Power grid operation simulation data management method

Abstract

The invention provides a power grid operation simulation data management method and a related device. The power grid operation simulation data management method comprises the following steps: acquiring power grid operation simulation basic data through a pre-constructed model library and classifying; performing data format verification and data relation verification on the acquired power grid operation simulation basic data, and recording error feedback information for the data which does not pass the format verification; related data in a model library are repaired according to the error feedback information; generating a required operation simulation calculation scheme by utilizing a preset personalized rule, acquiring corresponding data from the verified and repaired model library based on the operation simulation calculation scheme, and performing calculation; and carrying out visual display on calculation results of different operation simulation calculation schemes so as to carry out result analysis. Through a standardized data format, a multi-level verification mechanism and efficient calculation and visual display, the accuracy, consistency and analysis efficiency of the power grid operation simulation data are remarkably improved, and reliable support is provided for decision making.

Description

Technical Field

[0001] The invention belongs to the technical field of data management, and in particular relates to a method for managing power grid operation simulation data. Background Art

[0002] With the transformation of the global energy structure and the widespread use of renewable energy, the development of new power systems has entered a new stage. In this process, the power industry is not only facing the need to improve the efficiency of power grid operation, but also has higher requirements for data analysis capabilities. In order to better adapt to these changes, the business scope of the power industry continues to expand, especially in the areas of power grid operation monitoring, load forecasting, balance analysis, etc. The data processing work is becoming increasingly heavy. With the increase in business complexity, traditional data management methods have gradually exposed their shortcomings in dealing with massive data.

[0003] Specifically in the daily operation of the power system, staff need to frequently edit and analyze a large amount of grid operation simulation data. However, in actual operation, it is becoming increasingly difficult to use traditional spreadsheet software (such as Excel) to manage this data. On the one hand, as the amount of data grows, it becomes extremely challenging to ensure the consistency and accuracy of all input information; on the other hand, the lack of an effective data verification mechanism makes manual input errors difficult to avoid, and it is difficult to achieve comprehensive and accurate data verification through simple formula settings. In addition, when faced with multi-scheme comparisons or batch calculation tasks, existing tools are also unable to provide an efficient and convenient way to generate personalized reports or quickly complete balance analysis in different scenarios.

[0004] The existing technology for managing power grid operation simulation data in tabular form has obvious defects: first, the lack of unified standards leads to confusion in data format, which brings obstacles to subsequent data integration and utilization; second, there are serious deficiencies in ensuring data quality, especially in maintaining logical relationships between data and executing complex queries; finally, the current method needs to be improved in supporting large-scale parallel computing and providing intuitive and easy-to-understand result presentation, which directly affects the speed and accuracy of decision-making. In summary, it is imperative to develop a new generation of data management system that can overcome the above problems. Summary of the invention

[0005] In view of this, the present invention provides a power grid operation simulation data management method, aiming to overcome the above-mentioned problems faced by traditional means by constructing an integrated and intelligent power grid operation simulation data management system.

[0006] In order to achieve the above object, the technical solution provided by the present invention is as follows:

[0007] In a first aspect, the present invention provides a method for managing power grid operation simulation data, comprising the following steps:

[0008] Obtain and classify basic data for power grid operation simulation through a pre-built model library, which includes several different types of basic data for power grid operation simulation;

[0009] Perform data format verification on the acquired basic data of power grid operation simulation, and record error feedback information for data that fails the format verification;

[0010] Perform data relationship verification on the basic data of power grid operation simulation that have passed the format verification, and record error feedback information for the data that have not passed the relationship verification. The data relationship verification at least includes the association relationship verification and the value relationship verification between the classified data;

[0011] Repair the relevant data in the model library according to the error feedback information, and store all the data that have passed the verification and repair in the model library;

[0012] Generate the required operation simulation calculation plan using the preset personalized rules, obtain the corresponding data from the model library that has been verified and repaired based on the operation simulation calculation plan, and perform calculations;

[0013] The calculation results of different simulation calculation schemes are visualized to facilitate result analysis.

