Power grid section data automatic checking method, system, device and medium

Abstract

The present disclosure relates to a kind of power grid section data automatic checking method, system, equipment and medium, and it is related to electric power technical field.The power grid section data automatic checking method described includes: obtaining the public rule corresponding to multiple set verification rules and the power grid section data to be checked;Based on the public rule corresponding to the multiple set verification rules, the power grid section data to be checked is checked according to rule, and the first power grid section data that does not meet the public rule is obtained;Using the time period and / or equipment corresponding to the first power grid section data, the multiple set verification rules are adjusted, and the public rule corresponding to the multiple set verification rules is updated;Based on the updated public rule, the first power grid section data is checked, and the second power grid section data that does not meet the updated public rule is obtained.The embodiment of the present disclosure can realize the automatic checking of power grid section data.

Description

Technical Field

[0001] This disclosure relates to the field of power grid technology, and in particular to a method and apparatus, electronic device and storage medium for automatic verification of power grid cross-section data. Background Technology

[0002] Operational control sections are the core of power grid monitoring and electricity market clearing. With the gradual expansion of the power grid and its increasingly compact structure, the power grid topology and operating modes are becoming more complex and variable, bringing many challenges to the safe operation of the power system. Improving the intelligence level of section management is of great significance for the safe, economical, and high-quality operation of the power system. Operational control sections are the safety boundaries for the safe and reliable operation of the power grid and the clearing of the electricity market; they change accordingly under different operating modes. With the continuous advancement of new power systems and electricity markets, the power flow pattern of the power grid is no longer fixed in a specific operating domain, making the operating modes of the Southern Power Grid complex and variable, causing the limits of important sections to frequently change with the change in operating modes. The traditional method of manually compiling sections by experts suffers from low intelligence, low efficiency, and high risk, seriously affecting the practicality and accuracy of modules such as intelligent alarms, generation planning, and safety verification. Therefore, it is urgent to improve the level of auxiliary compilation technology for operational control sections.

[0003] The China Southern Power Grid (CSG) Operation Control Section Management System was put into operation in 2019 and is deployed in Security Zone III of the dispatch cloud, meeting the requirements of Level 3 Information Security Protection. To date, no functional modifications have been made to the Operation Control Section Management System since its commissioning. The current functional status of the Operation Control Section Management System is as follows: 1) Standardized section compilation: It possesses standardized compilation capabilities for section formulas, section data entry into the database, and connection to limit orders; 2) Form management of section data: It collects sections reported by various intermediate dispatch centers, forming the CSG section database and intermediate dispatch section database, achieving informatization and standardized management of sections, and sending section data to the Southern Regional Spot System and the CSG Integrated Dispatch Automation System (CSOS).

[0004] Currently, the main problems with the China Southern Power Grid's Operation Control Section Management System are as follows: Problem 1: The current system lacks automatic verification of provincial section data and automatic comparison between the OCS (Optical System Classification) and the Operation Management System (OMS) for power grid dispatching and production operations, resulting in inconsistencies in section data. Problem 2: With the continuous advancement of new power systems and the power market, power flow patterns are no longer fixed in a defined operating domain. Manually compiling sections by experts leads to low efficiency and high operational risks. Summary of the Invention

[0005] This disclosure proposes a method, system, electronic equipment, and storage medium for automatic verification of power grid cross-section data.

[0006] According to one aspect of this disclosure, an automatic verification method for power grid cross-section data is provided, comprising:

[0007] Obtain the common rules corresponding to multiple set verification rules and the power grid section data to be verified;

[0008] Based on the common rules corresponding to the multiple set verification rules, the power grid section data to be verified is verified by rules to obtain the first power grid section data that does not conform to the common rules.

[0009] Using the time period and / or equipment corresponding to the first power grid section data, the plurality of set verification rules are adjusted, and the common rules corresponding to the plurality of set verification rules are updated;

[0010] Based on the updated public rules, the data of the first power grid section is verified to obtain the data of the second power grid section that does not conform to the updated public rules.

[0011] Preferably, the method for determining the common rules corresponding to the multiple set verification rules before obtaining the common rules corresponding to the multiple set verification rules includes:

[0012] Acquire the existing data of power grid cross-section data and its corresponding tags;

[0013] By using the existing data of the power grid section data and its corresponding tags, multiple set verification rules are trained to generate common rules corresponding to the multiple set verification rules.

[0014] Preferably, the method for training multiple predefined verification rules using the existing data of the power grid section data and its corresponding tags to generate common rules corresponding to the multiple predefined verification rules includes:

[0015] Based on multiple defined classification models, the existing data of the power grid cross-section data and their corresponding labels are used to train the multiple defined classification models to obtain common rules corresponding to the multiple defined verification rules; and / or,

[0016] Before training multiple set verification rules using the existing data of the power grid section data and its corresponding labels, semantic recognition is performed on the existing data and / or its corresponding labels to obtain training text features corresponding to the existing data of the power grid section data and / or its corresponding labels; the training text features are used to train multiple set verification rules respectively to generate common rules corresponding to the multiple set verification rules; or, the training text features are used to train multiple set classification models respectively to obtain common rules corresponding to the multiple set verification rules.

