis result with the fuzzy rule, and respectively generating decision information and forecasting the influence of the disaster.
9. A weather disaster based decision support apparatus, the apparatus comprising a memory and a processor, wherein the memory is configured to store executable program code;
the processor is coupled with the memory;
the processor calls the executable program code stored in the memory to execute the weather disaster based decision support method according to any one of claims 1-7.
10. A computer-storable medium that stores computer instructions that, when invoked, perform a meteorological disaster-based decision support method according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110350023.5A CN113052250A (en) | 2021-03-31 | 2021-03-31 | Decision support method, system, device and medium based on meteorological disaster |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110350023.5A CN113052250A (en) | 2021-03-31 | 2021-03-31 | Decision support method, system, device and medium based on meteorological disaster |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113052250A true CN113052250A (en) | 2021-06-29 |
Family
ID=76516866
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110350023.5A Pending CN113052250A (en) | 2021-03-31 | 2021-03-31 | Decision support method, system, device and medium based on meteorological disaster |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113052250A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114370611A (en) * | 2022-01-17 | 2022-04-19 | 四创科技有限公司 | Pipe network inflow infiltration monitoring method and terminal |
-
2021
- 2021-03-31 CN CN202110350023.5A patent/CN113052250A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114370611A (en) * | 2022-01-17 | 2022-04-19 | 四创科技有限公司 | Pipe network inflow infiltration monitoring method and terminal |
| CN114370611B (en) * | 2022-01-17 | 2024-04-12 | 四创科技有限公司 | Pipe network inflow and infiltration monitoring method and terminal |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zheng et al. | Short-term wind power forecasting using a double-stage hierarchical ANFIS approach for energy management in microgrids | |
| Sheu | Dynamic relief-demand management for emergency logistics operations under large-scale disasters | |
| CN110807550B (en) | Distribution transformer overload recognition and early warning method based on neural network and terminal equipment | |
| CN110311376A (en) | A kind of Electrical Power System Dynamic security evaluation collective model and space-time method for visualizing | |
| CN109492857A (en) | A kind of distribution network failure risk class prediction technique and device | |
| CN105512448A (en) | Power distribution network health index assessment method | |
| CN107918830A (en) | A kind of distribution Running State assessment system and method based on big data technology | |
| CN108964023B (en) | Short-term prediction method and system for bus voltage situation of power grid | |
| CN115730749B (en) | Power dispatching risk early warning method and device based on fusion power data | |
| CN109947806A (en) | A kind of Super High construction safety accident emergency aid decision-making method of case-based reasioning | |
| Hou et al. | Spatial distribution assessment of power outage under typhoon disasters | |
| CN107784392A (en) | A kind of the defects of transmission line of electricity based on machine learning Forecasting Methodology and device | |
| CN114548800A (en) | Future-state power grid maintenance risk identification method and device based on power grid knowledge graph | |
| CN107784393A (en) | A kind of the defects of transmission line of electricity Forecasting Methodology and device | |
| CN106845780A (en) | A kind of generating set operating condition analysis system and its analysis method | |
| Doostan et al. | Predicting lightning-related outages in power distribution systems: A statistical approach | |
| CN113052250A (en) | Decision support method, system, device and medium based on meteorological disaster | |
| JP2003090887A (en) | Predication system and prediction method of instantaneous voltage drop by thunderbolt | |
| Hu et al. | Operational reliability evaluation method based on big data technology | |
| CN116128197A (en) | Intelligent airport management system and method | |
| Leite et al. | Failure probability metric by machine learning for online risk assessment in distribution networks | |
| Hou et al. | Research on real power grid security and stability based on big data analysis method and online data of power grid | |
| CN108108909A (en) | Data analysing method towards electric power accident, misoperation fault with operating accident against regulations | |
| CN114389361B (en) | Power grid panoramic control method and system for zero-carbon operation of county power grid | |
| Liang et al. | Research on risk evaluation of logistics park construction project by FMEA based on hesitation environment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |