Power grid dispatching anti-error method and system

Abstract

The invention discloses a power grid dispatching anti-error method and system. The method comprises the steps that a first classification standard is preset, and real-time category division is carried out in combination with target power grid dispatching real-time operation; presetting a second matching degree judgment standard, and performing matching degree judgment in combination with target power grid dispatching real-time operation; performing third risk level assessment according to a real-time category division result and a matching degree judgment result; and generating a corresponding anti-error strategy according to a result of the third risk level assessment. The method can effectively improve the accuracy and safety of power grid dispatching. Through real-time category division and matching degree judgment, potential risks in power grid dispatching can be quickly identified, and a corresponding anti-error strategy is formulated according to a risk level assessment result.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching error prevention, and in particular to a power grid dispatching error prevention method and system. Background Art

[0002] With the continuous development of power systems and the expansion of power grids, the complexity and importance of power grid dispatching have gradually increased. As the core link to ensure the safe and stable operation of the power system, power grid dispatching is responsible for monitoring, managing and dispatching power grid equipment and power load distribution. In the actual operation process, dispatchers need to make complex operational decisions based on the real-time status of the power grid and operating procedures.

[0003] The existing technology uses static topology models and depth-first search algorithms to prevent misjudgment of power grid dispatching operations, but its main disadvantage is that the model update is lagging, it is difficult to reflect the dynamic changes of power grid operation in real time, and the ability to identify the risk of misoperation of operation behavior in complex scenarios is weak. In addition, the existing method only relies on the device status and topological relationship for judgment, and cannot effectively process multi-dimensional information, resulting in a relatively single execution method of the anti-misoperation strategy, lacking the flexible adjustment and processing capabilities for different risk levels, so it is difficult to cope with the changing dispatching scenarios in practical applications, and the anti-misoperation effect still has a lot of room for improvement. In addition, the construction of the rule base in some existing technologies depends on natural language processing and the conversion of special languages, and there are problems with insufficient semantic recognition and conversion accuracy, especially in complex sentences and polysemous word scenarios, which are prone to understanding deviations. Secondly, the construction of the rule base depends on the close cooperation of domain experts and programmers, and there are still problems such as long development cycle and complex debugging process, and it is impossible to achieve rapid iterative updates of the rule base. Finally, the system's anti-misoperation recognition ability is more dependent on the coverage of existing rules, and has poor adaptability to new rules or scenarios that appear in actual dispatching, making it difficult to cope with the ever-changing risks and new misoperation scenarios in power grid dispatching operations. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method and system for preventing errors in power grid dispatching, which can solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for preventing errors in power grid dispatching, comprising:

[0009] A first classification standard is preset, and real-time classification is performed in combination with the real-time operation of the target power grid dispatching;

[0010] A second matching judgment standard is preset, and matching judgment is performed in combination with the real-time operation of the target power grid dispatch;

[0011] Performing a third risk level assessment based on the result of the real-time classification and the result of the matching degree judgment;

[0012] A corresponding error prevention strategy is generated according to the result of the third risk level assessment.

[0013] As a preferred solution of the method for preventing errors in power grid dispatching according to the present invention, the preset first classification standard, combined with the real-time classification of the target power grid dispatching real-time operation, includes:

[0014] Acquire historical operation characteristics of the target power grid dispatch, and classify the dispatch operations into several different categories based on the historical operation characteristics of the target power grid dispatch;

[0015] The plurality of different categories of the scheduling operations and the corresponding scheduling operation characteristics constitute the first classification standard;

[0016] Feature extraction is performed on the target power grid dispatching real-time operation, and classification is performed according to the extracted features.

[0017] As a preferred solution of the power grid dispatch error prevention method of the present invention, wherein: the third risk level assessment is an arbitrary level assessment standard related to the operation category after real-time category division and the matching degree judgment result;

[0018] The number of levels of the level evaluation standard is determined by the number of operation categories and matching degree judgment results.