[0017] Preferably, the method for performing rule verification on the power grid section data to be verified based on the common rules corresponding to the plurality of set verification rules, to obtain the first power grid section data that does not conform to the common rules, includes:

[0018] Based on the established expert database rules, the public rules corresponding to the power grid section data to be verified are invoked;

[0019] By using the invoked public rules, the power grid section data to be verified is validated according to the rules, and the first power grid section data that does not conform to the public rules is obtained.

[0020] Preferably, the method for performing rule verification on the power grid section data to be verified based on the common rules corresponding to the plurality of set verification rules, and obtaining the first power grid section data that does not conform to the common rules, further includes:

[0021] Semantic recognition is performed on the power grid section data to be verified to obtain the text features to be verified corresponding to the power grid section data to be verified; based on the common rules corresponding to the multiple set verification rules, the text features to be verified are verified by rules to obtain the first power grid section data that does not conform to the common rules.

[0022] Preferably, after semantic recognition is performed on the power grid section data to be verified to obtain the text features corresponding to the power grid section data to be verified, if the text features to be verified do not conform to the set text features, the power grid section data to be verified corresponding to the set text features is determined as invalid data, and the verification of the power grid section data to be verified is stopped.

[0023] Preferably, the method for adjusting the plurality of set verification rules and updating the common rules corresponding to the plurality of set verification rules using the time period and / or equipment corresponding to the first power grid section data includes:

[0024] Based on the time period and / or equipment corresponding to the first power grid section data, the existing data of the power grid section data is adjusted to ensure that the variables corresponding to the first power grid section data and the existing data are consistent; the adjusted existing data and its corresponding labels are used to train multiple set verification rules, and the common rules corresponding to the multiple set verification rules are updated; or,

[0025] Based on the time period and / or equipment corresponding to the first power grid section data, the multiple set verification rules are adjusted to ensure that the multiple set verification rules conform to the first power grid section data; the adjusted existing data and its corresponding tags are used to train the multiple set verification rules, and the common rules corresponding to the multiple set verification rules are updated.

[0026] According to one aspect of this disclosure, an automatic verification system for power grid section data is provided, comprising:

[0027] The acquisition unit is used to acquire the common rules corresponding to multiple set verification rules and the power grid section data to be verified;

[0028] The first verification unit is used to perform rule verification on the power grid section data to be verified based on the common rules corresponding to the multiple set verification rules, and obtain the first power grid section data that does not conform to the common rules.

[0029] The update unit is used to adjust the plurality of set verification rules and update the common rules corresponding to the plurality of set verification rules by using the time period and / or equipment corresponding to the first power grid section data.

[0030] The second verification unit is used to verify the first power grid section data based on the updated public rules, and obtain the second power grid section data that does not conform to the updated public rules.

[0031] According to one aspect of this disclosure, an electronic device is provided, comprising:

[0032] processor;

[0033] Memory used to store processor-executable instructions;

[0034] The processor is configured to execute the above-mentioned automatic verification method for power grid cross-section data.

[0035] According to one aspect of this disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the above-described automatic verification method for power grid cross-section data.

[0036] This disclosure presents an automatic verification method and device for power grid section data, an electronic device, and a storage medium to address the problems of inconsistency in section data due to the lack of automatic verification of provincial and grid section data in the operation control section management system, the automatic comparison of sections between the OCS and the dispatch management system (OMS) for power grid dispatching and production operation, and the low efficiency and high operational risks associated with manually compiling sections by experts.

[0037] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0038] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0040] Figure 1 A flowchart illustrating an automatic verification method for power grid cross-section data according to an embodiment of the present disclosure is shown.

[0041] Figure 2 A block diagram of an automatic verification system for power grid cross-section data according to an embodiment of the present disclosure is shown;

[0042] Figure 3 A block diagram of an electronic device 800 according to an embodiment of the present disclosure is shown;

[0043] Figure 4 A block diagram of an electronic device 1900 according to an embodiment of the present disclosure is shown. Detailed Implementation

[0044] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0045] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0046] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A exists alone, A and B exist simultaneously, and B exists alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0047] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0048] It is understood that the above-mentioned automatic verification methods for power grid section data mentioned in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further.

[0049] In addition, this disclosure also provides an automatic verification system for power grid cross-section data, electronic equipment, computer-readable storage medium, and program, all of which can be used to implement any of the automatic verification methods for power grid cross-section data provided in this disclosure. The corresponding technical solutions and descriptions are described in the relevant section of the method and will not be repeated here.