[0014] Furthermore, the data format verification includes:

[0015] Traverse the attributes of the basic data of power grid operation simulation in the model library, and perform data verification according to the rules annotated in the attributes, which are the preset constraints for different types of data;

[0016] The information violating the constraint condition is recorded as the error information of the corresponding data, so as to form the first error feedback information of the basic data of the power grid operation simulation.

[0017] Furthermore, data relationship verification includes:

[0018] Verify the basic data of power grid operation simulation in the model library according to preset verification rules, which include data association relationship rules and data value relationship rules;

[0019] The information that does not meet the verification rules is recorded as the error information of the corresponding data, forming the second error feedback information of the basic data of the power grid operation simulation.

[0020] Furthermore, the data of the model library is divided in turn according to the regional level. In each minimum regional level, the basic data of the power grid operation simulation is classified according to the type node to form a data model library with a tree structure.

[0021] Furthermore, the data association relationship rule is the master-slave relationship between each data after the basic data of the power grid operation simulation is classified.

[0022] Furthermore, the data numerical relationship rule is the numerical relationship between various data after the basic data of the power grid operation simulation is classified.

[0023] Furthermore, the personalized rules are basic operation rules set for different types of power grid operation simulation basic data.

[0024] In a second aspect, the present invention provides a power grid operation simulation data management device, comprising:

[0025] A data acquisition module is used to acquire and classify basic data of power grid operation simulation through a pre-built model library, wherein the model library includes several different types of basic data of power grid operation simulation;

[0026] The first verification module is used to perform data format verification on the acquired power grid operation simulation basic data, and record error feedback information for the data that fails the format verification;

[0027] The second verification module is used to perform data relationship verification on the basic data of power grid operation simulation that has passed the format verification, and record error feedback information for the data that has not passed the relationship verification. The data relationship verification at least includes the association relationship verification and the value relationship verification between the classified data;

[0028] A data repair module is used to repair relevant data in the model library according to error feedback information, and store all data that have passed verification and repair in the model library;

[0029] A simulation calculation module is used to generate the required operation simulation calculation scheme by using preset personalized rules, and obtain corresponding data from the model library that has been verified and repaired and perform calculations based on the operation simulation calculation scheme;

[0030] The result analysis module is used to visualize the calculation results of different simulation calculation schemes for the purpose of result analysis.

[0031] In a third aspect, the present invention provides a computer device, the device comprising a processor and a memory:

[0032] The memory is used to store the computer program and send the instructions of the computer program to the processor;

[0033] The processor executes the power grid operation simulation data management method according to the first aspect according to the instructions of the computer program.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for managing power grid operation simulation data as in the first aspect.

[0035] In summary, the present invention provides a power grid operation simulation data management method and related devices, which obtain and classify power grid operation simulation basic data through a pre-built model library, and the model library includes several different types of power grid operation simulation basic data; perform data format verification on the obtained power grid operation simulation basic data, and record error feedback information for data that fails the format verification; perform data relationship verification on the power grid operation simulation basic data that passes the format verification, and record error feedback information for data that fails the relationship verification, and the data relationship verification at least includes the association relationship verification and the numerical relationship verification between the classified data; repair the relevant data in the model library according to the error feedback information, and store all the data that pass the verification and repair in the model library; generate the required operation simulation calculation scheme using preset personalized rules, and obtain the corresponding data from the model library that has completed the verification and repair based on the operation simulation calculation scheme and perform calculation; visualize the calculation results of different operation simulation calculation schemes for result analysis. The present invention significantly improves the accuracy, consistency and analysis efficiency of power grid operation simulation data through standardized data format, multi-level verification mechanism, and efficient calculation and visualization display, and provides reliable support for decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A flowchart of a power grid operation simulation data management method provided by an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of the relationship between model library data provided by an embodiment of the present invention;

[0039] Figure 3 A diagram of the implementation process of the operation simulation calculation solution provided by an embodiment of the present invention;

[0040] Figure 4 A block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] See also Figure 1 , this embodiment provides a power grid operation simulation data management method, comprising the following steps:

[0043] S11: Obtain and classify basic data for power grid operation simulation through a pre-built model library, where the model library includes several different types of basic data for power grid operation simulation.

