Intelligent anti-misoperation model-based distribution network scheduling full-process anti-misoperation analysis method

Abstract

The invention relates to the technical field of distribution network dispatching anti-misoperation, and discloses a distribution network dispatching full-process anti-misoperation analysis method based on an intelligent anti-misoperation model, which comprises the following steps: constructing a universal intelligent analysis type anti-misoperation model; based on the general intelligent analysis type anti-misoperation model and an intelligent anti-misoperation reasoning algorithm, combining a real-time risk assessment algorithm to quantify the influence degree of operation on the power grid; calling the anti-misoperation model and the intelligent anti-misoperation reasoning algorithm to carry out verification based on a power grid operation mode required by the operation order; a dual mechanism of manual text auditing and power grid state simulation checking is adopted; and updating model parameters based on real-time data of the current power grid, and re-executing the intelligent anti-error reasoning algorithm and the real-time risk assessment algorithm. According to the invention, a full-process and dynamic self-adaptive anti-error closed loop is formed, and the defect of single-point prevention and control in the prior art is overcome.

Description

A Method for Error Prevention Analysis of the Entire Distribution Network Dispatch Process Based on Intelligent Error Prevention Model Technical Field

[0001] This invention relates to the field of distribution network scheduling error prevention technology, and more specifically, to a method for error prevention analysis of the entire distribution network scheduling process based on an intelligent error prevention model. Background Technology

[0002] Distribution network dispatching is a core component of the safe and stable operation of the power system. It involves multiple processes such as drafting, reviewing, and executing operation tickets. The objects of operation cover a massive number of devices such as circuit breakers, disconnect switches, and busbars. Moreover, the grid connection method and operating status are constantly changing. Any misoperation may cause grid failures, equipment damage, or even personal safety accidents. Therefore, preventing misoperation in dispatching has always been a key management and control issue in the power industry.

[0003] The existing prevention and control system mainly includes the following methods: such as building fixed error prevention logic based on the five basic rules of prevention and control, and verifying the legality of operations through preset rules; another example is to carry out single-point error prevention verification during the drafting or execution of operation tickets, and rely on manual review to supplement the deficiencies of technical verification.

[0004] The following shortcomings exist: Traditional error prevention models only focus on the five-prevention scenarios and lack constraints on high-risk erroneous operations such as accidentally crossing voltage level electromagnetic loops, asynchronous loop closure, and bus voltage loss; at the same time, they adopt a binary judgment of legality and illegality, and fail to quantify and assess operational risks, making it difficult to balance safety and efficiency; error prevention analysis is mostly concentrated on the single link of ticket drafting or execution, and has not formed a closed-loop prevention and control throughout the entire process, and has not considered the risk of non-erroneous operation turning into erroneous operation due to sudden changes in the power grid state during execution, resulting in an incomplete prevention and control chain. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for analyzing the entire process of distribution network scheduling based on an intelligent error prevention model, forming a full-process, dynamically adaptive error prevention closed loop, thus overcoming the shortcomings of existing technologies that rely on single-point control.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] The method for preventing errors in the entire process of distribution network scheduling based on an intelligent error prevention model includes the following steps:

[0008] S1. Construct a general intelligent analytical error prevention model. The general intelligent analytical error prevention model is based on the power grid topology model of the IEC61970 standard. It simulates the thinking process of knowledge acquisition, identification, reasoning and judgment to establish a general expert knowledge base. It integrates five layers of error prevention logic: ontology state error prevention, relational state error prevention, comprehensive analysis error prevention, operation process error prevention, management rules and permission error prevention. It also embeds an intelligent error prevention reasoning algorithm and outputs error prevention results through comprehensive judgment of multi-dimensional error prevention factors.

[0009] S2. Based on the general intelligent analytical anti-misoperation model and intelligent anti-misoperation reasoning algorithm, and combined with the real-time risk assessment algorithm, the impact of the operation on the power grid is quantified. According to the quantification results, the operation is divided into fault type, impact type, and no impact type, and the control strategies of blocking, reminder confirmation, and permission are executed respectively.