[0050] Figure 1 A flowchart illustrating an automatic verification method for power grid section data according to an embodiment of this disclosure is shown, such as... Figure 1 As shown, the automatic verification method for power grid cross-section data includes: Step S101: Obtaining common rules corresponding to multiple set verification rules and power grid cross-section data to be verified; Step S102: Performing rule verification on the power grid cross-section data to be verified based on the common rules corresponding to the multiple set verification rules to obtain first power grid cross-section data that does not conform to the common rules; Step S103: Adjusting the multiple set verification rules and updating the common rules corresponding to the multiple set verification rules using the time period and / or equipment corresponding to the first power grid cross-section data; Step S104: Verifying the first power grid cross-section data based on the updated common rules to obtain second power grid cross-section data that does not conform to the updated common rules. This method addresses the lack of automatic verification of provincial cross-section data in the operation control cross-section management system, the inconsistency in cross-section data due to the lack of automatic comparison between the OCS and the dispatch management system (OMS) for power grid dispatching and production operation, and the low efficiency and high operational risks associated with manually compiling cross-sections by experts.

[0051] Step S101: Obtain the common rules corresponding to multiple set verification rules and the power grid section data to be verified.

[0052] In the embodiments of this disclosure and other possible embodiments, the plurality of set verification rules may include one or more of the following: compliance verification, redundancy verification, rationality verification and integrity verification.

[0053] In the embodiments of this disclosure and other possible embodiments, the power grid section data to be verified can be configured as the power grid section data to be verified for each province, which may include: section name, section formula, section description, constraint formula, constraint description and other data, as well as one or more of the equipment data corresponding to the power grid section data to be verified.

[0054] Step S102: Based on the common rules corresponding to the multiple set verification rules, perform rule verification on the power grid section data to be verified to obtain the first power grid section data that does not conform to the common rules.

[0055] In embodiments of this disclosure, a method for determining the common rules corresponding to the multiple set verification rules before obtaining the common rules corresponding to the multiple set verification rules includes: obtaining the existing data (historical data) of power grid cross-section data and its corresponding tags; using the existing data of power grid cross-section data and its corresponding tags to train the multiple set verification rules, thereby generating the common rules (common rule model) corresponding to the multiple set verification rules.

[0056] In the embodiments of this disclosure and other possible embodiments, based on existing compliance checks, redundancy checks, reasonableness checks, integrity checks, and other set verification rules, the existing data of power grid cross-section data is used to train the set verification rules, generating common rules (common rule models) corresponding to the multiple set verification rules; wherein, the existing data is data that has been manually indexed (tagged) and verified, including data that conforms to the set verification rules or common rules and data that does not conform to the set verification rules or common rules. The trained verification rules can be further manually verified to generate common rules (common rule models) corresponding to the multiple set verification rules.

[0057] In the embodiments of this disclosure and other possible embodiments, the quality of the power grid section data to be verified is checked and verified, which involves one or more of the following: compliance verification, redundancy verification, rationality verification, integrity verification, and rule configuration. Remote real-time power grid section data transmission is achieved through a wide-area communication bus, enabling quality checks and content comparisons of the power grid section data to be verified by executing common rules, thus ensuring data accuracy.

[0058] In the embodiments of this disclosure and other possible embodiments, compliance verification involves verifying whether the ID encoding and owner of the power grid section data to be verified meet the requirements of the section naming specification and encoding generation specification.

[0059] In the embodiments of this disclosure and other possible embodiments, redundancy verification is performed by checking the name duplication of the power grid section data to be verified and its corresponding equipment data, checking whether the data names are similar and whether they are likely to be duplicate data. The equipment in the station is verified according to multiple attributes such as name, voltage level, and the station to which it belongs.

[0060] In the embodiments of this disclosure and other possible embodiments, the rationality verification is as follows: the reasonableness of the parameter filling range corresponding to the power grid section data to be verified is verified, relevant parameter thresholds are set according to the object type and characteristics, and a reminder is given when the filled content exceeds the threshold, combined with power professional knowledge and equipment attribute information.

[0061] In the embodiments of this disclosure and other possible embodiments, integrity verification is performed by verifying the integrity of the filled-in power grid section data to be verified, according to the standard section information, and by verifying the integrity of the required and optional fields.

[0062] In embodiments of this disclosure, the method for training multiple set verification rules using existing power grid section data and their corresponding labels to generate common rules corresponding to the multiple set verification rules includes: training the multiple set classification models using existing power grid section data and their corresponding labels respectively, based on multiple set classification models, to obtain common rules (common rule models) corresponding to the multiple set verification rules; and / or, before training the multiple set verification rules using existing power grid section data and their corresponding labels, performing semantic recognition on the existing data and / or their corresponding labels to obtain training text features corresponding to the existing power grid section data and / or their corresponding labels; training the multiple set verification rules using the training text features respectively, to generate common rules corresponding to the multiple set verification rules; or, training the multiple set classification models using the training text features respectively, to obtain common rules (common rule models) corresponding to the multiple set verification rules.

[0063] In embodiments of this disclosure and other possible embodiments, the plurality of defined classification models can be configured as machine learning classification models, including one or more of support vector machines, decision trees, random forests, K-nearest neighbors, logistic regression, adaptive augmentation, linear discriminant analysis, and multilayer perceptrons.