[0044] It should be noted that this step predefines a basic data model library containing multiple types (such as generators, loads, lines, etc.). The data collected from the actual power grid will be classified and stored according to these predefined types, thereby ensuring that the data is organized according to a unified standard and structure.

[0045] S12: Performing data format verification on the acquired basic data of power grid operation simulation, and recording error feedback information for the data that fails the format verification.

[0046] It should be noted that this step sets a series of format verification rules to automatically check whether each record meets the requirements. For data that does not meet the format, detailed error information is recorded to ensure that all input data meets the predetermined format standards.

[0047] S13: Performing data relationship verification on the basic data of power grid operation simulation that has passed the format verification, and recording error feedback information on the data that has not passed the relationship verification. The data relationship verification at least includes the association relationship verification and the numerical relationship verification between the classified data.

[0048] It should be noted that after completing the format check, this step further checks the relevance of data and the rationality of values. For example, verify whether the line name exists in the corresponding line basic information table, or check whether the power value is within a reasonable range. This enhances the consistency of the data and prevents the occurrence of isolated or contradictory data items.

[0049] S14: Repair the relevant data in the model library according to the error feedback information, and store all the data that have passed the verification and repair in the model library.

[0050] It should be noted that appropriate measures can be taken to correct this step, including manual intervention or the use of automated repair tools, to ensure that only data that has been strictly inspected and repaired as necessary can be ultimately used.

[0051] S15: Generate the required operation simulation calculation plan using the preset personalized rules, and obtain the corresponding data from the model library that has been verified and repaired based on the operation simulation calculation plan and perform calculations.

[0052] It should be noted that this step sets one or more sets of personalized calculation rules according to user needs or specific business logic, and then extracts the required information from the verified data to perform the corresponding calculations. This method improves the flexibility and pertinence of the calculation process and can better serve complex power grid operation decisions.

[0053] S16: Visually display the calculation results of different running simulation calculation schemes to facilitate result analysis.

[0054] It should be noted that the results of different solutions are presented in an easy-to-understand form using charts, maps or other graphical interface elements. This method enhances the readability and interactivity of data analysis, helps to quickly identify key trends and differences, and thus accelerates the decision-making process.

[0055] This embodiment provides a method for managing power grid operation simulation data, which ensures the quality and consistency of data through a strict verification mechanism, and improves the practical value of the system through flexible calculation rules and a friendly user interface. This method not only solves many limitations brought by traditional table formats, but also provides more efficient and accurate support for power grid operation simulation, which is very suitable for the complex and changing needs of modern power systems.

[0056] In one embodiment, the data format verification includes:

[0057] S21: traverse the attributes of the basic data of power grid operation simulation in the model library, and perform data verification according to the rules annotated in the attributes, where the annotations are preset constraints for different types of data;

[0058] S22: Recording the information that violates the constraint condition as error information of the corresponding data, and forming first error feedback information of the basic data of the power grid operation simulation.

[0059] In specific implementation, you can use annotations (@NotNull, @Min, @Max, @Pattern, etc.) to preset validation rules for the attributes of the model library, such as checking whether the attribute is empty, whether it is a character, whether it is a number, whether it is in date format, whether it is within a specific range, etc. The functions of annotations are as follows:

[0060] @NotNull: whether it is empty, @Min: minimum value, @Max: maximum value, @Pattern: custom validation regular expression, etc.

[0061] Check the validity, integrity and compliance of data according to the Validator. The Validator is the object that actually performs the validation operation. The Validator will first check whether the device type object in the model library has annotations (@NotNull, @Min, @Max, @Pattern, etc.). Then, the Validator will traverse the properties of the device type object in the model library and perform validation according to the annotation rules. For example, if a property is annotated with @NotNull, the Validator will check whether the property is empty. If the constraint is violated, a ConstraintViolation (detailed information of the violation that violated the constraint) object will be created to represent the violated constraint. Finally, all the information of the constraint violation will be collected into a collection and returned.