[0019] As a preferred solution of the power grid dispatch error prevention method of the present invention, the method of obtaining the historical operation characteristics of the target power grid dispatch and classifying the dispatch operations based on the historical operation characteristics of the target power grid dispatch, and classifying the dispatch operations into several different categories includes:

[0020] The target power grid dispatching historical operation characteristics at least include operation type, equipment status and operation time sequence;

[0021] The several different categories include at least a normal operation category, a dangerous operation category and an abnormal operation category.

[0022] As a preferred solution of the power grid dispatching error prevention method described in the present invention, the matching degree judgment result at least includes path deviation, abnormal state or operation behavior that does not comply with safety regulations.

[0023] As a preferred scheme of the power grid dispatching error prevention method described in the present invention, wherein: the preset first classification standard, combined with the target power grid dispatching real-time operation to perform real-time category division includes: calculating the membership value of the corresponding target power grid dispatching real-time operation for several different categories of dispatching operations, and taking the dispatching operation category corresponding to the maximum membership value as the real-time category division result.

[0024] As a preferred solution of the power grid dispatching error prevention method described in the present invention, wherein: generating a corresponding error prevention strategy based on the result of the third risk level assessment includes: the number of the error prevention strategies is not greater than the number of the results of the third risk level assessment.

[0025] In a second aspect, the present invention provides a power grid dispatch error prevention system, comprising:

[0026] A classification module, used to preset a first classification standard and perform real-time classification in combination with the real-time operation of the target power grid dispatch;

[0027] A judgment module, used to preset a second matching judgment standard and perform matching judgment in combination with the real-time operation of the target power grid dispatch;

[0028] An evaluation module, configured to perform a third risk level evaluation based on the result of the real-time classification and the result of the matching degree judgment;

[0029] A strategy generation module is used to generate a corresponding error prevention strategy according to the result of the third risk level assessment.

[0030] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0031] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0032] Compared with the prior art, the invention has the following beneficial effects: the invention proposes a method and system for preventing errors in power grid dispatching, wherein a first classification standard is preset, and real-time classification is performed in combination with the real-time operation of the target power grid dispatching; a second matching judgment standard is preset, and matching judgment is performed in combination with the real-time operation of the target power grid dispatching; a third risk level assessment is performed according to the result of the real-time classification and the result of the matching judgment; and a corresponding error prevention strategy is generated according to the result of the third risk level assessment. The method can effectively improve the accuracy and safety of power grid dispatching. Through real-time classification and matching judgment, potential risks in power grid dispatching can be quickly identified, and corresponding error prevention strategies can be formulated according to the risk level assessment results. The classification standard and matching judgment standard can also be dynamically adjusted according to the real-time data of power grid dispatching, so as to adapt to the changing risks and new error operation scenarios in power grid dispatching operations. This adaptive capability makes the method more flexible and adaptable in practical applications. At the same time, since the method reduces the dependence on domain experts and programmers, shortens the development cycle, simplifies the debugging process, and enables the rule base to be quickly iterated and updated, thereby improving the overall efficiency and effect of the power grid dispatching error prevention system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 creative labor. Among them:

[0034] Figure 1 A method flow chart of a power grid dispatching error prevention method and system provided by an embodiment of the present invention;

[0035] Figure 2 A schematic diagram of a system design of a power grid dispatching error prevention method and system provided by an embodiment of the present invention;

[0036] Figure 3 An internal structural diagram of a computer device of a power grid dispatching error prevention method and system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0038] Example 1

[0039] Reference Figure 1-Figure 3 , which is the first embodiment of the present invention, and provides a method and system for preventing errors in power grid dispatching, including:

[0040] There are some problems and deficiencies in the existing related technologies. For example, during the grid dispatching process, due to operator errors or insufficient understanding of the grid status, misoperation may occur, thus causing grid accidents.