[0010] S3. Before submitting the draft ticket for review, the anti-misoperation model and intelligent anti-misoperation reasoning algorithm are called to perform verification based on the power grid operation mode required by the operation ticket. If it is determined to be an erroneous operation, the submission for review is prohibited.

[0011] S4. A dual mechanism of manual text review and power grid status simulation verification is adopted. The reviewer selects the corresponding power grid status and uses the error prevention model to deduce the operation ticket steps. If the deduction determines that it is an error or there is a risk of power grid impact, it is prohibited from passing and is sent back for modification.

[0012] S5. Before the operation ticket enters the execution state, the model parameters are updated based on the current real-time power grid data, and the intelligent anti-misconception reasoning algorithm and real-time risk assessment algorithm are re-executed. If there are any problems, the ticket is returned for modification.

[0013] S6. After each operation step is completed, the model input parameters are updated by calling real-time data from the entire network, and the intelligent anti-misoperation reasoning algorithm and real-time risk assessment algorithm are re-executed. If the risk of misoperation is determined, subsequent operations are blocked and an alarm is triggered.

[0014] By employing hierarchical constraints, real-time adaptation, and closed-loop flow mechanisms, misoperation prevention and control can be achieved throughout the entire power distribution network dispatching process.

[0015] As a preferred embodiment of the present invention, the intelligent anti-misleading reasoning algorithm adopts the fuzzy comprehensive evaluation method, and the formula is:

[0016]

[0017] in:

[0018] To prevent misjudgment of the result vector, Indicates that the operation belongs to the first The degree of membership of the category of prevention of misconduct. To prevent accidental violations, the total number of violation categories;

[0019] To prevent errors, the factor weight vector, and , To prevent errors in the total number of factors, Corresponding to the status of the main body, the status of associated devices, etc. Weights of class factors;

[0020] It is a fuzzy relation matrix. Indicates the first Class factors on the first Membership degree of the category of prevention of misconduct;

[0021] For fuzzy synthesis operators, a weighted average operator is used. .

[0022] As a preferred embodiment of the present invention, the real-time risk assessment algorithm formula is as follows:

[0023]

[0024] in: For operational risk values, ; For the first The weights of risk-related factors, and , The total number of risk impact factors, including three core factors: power grid security, equipment loss, and power supply reliability; For the first The state value of the class factor, It is calculated from equipment operating parameters and power grid topology. For the first Risk coefficient of class factors According to the power safety regulations, it is preset;

[0025] when The operation was identified as a fault. For fault-related risk thresholds; when When it is determined to be an operation that affects others, For the impact risk threshold; when When it is determined to be an operation with no impact, and .

[0026] As a preferred embodiment of the present invention, the weight vector of the intelligent error-prevention reasoning algorithm described in S1 is... The steps to determine this using the Analytic Hierarchy Process (AHP) are as follows:

[0027] 1) Construct a hierarchical structure of error prevention factors, with the target layer being "error prevention judgment" and the criterion layer including the status of the entity and the status of related equipment. Misconception prevention factors, solution layer is Category of violations for preventing misunderstandings;

[0028] 2) Construct pairwise comparison judgment matrices , Indicates the first Class factors relative to the first The degree of importance of class factors ;

[0029] 3) Calculate the largest eigenvalue of the judgment matrix. And the corresponding feature vectors, after normalization, yield the weight vector. ;

[0030] 4) Pass the consistency test Verify the reasonableness of the weights, among which , This is the average random consistency index.

[0031] As a preferred embodiment of the present invention, the risk coefficient of the real-time risk assessment algorithm A dynamic update mechanism is adopted, and the update formula is:

[0032]

[0033] in: for Time of the first Risk coefficient of class factors; For the first The initial risk coefficient of the class factor is set based on the power safety regulations; For the first The dynamic adjustment coefficient of the class factor, ; for Time of the first Class factor state values ​​and reference values The absolute value of the deviation.