[0062] Obtain and provide real-time feedback on error information that does not comply with validation rules, record validation error logs and store them in the database. You can manually generate data format validation exception files from validation error log information to help users quickly correct errors and improve the fluency of user operations.

[0063] In one embodiment, data relationship verification includes:

[0064] S31: verifying the basic data of power grid operation simulation in the model library according to preset verification rules, where the verification rules include data association relationship rules and data value relationship rules;

[0065] S32: Record the information that does not meet the verification rules as error information of the corresponding data to form second error feedback information of the power grid operation simulation basic data.

[0066] Take the verification rules for setting data such as units and unit specified output as an example.

[0067] Step 1: First, formulate the verification rules. The specific rules are as follows:

[0068] (1) Unit

[0069] ① Commissioning time <= retirement time;

[0070] ② Whether the node exists;

[0071] ③ Unit capacity>=minimum output;

[0072] ④ The adjustment rate of the unit climbing is <= the unit capacity;

[0073] ⑤ The adjustment rate of the unit during ramp-up <= the unit capacity;

[0074] ⑥ The maximum number of starts and stops per day is <= 24.

[0075] (2) Specified output of the unit

[0076] ① Start date <= end date;

[0077] ② The value of period 1 to period 24 <= unit capacity;

[0078] ③ When the difference between the values ​​of the adjacent time periods from time period 1 to time period 24 is greater than or equal to 0, and the difference between the values ​​of the adjacent time periods from time period 1 to time period 24 is less than or equal to the ramp down adjustment rate of the unit, the power source types of wind power and photovoltaic power generation are not involved in the verification;

[0079] ④ When the difference between the values ​​of adjacent time periods from time period 1 to time period 24 is <= 0, and the difference between the values ​​of adjacent time periods from time period 1 to time period 24 is <= the ramp adjustment rate of the unit, the power source types of wind power and photovoltaic power are not involved in the verification.

[0080] (3) Three-stage hydropower output

[0081] ① The maximum power of the hydropower station from January to December is <= the sum of the unit capacities of the hydropower station.

[0082] (4) Node

[0083] ① Commissioning date <= retirement date;

[0084] ② Whether the region to which the node belongs exists in the middle region.

[0085] (5) Load curve

[0086] ① Start date <= end date;

[0087] ② Whether the node name exists in the node.

[0088] (6) Line

[0089] ① Commissioning date <= retirement date;

[0090] ② Whether the starting node and the ending node exist.

[0091] (7) Line power

[0092] ① Whether the line name exists in the line;

[0093] ② The maximum line power value from January 1 to December of the month >= the average line power value from January 1 to December of the month, and the average line power value from January 1 to December of the month >= the minimum line power value from January 1 to December of the month;

[0094] ③ The maximum line power value from January 1 to December is less than the transmission capacity of the line;

[0095] ④ The average line power value from 1 to 12 months*number of days in the month*24 <= the sum of the loads of the corresponding line stop nodes;

[0096] ⑤ The number of units at the starting node of the line is >= 1.

[0097] (8) Line specified power

[0098] ① The specified power value of the line is less than or equal to the transmission capacity of the line;

[0099] ② The sum of the line specified power values ​​(each month) is less than or equal to the sum of the loads of the termination nodes of the corresponding line.

[0100] Step 2: Search through the database according to the SQL statements of each device object in the model library to obtain the required latest data to be verified.

[0101] Step 3: According to the validation rules of the first step, the validation data is iterated in sequence to perform association validation and numerical relationship validation analysis. Determine whether the validated data matches. If the match is successful, execute the next set of data to be validated. If the match is not successful, record the number of error data rows and error feedback information, and accumulate the number of errors to facilitate knowing the total number of error feedback information. Record the error feedback information through ObservableList (collection), use ListView (scrollable list item collection in JavaFx) to monitor and bind the error feedback information, and when the latest error feedback information is obtained, ListView will automatically update the display to monitor the model library validation process in real time.