[0041] The present application provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to explain in detail how to implement the power grid dispatch error prevention method;

[0042] Figure 1 A method flow chart of a power grid dispatch error prevention method and system is shown, including:

[0043] S101, presetting a first classification standard, and performing real-time classification in combination with the real-time operation of the target power grid dispatch;

[0044] In the embodiment of the present application, the first classification standard is preset, and the real-time classification is performed in combination with the real-time operation of the target power grid dispatching, including:

[0045] Obtaining historical operation characteristics of the target power grid dispatching, and classifying the dispatching operations into several different categories based on the historical operation characteristics of the target power grid dispatching;

[0046] Several different categories of scheduling operations and corresponding scheduling operation characteristics constitute a first classification standard;

[0047] The real-time operation of the target power grid dispatching is subjected to feature extraction and the categories are divided according to the extracted features.

[0048] In the embodiment of the present application, the historical operation characteristics of the target power grid dispatch are obtained, and the classification is performed based on the historical operation characteristics of the target power grid dispatch, and the dispatch operation is divided into several different categories including:

[0049] The historical operation characteristics of the target power grid dispatching include at least the operation type, equipment status and operation time sequence;

[0050] The several different categories include at least a normal operation category, a dangerous operation category, and an abnormal operation category.

[0051] In an optional embodiment, the target power grid dispatch historical operation characteristics may include other characteristics in addition to the operation type, equipment status and operation time sequence, such as operator identity, operating environment conditions, etc. By integrating these characteristics, the power grid dispatch operation can be classified more accurately, thereby improving the accuracy of risk assessment.

[0052] In an optional embodiment, the several different categories may include other categories, such as warning operation categories, in addition to normal operation categories, dangerous operation categories, and abnormal operation categories, to cover more types of operation situations. This can further refine risk assessment and ensure that every aspect of power grid dispatching is fully considered.

[0053] In an optional embodiment, the first classification standard is designed according to the specific target power grid dispatching historical operation characteristics and the different designed categories. For example, when the target power grid dispatching historical operation characteristics select the operation type and the equipment status, and several different categories are set as the normal operation category and the dangerous operation category, the first classification standard can be designed as follows: if the operation type is "opening and closing the gate" and the equipment status is "normal", it is classified as the normal operation category; if the operation type is "emergency shutdown" and the equipment status is "abnormal", it is classified as the dangerous operation category. Through such a classification standard, it can be ensured that each step of the power grid dispatching operation is properly evaluated and classified.

[0054] In an optional embodiment, when the target power grid dispatching historical operation characteristics select operation type, equipment status, operator identity, operating environment conditions, and several different categories are set as normal operation category, dangerous operation category, abnormal operation category, and warning operation category, the first classification standard can be designed as follows: if the operation type is "switch", the equipment status is "normal", the operator identity is "senior", and the operating environment condition is "good", it is classified as the normal operation category; if the operation type is "emergency shutdown", the equipment status is "abnormal", the operator identity is "novice", and the operating environment condition is "bad", it is classified as the dangerous operation category. Such a classification standard can comprehensively consider the experience of the operator and the influence of the operating environment, so as to conduct a more detailed and accurate risk assessment of the power grid dispatching operation.

[0055] In the embodiment of the present application, the historical operation characteristics of the target power grid dispatching are selected from the following three types: operation type, equipment status and operation time sequence. This is because if operators need to be considered, the system design will become complicated, and in actual operations, the differences between operators can often be compensated by operating specifications and training.

[0056] In an embodiment of the present application, several different categories are selected, including normal operation category, dangerous operation category and abnormal operation category, because such classification can cover common operation conditions in power grid dispatching and simplify the risk assessment process. In practical applications, by real-time monitoring of power grid dispatching operations and comparing them with the preset first classification standard, the category of the current operation can be quickly identified. If the operation is classified as a normal operation category, it is allowed to continue; if it is classified as a dangerous operation category or an abnormal operation category, the system will issue an alarm to prompt the operator or automatically take measures to prevent the occurrence of erroneous operations, thereby effectively preventing power grid accidents.