[0064] For example, the Support Vector Machine (SVM) aims to find the separating hyperplane that correctly partitions the training dataset and maximizes the geometric margin. For a given dataset T and a hyperplane ω·x+b=0, the geometric margin of the hyperplane with respect to the sample points is defined as shown in Equation (1).

[0065]

[0066] Wherein, the sample points are represented as (x i ,y i ), i∈[1,l]. For linearly separable datasets, the binary classification problem can be transformed into a constrained minimization problem for optimization. After introducing the Agrange multiplier α, the optimization problem can be expressed as equation (2) and is subordinate to equation (3).

[0067]

[0068]

[0069] Therefore, the decision function of the SVM model is defined as Equation (4).

[0070]

[0071] In the SVM model, data can be separated linearly or nonlinearly at higher dimensions, which requires the introduction of a kernel function. Therefore, the basic form of the SVM model can be expressed as equation (5).

[0072]

[0073] Where K(x,x) i ) represents a kernel function. Commonly used kernel functions include linear kernel functions, polynomial kernel functions, and Gaussian kernel functions.

[0074] (a) Linear kernel function K(x,x) i )=x T x i It does not include additional parameters, has the characteristics of fast calculation speed, and has a good classification effect on linearly separable data.

[0075] (b) Polynomial kernel function K(x,x) i )=[a(x T x i )+b] d Parameter 'a' is used to scale the inner product, parameter 'b' is a constant term, and parameter 'd' represents the dimension. Due to the effect of parameter 'd', the polynomial function can map the input space of the data to a higher-dimensional feature space, thus having a wider range of applications. However, as parameter 'd' increases and the dimension rises, the computational cost becomes increasingly large, and the learning complexity increases accordingly, making overfitting more likely.

[0076] The mathematical expression for the Gaussian kernel function is shown in (6). The Gaussian kernel function is a function representing the Euclidean distance coefficient between two vectors. It can map the data input space of the preferred dimension to a higher-dimensional feature space through Taylor expansion, and it has strong locality (determined by σ). In addition, due to the small number of parameters, the computational cost is small. The Gaussian kernel function is currently the most widely used of the three types of kernel functions. When the kernel function cannot be determined, the Gaussian kernel function is the preferred choice.

[0077]

[0078] In an embodiment of this disclosure, the method of performing rule verification on the power grid section data to be verified based on the common rules corresponding to the plurality of set verification rules to obtain first power grid section data that does not conform to the common rules includes: calling the common rules (common rule model) corresponding to the power grid section data to be verified based on the set expert database rules; and performing rule verification on the power grid section data to be verified using the called common rules (common rule model) to obtain first power grid section data that does not conform to the common rules.

[0079] In the embodiments of this disclosure and other possible embodiments, the configuration of the expert database rules is one or more of a rule-based reasoning (RBR) expert system (expert model) or a case-based reasoning (CBR) expert system (expert model). Based on the power grid section data to be verified, the common rules (common rule model) corresponding to the power grid section data to be verified are called; using the called common rules (common rule model), the power grid section data to be verified is validated to obtain first power grid section data that does not conform to the common rules.

[0080] Specifically, in the embodiments of this disclosure and other possible embodiments, the method of calling the common rules (common rule model) corresponding to the power grid section data to be verified based on the set expert database rules includes: obtaining the common rules (common rule model) corresponding to the plurality of set verification rules; prioritizing the common rules (common rule model) corresponding to the plurality of set verification rules according to the set expert database rules; and sequentially calling the common rules (common rule model) corresponding to the power grid section data to be verified according to the priority-ordered common rules based on the set expert database rules.

[0081] More specifically, in the embodiments and other possible embodiments of this disclosure, the method for prioritizing the common rules (common rule models) corresponding to the plurality of setting expert database rules includes: training the setting expert database rules (setting expert database rule models) using the existing data of the power grid section data and its corresponding configuration tags; calculating the probability values ​​of the common rules (common rule models) corresponding to the plurality of setting verification rules of the power grid section data to be verified based on the trained setting expert database rules (setting expert database rule models); and prioritizing the common rules (common rule models) corresponding to the plurality of setting verification rules according to the magnitude of the probability values ​​of the common rules (common rule models) corresponding to the plurality of setting verification rules. The configuration tags are configured as one or more of the plurality of setting verification rules.

[0082] For example, in the embodiments of this disclosure and other possible embodiments, the priority order (from high to low) of the public rules corresponding to the plurality of set verification rules is as follows: public rules (public rule model) corresponding to compliance verification, integrity verification, redundancy verification and reasonableness verification.

[0083] In embodiments of this disclosure, the method of performing rule verification on the power grid section data to be verified based on the common rules corresponding to the plurality of set verification rules to obtain first power grid section data that does not conform to the common rules further includes: performing semantic recognition on the power grid section data to be verified to obtain the text features to be verified corresponding to the power grid section data to be verified; and performing rule verification on the text features to be verified based on the common rules corresponding to the plurality of set verification rules to obtain first power grid section data that does not conform to the common rules.