[0102] Step 4: Continuously monitor and obtain verification error feedback information and feed it back to the user in real time. Record the verification process to form an error log and store it in the database. The verification error log information can be manually generated into a data relationship verification exception file to help users quickly locate and repair data.

[0103] In one embodiment, the data of the model library is divided in sequence according to regional levels. In each minimum regional level, the basic data of the power grid operation simulation is classified according to the type node to form a data model library with a tree structure.

[0104] like Figure 2 As shown, Figure 2 It is a data relationship diagram of the model library. The diagram is divided into levels according to medium area -> small area -> node, and then divided into nodes, including units, unit specified output, unit maintenance, three-stage hydropower output, utilization hours, monthly electricity, load curves, line specified power, line power, power output limitation, power maintenance and other basic data of power grid operation simulation to form a model library.

[0105] In one embodiment, the data association relationship rule is the master-slave relationship between various data after the basic data of power grid operation simulation is classified.

[0106] Data association rules can be used to check whether there is a correct association between different data items. For example, whether the line name in the line power and quantity exists in the line, and ensure that each line name in the line power and quantity has corresponding line basic information.

[0107] In one embodiment, the data value relationship rule is the value relationship between each data item after the basic data of the power grid operation simulation is classified.

[0108] Data value relationship rules can be used to verify specific relationships between numerical data, such as whether one value is within a specific range of another value (maximum number of starts and stops of the unit per day <= 24), or whether the ratio of two values ​​meets the requirements.

[0109] In one embodiment, the personalized rules are basic operation rules set for different types of power grid operation simulation basic data. Based on the personalized rules, when performing data simulation editing, the following steps can be followed:

[0110] Step 1: Create a plan

[0111] ① Solution import: supports uploading multiple table files. After successful import, proceed to step 2; if the import fails, a prompt will be given.

[0112] ② Create a new plan: Manually create new plan data in the system and introduce "model rule verification" during the data editing process.

[0113] Step 2: Data verification

[0114] Call "Model Rule Verification" to perform data verification. If it passes, proceed to step three; if it fails, a prompt will be given and the user will return to step one or the user will be allowed to modify the incorrect data on the current page.

[0115] Step 3: Solution storage

[0116] The solution data that meets the "model rule verification" is stored in the database.

[0117] Step 4: Edit the plan

[0118] ① Display with charts and graphics: such as bar charts, line charts, pie charts, etc.

[0119] ② Provide quick search function: to facilitate users to find specific data.

[0120] ③ Introduce "model rule verification" during data editing: save if passed, and give a prompt and re-edit if failed.

[0121] ④ Support copying and pasting single or multiple cells.

[0122] ⑤ You can copy and paste multiple rows in batches to insert data: To avoid duplicate data that violates the unique check, add "(copy)" after the corresponding value as the unique identifier of the newly added data.

[0123] Step 5: Build a solution model

[0124] The "model rule verification" has ensured the rigor of logic and the accuracy of data, and a complete set of model data has been constructed.

[0125] Step 6: Batch generation of solutions

[0126] ① Set up a personalized rule generation solution model: Based on the device parameters of the "model library", clarify the personalized rule settings, and batch generate multiple different solution generation rules based on these personalized rules, as follows:

[0127] Combination: specify attribute value, arithmetic operator, data value, comparison operator, expected value.

[0128] Examples: Unit Capacity + 10 = 600, Unit Capacity - 10 < 300, Unit Capacity - 10 >= 500.

[0129] Step 7: Batch calculation of solutions

[0130] ① Support running simulation calculations of one or more schemes.

[0131] ② Formulate calculation rules based on the system parameters in the solution model, and then dispatch the model calculation tool to perform balance analysis and calculation. The feedback information during the calculation process can be monitored in real time. If the calculation is successful, go to step 8; if the calculation fails, pause and feedback error information.