[0057] In an optional embodiment, the design of the first classification standard can be implemented by different algorithms, such as decision tree algorithm, support vector machine (SVM) algorithm, neural network algorithm, etc. These algorithms can learn and establish classification models based on historical operation data, so as to accurately classify real-time operations. For example, using a decision tree algorithm, a tree-structured model can be constructed to classify operations through a series of judgment rules. The support vector machine algorithm distinguishes different types of operations by finding the optimal hyperplane, while the neural network algorithm can handle complex nonlinear relationships and classify operations by simulating the structure and function of neurons in the human brain.

[0058] In an optional embodiment, these algorithms can be selected and adjusted according to the specific needs of power grid dispatch and the characteristics of historical data. For example, if the amount of historical data is large and the relationship between features is complex, it may be more appropriate to use a neural network algorithm; if fast and intuitive classification results are required, a decision tree algorithm may be a better choice. Through the application of these algorithms, the power grid dispatch error prevention system can more intelligently and efficiently identify and prevent potential misoperations, thereby improving the safety and reliability of power grid operation.

[0059] In an embodiment of the present application, a first classification standard is preset, and real-time category division is performed in combination with the target power grid dispatching real-time operation, further comprising: calculating the membership value of the corresponding target power grid dispatching real-time operation for several different categories of dispatching operations, and taking the dispatching operation category corresponding to the maximum membership value as the real-time category division result.

[0060] In an embodiment of the present application, a first classification standard is designed for power grid dispatching operations by using a decision tree algorithm and a fuzzy classification algorithm. During the power grid dispatching operation, the decision tree algorithm classifies the dispatching operation as "normal operation", "dangerous operation" or "abnormal operation" by analyzing the characteristics of the dispatching operation (such as operation type, equipment status, and operation time sequence, etc.). The best classification feature is selected by calculating the information gain of each feature. The information gain formula is as follows:

[0061]

[0062] Where D represents the current scheduling operation data set, which contains all the operation records to be classified, and A represents a specific scheduling operation feature (such as equipment status, operation type). i is the data subset corresponding to the i-th value of feature A. |D| and |D i | respectively dataset D and sub-dataset D i The number of operations in . E(D) represents the entropy of the data set D. Its calculation formula is:

[0063]

[0064] Among them, p k is the probability of the operation of the kth category (normal, dangerous, and abnormal operation) in the current data set, and m is the number of different categories in the data set (i.e., three categories: normal, dangerous, and abnormal operations).

[0065] In the embodiment of the present application, the fuzzy classification algorithm is used to distinguish "normal operation", "dangerous operation" and "abnormal operation" in the power grid dispatching operation. By constructing a fuzzy membership function, the system can calculate the degree of match between the current operation behavior and each category. The fuzzy membership function formula is as follows:

[0066]

[0067] Among them, μ A (O) indicates the membership value of the scheduling operation O to a certain operation category A (normal, dangerous or abnormal operation), ranging from [0,1]. O represents the scheduling operation data point to be classified (represented by a feature vector, such as operation sequence, operation duration, equipment status, etc.). A is the mean of the eigenvalues ​​in category A (indicating the characteristic center value of this type of operation). A is the standard deviation of category A (indicating the degree of dispersion of the operating characteristic distribution of this category).

[0068] In the embodiment of the present application, the membership value of the scheduling operation O to each category (normal, dangerous, abnormal operation) is calculated, and the category with the highest membership value is selected as the final classification result:

[0069]

[0070] That is, for each dispatch operation O, the system selects the category A with the highest membership value as the final classification result. In this way, the system can dynamically adjust the classification strategy according to the characteristics of the operation behavior, effectively identify the type of dispatch operation, and ensure the real-time and accuracy of the classification.

[0071] It should be noted that presetting the first classification standard and performing real-time classification in combination with the real-time operation of the target power grid dispatching can reflect the current state of the power grid dispatching in real time and adjust the operation strategy in time to adapt to the real-time changes in the power grid operation.