[0084] Furthermore, in the embodiments of this disclosure and other possible embodiments, before training multiple set verification rules using the existing data of the power grid cross-section data and its corresponding labels, semantic recognition is performed on the existing data and / or its corresponding labels to obtain training text features corresponding to the existing data of the power grid cross-section data and / or its corresponding labels; the training text features are used to train multiple set verification rules respectively to generate common rules corresponding to the multiple set verification rules; or, the training text features are used to train multiple set classification models respectively to obtain common rules (common rule models) corresponding to the multiple set verification rules. Also, the method for training the set expert database rules (set expert database rule model) using the existing data of the power grid cross-section data and its corresponding configuration labels further includes: performing semantic recognition on the existing data and / or its corresponding labels to obtain training text features corresponding to the existing data of the power grid cross-section data and / or its corresponding labels; and training the set expert database rules (set expert database rule model) using the training text features.

[0085] In the embodiments of this disclosure and other possible embodiments, a semantic recognition system (NER) based on the LSTM (Long Short-Term Memory) algorithm can be used to perform semantic recognition on the above-mentioned stock data and / or its corresponding tags, or on the power grid section data to be verified.

[0086] In the embodiments of this disclosure, after semantic recognition is performed on the power grid section data to be verified to obtain the text features corresponding to the power grid section data to be verified, if the text features to be verified do not conform to the set text features, the power grid section data to be verified that does not conform to the set text features is determined as invalid data, and the verification of the power grid section data to be verified is stopped.

[0087] For example, if the semantically identified text feature corresponding to the grid section data to be verified lacks the ID code in the set text feature, the grid section data to be verified is directly determined to be invalid data, and the verification of the grid section data to be verified is stopped. As another example, if the data type of the text feature corresponding to the grid section data to be verified does not match the set data type in the set text feature and / or the data range exceeds the set threshold, a targeted reminder is directly issued for the grid section data to be verified, and the grid section data to be verified corresponding to the data type that does not match the set data type and / or the data range that exceeds the set threshold is deleted.

[0088] In the embodiments of this disclosure and other possible embodiments, if the data type of the text feature corresponding to the grid section data to be verified, as identified by semantic recognition, does not match the set data type in the set text feature and / or the data range exceeds the set threshold, the grid section data to be verified that does not match the set data type is deleted; if the grid section data to be verified matches the set data type, the data exceeding the set threshold is interpolated using the existing data of the grid section data and its corresponding tags.

[0089] Step S103: Using the time period and / or equipment corresponding to the first power grid section data, adjust the multiple set verification rules and update the common rules corresponding to the multiple set verification rules.

[0090] In embodiments of this disclosure, the method of adjusting the plurality of set verification rules and updating the common rules corresponding to the plurality of set verification rules using the time period and / or equipment corresponding to the first power grid section data includes: adjusting the existing data of the power grid section data based on the time period and / or equipment corresponding to the first power grid section data to ensure that the variables corresponding to the first power grid section data and the existing data are consistent; training the plurality of set verification rules using the adjusted existing data and its corresponding labels to update the common rules (common rule model) corresponding to the plurality of set verification rules; or, adjusting the plurality of set verification rules based on the time period and / or equipment corresponding to the first power grid section data to ensure that the plurality of set verification rules conform to the first power grid section data; training the adjusted plurality of set verification rules using the adjusted existing data and its corresponding labels to update the common rules (common rule model) corresponding to the plurality of set verification rules.

[0091] In embodiments of this disclosure and other possible embodiments, the method for adjusting the existing data of the power grid section data based on the time period and / or device corresponding to the first power grid section data includes: obtaining the time period and / or device corresponding to the first power grid section data; determining the existing data of the power grid section data corresponding to the time period and / or device based on the time period and / or device; selecting the existing data of the power grid section data corresponding to the time period and / or device based on the obtained content information corresponding to the first power grid section data to obtain the adjusted final existing data.

[0092] For example, if the time period is configured as 10:00 AM to 12:00 PM, then the existing data of the power grid section data is adjusted, retaining only the existing data of the power grid section data corresponding to 10:00 AM to 12:00 PM. As another example, if the equipment is configured as a transformer, then the existing data of the power grid section data corresponding to the transformer is determined.

[0093] In embodiments of this disclosure and other possible embodiments, the method for selecting the existing data of the network section data corresponding to the time period and / or equipment based on the content information corresponding to the acquired first power grid section data to obtain the adjusted final existing data includes: if the existing data of the network section data corresponding to the time period and / or equipment contains the content information, then retain the existing data corresponding to the content information; otherwise, delete the corresponding existing data to ensure that the variables corresponding to the first power grid section data and the existing data are consistent. The content information corresponding to the first power grid section data may include one or more of the following: section name, section formula, section description, constraint formula, constraint description, etc.

[0094] In the embodiments of this disclosure and other possible embodiments, the method of adjusting the plurality of set verification rules based on the time period and / or device corresponding to the first power grid section data to ensure that the plurality of set verification rules conform to the first power grid section data includes: deleting or modifying a certain rule among the plurality of set verification rules based on the time period and / or device corresponding to the first power grid section data to ensure that the plurality of set verification rules conform to the first power grid section data.