[0132] ③ Visual operation simulation calculation process monitoring: Provides a monitoring interface to observe data inflow, processing speed, resource usage, etc., and can present the iterative process of the calculation, parameter changes and the gradual generation of the simulation calculation results in real time, obtain the latest information in the simulation calculation process, and update the display in real time.

[0133] Step 8: Generate calculation results

[0134] After the equilibrium analysis calculation is successful, the generated operation simulation result data is stored in the database, and the equilibrium result files of each scheme are generated in the form of table files.

[0135] Step 9: Result Analysis

[0136] ① Display in the form of charts and graphics: such as bar charts, line charts, pie charts, etc.

[0137] ② Provide quick search function.

[0138] Comparative analysis: Different scheme models can be summarized according to the region and the specified attributes to intuitively compare the differences in the results of different simulation calculations. Figure 3 shown.

[0139] Take the unit simulation operation calculation as an example. The operation simulation mathematical model is a unit combination model with days as the time unit and hours as the calculation step. The objective function is to minimize the power generation cost of the entire system. The expression of the objective function is as follows:

[0140] (1)

[0141] Where: Indicates the system power generation cost, variable subscript , , , , Respectively represent the non-startable shut-down thermal power units, the startable shut-down thermal power units, the hydropower units, the pumped storage units and the renewable energy units; the variable subscripts Indicates the removal of renewable energy. Indicates that the thermal power unit cannot be started or stopped during the day. contribution; Indicates that the thermal power units that can be started and shut down during the day are in the period contribution; Renewable energy units in the period contribution; Indicates the hydropower unit in the period contribution; Indicates the pumped storage unit in the period contribution; Renewable energy units in the period The removal output; Indicates the power generation cost of thermal power units that cannot be started or stopped within the day; It indicates the power generation cost of thermal power units that can be started and stopped within the day; represents the electricity generation cost of renewable energy units; represents the power generation cost of the hydropower unit; represents the power generation cost of the pumped storage unit; To cut out the costs of renewable energy; Cost of starting and stopping the unit; The thermal power units that can be restarted within the day Time period start and stop state variables;

[0142] The constraints mainly include the operation constraints of conventional units such as thermal power and hydropower, new energy units, system power balance constraints, positive and negative reserve constraints, etc. The mathematical expression of the constraints is as follows:

[0143] (2)

[0144] (3)

[0145] (4)

[0146] (5)

[0147] (6)

[0148] (7)

[0149] (8)

[0150] (9)

[0151] (10)

[0152] (11)

[0153] (12)

[0154] (13)

[0155] (14)

[0156] (15)

[0157] (16)

[0158] (17)

[0159] (18)

[0160] Where: Indicates the time period number; Indicates the total number of time periods; The thermal power units cannot be started or stopped within the day. Time period start and stop state variables; For hydroelectric units Time period start and stop state variables; For pumped storage group Time period power generation state variables; For pumped storage group Period pumping state variables; Indicates the system time period Load; is the positive reserve rate required by the system in period t; is the negative reserve rate required by the system in period t; Indicates that the maximum output of the thermal power unit cannot be started or stopped; Indicates the maximum output of the thermal power generating units that can be started and stopped; Indicates the maximum output of the hydropower unit; Indicates the maximum power generation output of the pumped storage unit; Indicates the maximum pumping output of the water storage unit; Indicates the power generation capacity of new energy units; Indicates the minimum output of the thermal power unit that cannot be started or stopped; Indicates the minimum output of the thermal power unit that can be started and stopped; Indicates the minimum output of the hydropower unit; It represents the conversion efficiency of pumped storage unit; Indicates the minimum continuous power-on time; Indicates the minimum continuous downtime; Indicates that the output climbing limit of the thermal power unit cannot be started or stopped; Indicates the lower limit of the output ramp of the thermal power generating units that can be started or stopped; Indicates the output ramp upper limit of the thermal power generating units that can be started or stopped; Indicates the lower limit of the output ramp of the thermal power generating unit that cannot be started or stopped; Indicates the daily power generation of the hydropower unit.