[0072] S102, presetting a second matching degree judgment standard, and performing matching degree judgment in combination with the real-time operation of the target power grid dispatch;

[0073] In an optional embodiment, the second matching judgment criterion is to judge the matching degree for the target power grid dispatching real-time operation, which can be achieved through different matching algorithms. For example, a fuzzy matching algorithm can be used, which evaluates the similarity between the real-time operation and the preset operation mode by setting a series of fuzzy rules. The fuzzy matching algorithm can handle the uncertainty and ambiguity in the operation, and quantify the matching degree of the operation through fuzzy sets and membership functions.

[0074] In an optional embodiment, the system will determine whether to allow the operation to continue to be executed based on the matching degree between the real-time operation data and the preset operation mode. If the matching degree exceeds the set threshold, the operation is considered to be consistent with the preset mode and is allowed to be executed; if the matching degree is lower than the threshold, the system will issue an alarm to prompt the operator to re-evaluate the rationality of the operation, or automatically take measures to prevent the execution of the operation. In this way, the power grid dispatching error prevention system can further improve the safety and reliability of power grid dispatching.

[0075] In an optional embodiment, the second matching degree judgment standard can also be implemented in other ways, for example, a rule-based matching algorithm can be used, which evaluates the matching degree between the real-time operation and the standard operation mode according to a series of predefined rules. These rules can be based on specific conditions of the grid dispatching operation, such as operation time, equipment status, operation sequence, etc. In this way, the system can quickly evaluate the operation and make a judgment on whether it meets the predetermined standards. If the operation does not match the standard mode, the system will trigger corresponding safety measures, such as issuing an alarm or automatically blocking the operation to prevent potential misoperation.

[0076] In an optional embodiment, a case-based reasoning (CBR) algorithm can also be used, which evaluates the rationality of the operation by comparing the current operation with similar cases in the historical case library. In this way, the power grid dispatch error prevention system can use historical experience to guide the current operation, thereby improving the accuracy of decision-making and the overall efficiency of power grid dispatch.

[0077] In the embodiment of the present application, the matching judgment result at least includes path deviation, abnormal state or operation behavior that does not comply with safety regulations.

[0078] In an optional embodiment, the matching judgment result may also include other potential risk factors, such as equipment aging, weather conditions or external interference. The system takes these factors into consideration to ensure the safety and stability of power grid dispatching. By real-time monitoring and analyzing these risk factors, the power grid dispatching error prevention system can adjust the operation strategy in time to avoid possible risks and losses.

[0079] It should be noted that the matching judgment result is to determine the specific reason why the device enters an abnormal state.

[0080] In the embodiment of the present application, a dynamic time warping (DTW) algorithm and a state machine analysis technique are used to design a second matching judgment standard for the operation path and the device state. The standard operation path sequence is defined as P = {p 1 ,p 2 ,…,p m}, the actual operation path sequence is defined as Q = {q 1 ,q 2 ,…,q n}, these feature points p i and q i represents the key step or equipment status in the scheduling operation. The DTW calculation formula is as follows

[0081]

[0082] Where D(i,j) represents the cumulative minimum distance between the i-th operation step in the standard path sequence P and the j-th operation step in the actual path sequence Q. i ,q j ) represents the distance measure between two operation steps, and the Euclidean distance formula can be used:

[0083] d(p i ,q j )=|p i -q j |

[0084] In the present application example, p i and q j They represent the device state values ​​or operation sequences of the and steps in the scheduling operation path respectively. The boundary conditions are D(0,0)=0,D(i,0)=∞(i=1,2,…,m),D(0,j)=∞(j=1,2,…,n)

[0085] In an embodiment of the present application, state machine analysis is used to monitor the state changes of the device during the scheduling operation and to identify whether the current operation causes the device to enter an abnormal state.