[0095] For example, in the embodiments of this disclosure and other possible embodiments, if the time period is configured as 10:00 AM to 12:00 PM, then the existing data of the power grid section data is adjusted, retaining only the existing data of the power grid section data corresponding to 10:00 AM to 12:00 PM; or, if the device is configured as a transformer, then the existing data of the power grid section data corresponding to the transformer is determined; in this case, redundant checks in the multiple set verification rules can be deleted. As another example, in this case, the required and optional fields for integrity checks in the multiple set verification rules are modified.

[0096] In embodiments of this disclosure and other possible embodiments, the method of training the adjusted multiple set verification rules using the adjusted existing data (historical data) and their corresponding labels, and updating the common rules (common rule model) corresponding to the multiple set verification rules, includes:

[0097] In embodiments of this disclosure and other possible embodiments, the method of training the adjusted multiple set verification rules using the adjusted existing data (historical data) and their corresponding labels, and updating the common rules (common rule models) corresponding to the multiple set verification rules, includes: training the multiple set classification models using the adjusted existing data (historical data) and their corresponding labels respectively, based on multiple set classification models, to obtain updated common rules (common rule models) corresponding to the adjusted multiple set verification rules; and / or, before training the multiple set verification rules using the adjusted existing data (historical data) and their corresponding labels, performing semantic recognition on the adjusted existing data (historical data) and their corresponding labels to obtain training text features of the adjusted existing data (historical data) and their corresponding labels; training the multiple set verification rules using the training text features respectively, and updating the common rules corresponding to the multiple set verification rules; or, training the multiple set classification models using the training text features respectively, to obtain updated common rules (common rule models) corresponding to the adjusted multiple set verification rules.

[0098] Step S104: Based on the updated public rules, verify the first power grid section data to obtain the second power grid section data that does not conform to the updated public rules.

[0099] In the embodiments of this disclosure and other possible embodiments, the method of verifying the first power grid section data based on the updated public rules to obtain the second power grid section data that does not conform to the updated public rules includes: calling the public rules corresponding to the first power grid section data based on the set expert database rules;

[0100] By using the invoked public rules, the data of the first power grid section is validated to obtain the data of the second power grid section that does not conform to the public rules.

[0101] Specifically, in the embodiments of this disclosure and other possible embodiments, the method of calling the public rules (public rule model) corresponding to the first power grid section data based on the set expert database rules includes: obtaining the updated public rules (updated public rule model) corresponding to the plurality of set verification rules; prioritizing the updated public rules (updated public rule model) corresponding to the plurality of set verification rules according to the set expert database rules; and sequentially calling the updated public rules (updated public rule model) corresponding to the first power grid section data according to the updated public rules prioritized by the set expert database rules.

[0102] More specifically, in the embodiments and other possible embodiments of this disclosure, the method for prioritizing the updated common rules (updated common rule models) corresponding to the plurality of set verification rules by setting expert library rules includes: training the set expert library rules (set expert library rule models) using the adjusted or final stock data of the power grid section data and its corresponding configuration tags; calculating the probability values ​​of the updated common rules (updated common rule models) corresponding to the plurality of set verification rules of the first power grid section data based on the trained set expert library rules (set expert library rule models); and prioritizing the updated common rules (updated common rule models) corresponding to the plurality of set verification rules according to the magnitude of the probability values ​​of the updated common rules (updated common rule models) corresponding to the plurality of set verification rules. Wherein, the configuration tags are configured as one or more of the plurality of set verification rules.

[0103] In the embodiments of this disclosure, after semantic recognition is performed on the first power grid section data to obtain the text features to be verified corresponding to the first power grid section data, if the text features to be verified do not conform to the set text features, the first power grid section data that does not conform to the set text features is determined as invalid data, and the verification of the first power grid section data is stopped.

[0104] For example, if the text feature to be verified corresponding to the first power grid section data identified by semantic recognition lacks the ID code in the set text feature, then the power grid section data to be verified is directly determined to be invalid data, and the verification of the power grid section data to be verified is stopped. As another example, if the data type of the text feature to be verified corresponding to the first power grid section data identified by semantic recognition does not match the set data type in the set text feature and / or the data range exceeds the set threshold, then a targeted reminder is directly given to the first power grid section data, and the first power grid section data that does not match the set data type and / or the data range exceeds the set threshold is deleted.

[0105] In the embodiments of this disclosure and other possible embodiments, if the data type of the text feature to be verified corresponding to the semantically identified first power grid section data does not match the set data type in the set text feature and / or the data range exceeds the set threshold, the first power grid section data that does not match the set data type is deleted; if the first power grid section data matches the set data type, the data exceeding the set threshold is interpolated using the existing data of the power grid section data and its corresponding tags.