[0161] Formula (2) is the power balance constraint; Formula (3)-(4) is the system positive and negative reserve constraint; Formula (5)-(9) is the unit output upper and lower limit constraint; Formula (10)-(11) is the minimum start-up and shutdown time constraint of the start-stop unit; Formula (12)-(13) is the unit climbing constraint; Formula (14) is the hydropower unit power constraint; Formula (15)-(16) is the pumped storage unit pumped power generation balance constraint; Formula (17)-(18) is the variable range constraint.

[0162] Through the above scheme, the present invention can have the following advantages:

[0163] 1. Perform multi-level data verification (including format verification and relationship verification) through "model rule verification" to ensure data consistency and accuracy.

[0164] 2. Continuously introduce "model rule verification" during the data editing process to ensure the logical correctness and numerical rationality of the data, reduce errors and inconsistencies. And repair the relevant data in the model library based on the error feedback information to further improve the reliability of the data.

[0165] 3. Display data in charts and graphs (such as bar charts, line charts, pie charts, etc.), and provide an intuitive and easy-to-understand result analysis interface. It is convenient for users to find specific data and improve data access efficiency.

[0166] 4. Set personalized basic operation rules according to the equipment parameters of the "model library" to generate multiple different operation simulation schemes. By combining specified attribute values, arithmetic operators, data values, comparison operators, expected values, etc., multiple schemes can be generated in batches to meet diverse business needs. When the needs change, you only need to adjust the rules to quickly and efficiently regenerate a batch of new schemes.

[0167] 5. Support the operation simulation calculation of one or more schemes, and perform balance analysis calculation through scheduling model calculation tools. Provide visual calculation process monitoring, observe data inflow, processing speed, resource usage, etc., present the calculation iteration process, parameter changes and result generation in real time, and discover and handle exceptions and errors in time.

[0168] 6. Display calculation results in the form of charts and graphs to enhance the readability of data. Different scheme models can be summarized according to regions and specified attributes, and the differences in calculation results of different simulation runs can be intuitively compared to help users make more scientific and reasonable decisions.

[0169] Based on the same inventive concept, the embodiment of the present application also provides a power grid operation simulation data management device for implementing the power grid operation simulation data management method involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in the power grid operation simulation data management device embodiment provided below can refer to the limitations of the power grid operation simulation data management method above, and will not be repeated here.

[0170] This embodiment provides a power grid operation simulation data management device, including:

[0171] A data acquisition module is used to acquire and classify basic data of power grid operation simulation through a pre-built model library, wherein the model library includes several different types of basic data of power grid operation simulation;

[0172] The first verification module is used to perform data format verification on the acquired power grid operation simulation basic data, and record error feedback information for the data that fails the format verification;

[0173] The second verification module is used to perform data relationship verification on the basic data of power grid operation simulation that has passed the format verification, and record error feedback information for the data that has not passed the relationship verification. The data relationship verification at least includes the association relationship verification and the value relationship verification between the classified data;

[0174] A data repair module is used to repair relevant data in the model library according to error feedback information, and store all data that have passed verification and repair in the model library;

[0175] A simulation calculation module is used to generate the required operation simulation calculation scheme by using preset personalized rules, and obtain corresponding data from the model library that has been verified and repaired and perform calculations based on the operation simulation calculation scheme;

[0176] The result analysis module is used to visualize the calculation results of different simulation calculation schemes for the purpose of result analysis.

[0177] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0178] Reference Figure 4 An embodiment of the present invention further provides a computer device, comprising: a memory and a processor and a computer program stored in the memory. When the computer program is executed on the processor, a power grid operation simulation data management method as described in any one of the above methods is implemented.

[0179] The computer device may be a desktop computer, a notebook, a PDA, a cloud server or other computing device. The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that Figure 4 It is only an example of a computer device and does not constitute a limitation of the computer device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0180] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0181] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device. Further, the memory may include both an internal storage unit and an external storage device of the computer device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory may also be used to temporarily store data that has been output or is to be output.