[0086] It should be noted that the preset second matching judgment standard and the matching judgment combined with the real-time operation of the target power grid dispatching can reflect the current state of the power grid dispatching in real time, and adjust the operation strategy in time to adapt to the real-time changes in the power grid operation. Through the dynamic time warping (DTW) algorithm and state machine analysis technology, the system can accurately judge the matching degree of the operation path and the equipment status, so as to effectively identify and prevent potential misoperations. The DTW algorithm evaluates the similarity between the operation steps by calculating the minimum cumulative distance between the standard operation path sequence and the actual operation path sequence. The state machine analysis focuses on the changes in the equipment status to ensure that the equipment does not enter an abnormal state during the operation. Such a design not only improves the safety of power grid dispatching, but also enhances the overall efficiency and reliability of the system.

[0087] S103, performing a third risk level assessment based on the results of the real-time classification and the results of the matching judgment;

[0088] In the embodiment of the present application, the third risk level assessment is an arbitrary level assessment standard related to the operation category after real-time category division and the matching degree judgment result;

[0089] The number of levels of the level evaluation standard is determined by the number of operation categories and matching judgment results.

[0090] In an optional embodiment, the third risk level assessment can be designed with different numbers of levels, and these levels all need to correspond to a matching judgment result. For example, three levels can be set: low risk, medium risk and high risk. The low risk level may correspond to a situation where the matching degree is high and the operation category is a regular operation, the medium risk level may correspond to a situation where the matching degree is medium or the operation category is a special operation, and the high risk level corresponds to a situation where the matching degree is low and the operation category is an emergency or abnormal operation.

[0091] In an optional embodiment, the third risk level assessment can also set a more complex level division, for example, the risk level can be subdivided into five levels, each level corresponding to a different operation category and matching judgment result. For example, level one may correspond to a situation where the matching degree is very high and the operation category is conventional operation, level two may correspond to a situation where the matching degree is relatively high and the operation category is conventional operation, level three may correspond to a situation where the matching degree is medium and the operation category is special operation, level four may correspond to a situation where the matching degree is relatively low and the operation category is emergency operation, and level five corresponds to a situation where the matching degree is very low and the operation category is abnormal operation. Through this refined level division, the power grid dispatching error prevention system can provide a more accurate risk assessment, thereby helping operators make more informed decisions. In addition, the system can also automatically adjust the operation strategy according to the risk level monitored in real time to ensure the stability and safety of power grid operation.

[0092] In an optional embodiment, the specific division of the third risk level assessment can be achieved through a Bayesian network model, and can also be completed through an expert system or a machine learning algorithm. The Bayesian network model can calculate the risk probability under different operation categories and matching judgment results based on historical data and real-time data, thereby providing a more scientific risk assessment for power grid dispatching. The expert system uses the knowledge and experience of domain experts to make judgments on complex situations in power grid dispatching and provide decision support. By continuously learning the historical data of power grid dispatching, the machine learning algorithm can self-optimize the risk assessment model and improve the accuracy and adaptability of the assessment. The application of these technologies enables the power grid dispatching anti-error system to not only effectively prevent misoperation, but also provide more intelligent and personalized risk assessment and decision support in a complex and changeable power grid environment.

[0093] In an embodiment of the present application, a Bayesian network model is used to analyze the scheduling operation behavior, equipment status, and operation data to calculate the risk probability of the current operation, thereby determining the third risk level of the current operation.

[0094] In the embodiment of the present application, it is assumed that the scheduling operation behavior is represented by variable O, the equipment state is represented by variable S, and the operation data is represented by variable H, which respectively affect the probability P(R) of the occurrence of misoperation, that is, the probability of the current operation risk. Then the Bayesian network model formula is as follows:

[0095]

[0096] Among them, P(R|O,S,H) represents the conditional probability of the occurrence of the misoperation risk R (i.e., the risk value of the current operation) given the current scheduling operation behavior O, equipment status S, and operation data H. P(O,S,H|R) represents the joint probability of the current scheduling operation behavior, equipment status, and operation data when the misoperation risk R occurs. P(R) represents the prior probability of the misoperation risk R, which is usually obtained based on the statistics of the operation data. P(O,S,H) is the joint probability of the scheduling operation behavior, equipment status, and operation data in all cases. The risk level is determined by comparing the P(R|O,S,H) value with the given threshold.