[0106] In the embodiments of this disclosure and other possible embodiments, the verification result data of the second power grid section data that fails the secondary verification is output as verification result data. The verification result data includes the section name, section formula, section description, and name of any non-compliance rule. Finally, the verification result data is encapsulated as a service, meaning that a section verification service is provided externally. Applications such as the OMS system call the section verification service through the service bus and return the verification result based on the section data.

[0107] Based on the previous construction achievements of the Southern Power Grid General Dispatch Operation Control Section Management System, functions were added according to the current situation and related business needs to achieve: (1) Improve the quality of section data, ensure data consistency and improve the quality of section data by automatically verifying the section data of the grid and provinces, and ensure the accuracy of spot market clearing; (2) Monitor the section status in an auxiliary way, realize automatic monitoring of the power grid section, supervise the safety of section operation according to the expert database rules, automatically compare and analyze the section operation data, and give prompts for abnormal data.

[0108] The automatic verification method for power grid cross-section data can be executed by a power grid cross-section data automatic verification device / system. For example, the automatic verification method can be executed by terminal equipment, servers, or other processing equipment. The terminal equipment can be user equipment (UE), mobile devices, user terminals, terminals, cellular phones, cordless phones, personal digital assistants (PDAs), handheld devices, computing devices, vehicle-mounted devices, wearable devices, etc. In some possible implementations, the automatic verification method for power grid cross-section data can be implemented by a processor calling computer-readable instructions stored in memory. This addresses the problems of inconsistencies in cross-section data due to the lack of automatic verification of provincial cross-section data in the operation control cross-section management system, the lack of automatic comparison between cross-sections in the OCS and the dispatch management system (OMS) for power grid dispatching and production operation, and the low efficiency and high operational risks associated with manually compiling cross-sections by experts.

[0109] Those skilled in the art will understand that in the above-described automatic verification method for power grid cross-section data in specific embodiments, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0110] Figure 2 A block diagram of an automatic verification system for power grid section data according to an embodiment of the present disclosure is shown, such as... Figure 2 As shown, the automatic verification system for power grid cross-section data includes: an acquisition unit 101, used to acquire common rules corresponding to multiple set verification rules and power grid cross-section data to be verified; a first verification unit 102, used to perform rule verification on the power grid cross-section data to be verified based on the common rules corresponding to the multiple set verification rules, to obtain first power grid cross-section data that does not conform to the common rules; an update unit 103, used to adjust the multiple set verification rules and update the common rules corresponding to the multiple set verification rules using the time period and / or equipment corresponding to the first power grid cross-section data; and a second verification unit 104, used to verify the first power grid cross-section data based on the updated common rules, to obtain second power grid cross-section data that does not conform to the updated common rules. This system addresses the problems of inconsistencies in cross-section data due to the lack of automatic verification of provincial cross-section data in the operation control cross-section management system, the lack of automatic comparison between cross-sections in the OCS and the dispatch management system (OMS) for power grid dispatching and production operation, and the low efficiency and high operational risks associated with manually compiling cross-sections by experts.

[0111] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to execute the automatic verification method for power grid cross-section data described in the above method embodiments. The specific implementation can be referred to the description of the automatic verification method for power grid cross-section data in the above embodiments, and will not be repeated here for the sake of brevity.

[0112] This disclosure also proposes a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the aforementioned automatic verification method for power grid section data. The computer-readable storage medium can be a non-volatile computer-readable storage medium. This addresses the problems of inconsistencies in section data arising from the lack of automatic verification of provincial and grid section data in the Operation Control Section Management System (OCS) and the Automatic Comparison of Sections between the OCS and the Operation Management System (OMS) for Power Grid Dispatch and Production Operation, as well as the low efficiency and high operational risks associated with manually compiling sections by experts.

[0113] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured for the aforementioned automatic verification method for power grid section data. The electronic device can be provided as a terminal, server, or other form of device. This addresses the problems of inconsistencies in section data due to the lack of automatic verification of provincial section data in the operation control section management system, the lack of automatic comparison between OCS and the power grid dispatching and production operation management system (OMS), and the low efficiency and high operational risks associated with manually compiling sections by experts.

[0114] Figure 3 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, or other terminal.

[0115] Reference Figure 3 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0116] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the aforementioned automatic power grid cross-section data verification method. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0117] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of such data include instructions for any application or automatic verification method for power grid cross-section data used on electronic device 800, contact data, phone book data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0118] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0119] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0120] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0121] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0122] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 can detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0123] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0124] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0125] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions, which can be executed by a processor 820 of an electronic device 800 to complete the above-described automatic verification method for power grid cross-section data.

[0126] Figure 4 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. For example, the electronic device 1900 may be provided as a server. (Refer to...) Figure 4 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the aforementioned automatic verification method for power grid cross-section data.

[0127] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0128] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to complete the above-described automatic verification method for power grid cross-section data.