[0182] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for managing power grid operation simulation data as described in any one of the above methods is implemented.

[0183] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, USB flash drive, mobile hard disk, disk or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0184] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0185] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0186] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0187] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for managing power grid operation simulation data, characterized in that: The steps include: Acquiring and classifying basic data for power grid operation simulation through a pre-built model library, wherein the model library includes several different types of basic data for power grid operation simulation; Performing data format verification on the acquired power grid operation simulation basic data, and recording error feedback information for data that fails the format verification; Performing data relationship verification on the power grid operation simulation basic data that has passed the format verification, and recording the error feedback information on the data that has not passed the relationship verification, wherein the data relationship verification at least includes an association relationship verification and a numerical relationship verification between the classified data; Repairing the relevant data in the model library according to the error feedback information, and storing all the data that have passed the verification and repair in the model library; Generate the required operation simulation calculation scheme by using the preset personalized rules, and obtain the corresponding data from the model library that has been verified and repaired and perform calculation based on the operation simulation calculation scheme; The calculation results of different operation simulation calculation schemes are visualized to facilitate result analysis.

2. The power grid operation simulation data management method according to claim 1, characterized in that: The data format verification includes: Traversing the attributes of the basic data of power grid operation simulation in the model library, and performing data verification according to the rules annotated in the attributes, wherein the annotations are preset constraint conditions for different types of data; The information violating the constraint condition is recorded as error information of the corresponding data to form first error feedback information of the basic data of the power grid operation simulation.

3. The power grid operation simulation data management method according to claim 1, characterized in that: The data relationship verification includes: Verifying the power grid operation simulation basic data in the model library according to preset verification rules, wherein the verification rules include data association relationship rules and data value relationship rules; Information that does not conform to the verification rule is recorded as error information of corresponding data to form second error feedback information of the power grid operation simulation basic data.

4. The power grid operation simulation data management method according to claim 3, characterized in that: The data of the model library is divided in sequence according to regional levels. In each minimum regional level, the basic data of the power grid operation simulation is classified according to type nodes to form a data model library with a tree structure.

5. The power grid operation simulation data management method according to claim 4, characterized in that: The data association relationship rule is the master-slave relationship between the various data after the power grid operation simulation basic data is classified.

6. The power grid operation simulation data management method according to claim 4, characterized in that: The data numerical relationship rule is the numerical relationship between each data after the power grid operation simulation basic data is classified.

7. The power grid operation simulation data management method according to claim 1, characterized in that: The personalized rules are basic operation rules set for different types of basic data of power grid operation simulation.

8. A power grid operation simulation data management device, characterized in that: include: A data acquisition module, used to acquire and classify basic data for power grid operation simulation through a pre-built model library, wherein the model library includes several different types of basic data for power grid operation simulation; A first verification module is used to perform data format verification on the acquired power grid operation simulation basic data, and record error feedback information for data that fails the format verification; A second verification module is used to perform data relationship verification on the power grid operation simulation basic data that has passed the format verification, and record the error feedback information for the data that has not passed the relationship verification, wherein the data relationship verification at least includes an association relationship verification and a numerical relationship verification between the classified data; A data repair module, used to repair the relevant data in the model library according to the error feedback information, and store all the data that have passed the verification and repair into the model library; A simulation calculation module, used to generate a required operation simulation calculation scheme using preset personalized rules, and obtain corresponding data from the model library that has been verified and repaired and perform calculations based on the operation simulation calculation scheme; The result analysis module is used to visualize the calculation results of different operation simulation calculation schemes to facilitate result analysis.

9. A computer device, characterized in that: The device comprises a processor and a memory: The memory is used to store a computer program and send instructions of the computer program to the processor; The processor executes a power grid operation simulation data management method according to any one of claims 1 to 7 according to the instructions of the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a method for managing power grid operation simulation data according to any one of claims 1 to 7 is implemented.

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