[0097] In an embodiment of the present application, by comparing the similarity between the current scheduling operation and the misoperation case, the potential risk trend of the current operation is evaluated. The system matches the current operation with the operation data one by one and calculates the similarity. The more similar, the higher the risk of the current operation. Then, by combining the risk levels of all cases, the overall risk trend of the current operation is obtained. Combined with the risk probability evaluated by the Bayesian network, the system generates an anti-error strategy, risk warning, corrective suggestions or automatic anti-error control. When the risk exceeds the threshold, the system automatically takes mandatory anti-error measures to ensure the safety of the scheduling operation.

[0098] In the embodiment of the present application, the risk level is subdivided into five levels, each level corresponds to a different operation category and matching judgment result, and the risk level is judged by comparing the P(R|O,S,H) value with a given threshold.

[0099] It should be noted that, based on the results of real-time classification and matching judgment, the third risk level assessment can more accurately reflect the actual situation of power grid dispatching operations. Through this assessment, operational behaviors that may lead to unstable or faulty power grid operation can be discovered and corrected in a timely manner.

[0100] In an optional embodiment, the operation strategy can also be dynamically adjusted according to the evaluation results. For example, when the risk level is high, the system can limit or prohibit certain high-risk operations, or require operators to perform additional confirmation steps. This dynamic adjustment mechanism not only improves the safety of power grid dispatching, but also improves the flexibility and adaptability of power grid operation. In practical applications, the power grid dispatching anti-error system can effectively reduce power grid accidents caused by misoperation and ensure the stable power supply of the power grid, which is of great significance to the safe operation of the power system.

[0101] S104: Generate a corresponding error prevention strategy according to the result of the third risk level assessment.

[0102] In the embodiment of the present application, generating corresponding error prevention strategies according to the results of the third risk level assessment includes: the number of error prevention strategies is not greater than the number of the results of the third risk level assessment.

[0103] In an embodiment of the present application, the risk level is subdivided into five levels, so each evaluation result generates a corresponding error prevention strategy, for example, error prevention strategy 1: When the risk level is level one, the system allows normal operations without additional confirmation steps. Error prevention strategy 2: For level two risks, the system recommends that the operator make a confirmation to ensure the correctness of the operation. Error prevention strategy 3: In the case of level three risks, the system will remind the operator to pay attention to the details of special operations and may require a second confirmation. Error prevention strategy 4: For level four risks, the system will restrict or prohibit emergency operations and require operators to conduct a detailed review and approval process. Error prevention strategy 5: At the high risk level of level five, the system will automatically block abnormal operations and trigger emergency response procedures to prevent potential power grid failures.

[0104] It should be noted that by implementing these error prevention strategies, the power grid dispatching error prevention system can dynamically adjust the operating process according to the real-time risk assessment results, thereby ensuring the safe and stable operation of the power grid while also improving the flexibility and efficiency of operations.

[0105] This embodiment also provides a power grid dispatch error prevention system, including:

[0106] A classification module, used to preset a first classification standard and perform real-time classification in combination with the real-time operation of the target power grid dispatch;

[0107] A judgment module, used to preset a second matching judgment standard and perform matching judgment in combination with the real-time operation of the target power grid dispatch;

[0108] An evaluation module, used to perform a third risk level evaluation based on the results of real-time classification and matching judgment;

[0109] The strategy generation module is used to generate a corresponding error prevention strategy according to the result of the third risk level assessment.