[0129] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0130] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0131] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0132] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0133] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0134] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0135] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0136] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0137] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. An automatic verification method for power grid cross-section data, characterized in that, include: Obtain common rules corresponding to multiple set verification rules and power grid section data to be verified; wherein, the common rules corresponding to the multiple set verification rules are configured as any of the following: compliance verification, redundancy verification, rationality verification and integrity verification; Based on the common rules corresponding to the multiple set verification rules, rule verification is performed on the power grid section data to be verified to obtain first power grid section data that does not conform to the common rules. This includes: prioritizing the common rules corresponding to the multiple set verification rules using set expert library rules; sequentially calling the common rules corresponding to the power grid section data to be verified according to the priority-ranked common rules; and performing rule verification on the power grid section data to be verified using the called common rules to obtain first power grid section data that does not conform to the common rules. Using the time period and / or equipment corresponding to the first power grid section data, the multiple set verification rules are adjusted, and the common rules corresponding to the multiple set verification rules are updated. This includes: adjusting the existing data corresponding to the power grid section data that has been manually indexed and verified based on the time period and / or equipment corresponding to the first power grid section data, so as to ensure that the variables corresponding to the first power grid section data and the existing data are consistent; using the adjusted existing data and its corresponding tags to train the multiple set verification rules, so as to adjust and update the common rules corresponding to the multiple set verification rules. Based on the updated public rules, the data of the first power grid section is verified to obtain the data of the second power grid section that does not conform to the updated public rules.

2. The automatic verification method for power grid section data according to claim 1, characterized in that, Before obtaining the common rules corresponding to the multiple set verification rules, the common rules corresponding to the multiple set verification rules are determined, including: Acquire the existing data of power grid cross-section data and its corresponding tags; By using the existing data of the power grid section data and its corresponding tags, multiple set verification rules are trained to generate common rules corresponding to the multiple set verification rules.

3. The automatic verification method for power grid section data according to claim 2, characterized in that, The process involves training multiple predefined verification rules using existing power grid section data and their corresponding tags to generate common rules corresponding to these predefined verification rules, including: Based on multiple defined classification models, the existing data of the power grid section data and their corresponding labels are used to train the multiple defined classification models to obtain the common rules corresponding to the multiple defined verification rules.

4. The automatic verification method for power grid cross-section data according to claim 3, characterized in that, Before training multiple set verification rules on the existing data of the power grid section data and its corresponding label pairs, the following steps are included: Semantic recognition is performed on the existing data and / or its corresponding tags to obtain the training text features corresponding to the existing data of the power grid section data and / or its corresponding tags. The training text features are used to train multiple set verification rules to generate common rules corresponding to the multiple set verification rules; or, the training text features are used to train multiple set classification models to obtain common rules corresponding to the multiple set verification rules.

5. The automatic verification method for power grid cross-section data according to any one of claims 1-4, characterized in that, The step of performing rule verification on the power grid section data to be verified based on the common rules corresponding to the multiple set verification rules, and obtaining first power grid section data that does not conform to the common rules, further includes: Semantic recognition is performed on the power grid section data to be verified to obtain the text features to be verified corresponding to the power grid section data to be verified; Based on the common rules corresponding to the multiple set verification rules, the features of the text to be verified are verified by rules to obtain the first power grid section data that does not conform to the common rules.

6. The automatic verification method for power grid cross-section data according to claim 5, characterized in that, After performing semantic recognition on the power grid section data to be verified to obtain the text features corresponding to the power grid section data to be verified, if the text features to be verified do not conform to the set text features, the power grid section data to be verified corresponding to the set text features is determined as invalid data, and the verification of the power grid section data to be verified is stopped.

7. An automatic verification system for power grid cross-section data, characterized in that, include: The acquisition unit is used to acquire common rules corresponding to multiple set verification rules and power grid section data to be verified; wherein, the common rules corresponding to the multiple set verification rules are configured to be any of the following: compliance verification, redundancy verification, rationality verification and integrity verification; The first verification unit is used to perform rule verification on the power grid section data to be verified based on the common rules corresponding to the plurality of set verification rules, and to obtain first power grid section data that does not conform to the common rules. The verification unit includes: prioritizing the common rules corresponding to the plurality of set verification rules using set expert library rules; sequentially calling the common rules corresponding to the power grid section data to be verified according to the priority-sorted common rules; and performing rule verification on the power grid section data to be verified using the called common rules to obtain first power grid section data that does not conform to the common rules. The update unit is configured to adjust the plurality of set verification rules and update the common rules corresponding to the plurality of set verification rules using the time period and / or equipment corresponding to the first power grid section data, including: adjusting the existing data corresponding to the power grid section data that has been manually indexed and verified based on the time period and / or equipment corresponding to the first power grid section data to ensure that the variables corresponding to the first power grid section data and the existing data are consistent; and training the plurality of set verification rules using the adjusted existing data and its corresponding tags to adjust and update the common rules corresponding to the plurality of set verification rules. The second verification unit is used to verify the first power grid section data based on the updated public rules, and obtain the second power grid section data that does not conform to the updated public rules.

8. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the automatic verification method for power grid cross-section data according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the automatic verification method for power grid cross-section data as described in any one of claims 1 to 6.

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