[0110] In an optional embodiment, if Figure 2 , an implementable system based on the above system description is designed, in which a dynamic operation detection module 200 is designed to extract operation behavior characteristics using a decision tree algorithm and to divide normal operation, dangerous operation and abnormal operation using a fuzzy classification algorithm; a behavior trajectory and equipment status analysis module 300 is designed to use dynamic time adjustment (DTW) technology to analyze whether the operation path deviates from the standard process and to use state machine analysis technology to monitor the equipment status in real time; a risk assessment and error-prevention decision-making module 400 is designed to use a Bayesian network model to analyze operation behavior and divide risk levels; a case-based reasoning method is used to infer error operation cases and predict current behavior risks.

[0111] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0112] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 3As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for preventing errors in power grid dispatching is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0113] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0114] A first classification standard is preset, and real-time classification is performed in combination with the real-time operation of the target power grid dispatching;

[0115] A second matching judgment standard is preset, and matching judgment is performed in combination with the real-time operation of the target power grid dispatch;

[0116] Conduct a third risk level assessment based on the results of real-time classification and matching judgment;

[0117] Generate a corresponding error prevention strategy based on the results of the third risk level assessment.

[0118] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0119] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0120] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0121] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0123] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0124] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for preventing errors in power grid dispatching, characterized in that: include: A first classification standard is preset, and real-time classification is performed in combination with the real-time operation of the target power grid dispatching; A second matching judgment standard is preset, and matching judgment is performed in combination with the real-time operation of the target power grid dispatch; Performing a third risk level assessment based on the result of the real-time classification and the result of the matching degree judgment; A corresponding error prevention strategy is generated according to the result of the third risk level assessment.

2. The method for preventing errors in power grid dispatching according to claim 1, characterized in that: The preset first classification standard, combined with the real-time operation of the target power grid dispatching, includes: Acquire historical operation characteristics of the target power grid dispatch, and classify the dispatch operations into several different categories based on the historical operation characteristics of the target power grid dispatch; The plurality of different categories of the scheduling operations and the corresponding scheduling operation characteristics constitute the first classification standard; Feature extraction is performed on the target power grid dispatching real-time operation, and classification is performed according to the extracted features.

3. The method for preventing errors in power grid dispatching according to claim 2, characterized in that: The third risk level assessment is an arbitrary level assessment standard related to the operation category after real-time category division and the matching degree judgment result; The number of levels of the level evaluation standard is determined by the number of operation categories and matching degree judgment results.

4. The method for preventing errors in power grid dispatching according to claim 3, characterized in that: The acquiring of the historical operation characteristics of the target power grid dispatching and the classification based on the historical operation characteristics of the target power grid dispatching, and the classification of the dispatching operation into several different categories include: The target power grid dispatching historical operation characteristics at least include operation type, equipment status and operation time sequence; The several different categories include at least a normal operation category, a dangerous operation category and an abnormal operation category.

5. The method for preventing errors in power grid dispatching according to claim 4, characterized in that: The matching degree judgment result at least includes path deviation, abnormal state or operation behavior that does not comply with safety regulations.

6. The method for preventing errors in power grid dispatching according to claim 5, characterized in that: The preset first classification standard, combined with the target power grid dispatching real-time operation, performs real-time category division and further includes: calculating the membership value of the corresponding target power grid dispatching real-time operation for several different categories of dispatching operations, and taking the dispatching operation category corresponding to the maximum membership value as the real-time category division result.

7. The method for preventing errors in power grid dispatching according to claim 6, characterized in that: Generating corresponding error prevention strategies according to the results of the third risk level assessment includes: the number of the error prevention strategies is not greater than the number of the results of the third risk level assessment.

8. A power grid dispatching error prevention system, characterized in that: include: A classification module, used to preset a first classification standard and perform real-time classification in combination with the real-time operation of the target power grid dispatch; A judgment module, used to preset a second matching judgment standard and perform matching judgment in combination with the real-time operation of the target power grid dispatch; An evaluation module, configured to perform a third risk level evaluation based on the result of the real-time classification and the result of the matching degree judgment; A strategy generation module is used to generate a corresponding error prevention strategy according to the result of the third risk level assessment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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