[0034] As a preferred embodiment of the present invention, the relational state-based error prevention logic is embedded in a topological correlation algorithm to determine the electrical correlation strength between devices, and the formula is:

[0035]

[0036] in: For equipment With equipment The degree of topological association; For equipment and The collection of electrical connection branches between them; For equipment The set of all connecting branches; branch road The electrical distance is determined by the branch impedance. Calculated , For branch resistance, For branch circuit reactance.

[0037] As a preferred embodiment of the present invention, in the comprehensive analysis-based error prevention logic, the membership degree of high-risk erroneous operations... The cosine similarity between the device state vector and the high-risk state template vector is calculated using the following formula:

[0038]

[0039] in: This is the current state vector of the power grid equipment. For the first Operating status values ​​for each device (Operating = 1, Hot Standby = 0.8, Cold Standby = 0.5, Maintenance = 0.2); For the first The state template vector for high-risk misoperations is obtained by training from historical fault data; It is the dot product of vectors; , Let be the vector magnitude.

[0040] As a preferred embodiment of the present invention, the operation flow-type error prevention logic embeds a step compliance verification algorithm, the formula of which is:

[0041]

[0042] in: For procedural compliance index; This represents the total number of steps in the operation ticket.

[0043] For the first Importance weight of steps and ; For the first The completion status indicator for the step; if completed... If not completed ;

[0044] when Time-locking subsequent steps, This is the threshold for compliance with procedures.

[0045] As a preferred embodiment of the present invention, in the error prevention analysis of the ticket drafting process, the power grid operation parameters include bus voltage. Line load rate Transformer load rate ,in Rated voltage, This represents the actual power of the line. The rated power of the line. This is the actual capacity of the transformer. This refers to the rated capacity of the transformer.

[0046] In the simulation verification during the review phase, the power grid state simulation is generated using the Monte Carlo sampling method. A typical operating scenario ( For each scenario, the error prevention reasoning algorithm and risk assessment algorithm are executed. When more than [a certain number of scenarios are encountered], [the algorithm is executed]. The scenario satisfies and The review was approved at that time.

[0047] In the pre-execution simulation verification, the current power grid status data includes telemetry data. HeYaoXin Data ,in For voltage, For current, For active power, Reactive power The circuit breaker status is (closed = 1, open = 0). The disconnect switch is in the following states: Closed = 1, Open = 0. The grounding switch is in the following state: closed = 1, open = 0.

[0048] In a preferred embodiment of the present invention, the management rules and the permission-based error prevention logic are embedded in a permission verification algorithm, the formula of which is:

[0049]

[0050] in:

[0051] This indicates that the permissions are valid. This indicates that the user has invalid access permissions. Assigning operator privilege levels, This represents the minimum privilege level required for the operation. For operation time, For the operator's shift; Number the operating equipment. A set of authorized equipment for operators.

[0052] The beneficial technical effects of this invention are:

[0053] This invention employs a full-process design that includes draft verification, audit simulation verification, automatic verification before execution, and real-time verification at each step during execution. Combined with intelligent anti-misoperation reasoning algorithms and real-time risk assessment algorithms, it updates model parameters and recalculates by calling real-time data from the entire network after each step in the execution phase. This dynamically identifies the risk of misoperation caused by changes in the power grid status and intercepts the risk through a lockout mechanism, forming a full-process, dynamically adaptive anti-misoperation closed loop, thus solving the shortcomings of single-point prevention and control in existing technologies.

[0054] The comprehensive analytical anti-misoperation logic in this invention can cover high-risk scenarios beyond the five-fold protection, such as accidental crossing of voltage level electromagnetic loops and asynchronous loop closures. The real-time risk assessment algorithm quantifies the operational risk value and, combined with thresholds, distinguishes between fault-related, impactful, and non-impactful operations, executing blocking, notification confirmation, and permission strategies accordingly. This not only expands the coverage of anti-misoperation measures but also avoids over-control through risk quantification, significantly improving the safety and efficiency of scheduling operations and solving the problems of narrow coverage and rigid control in traditional anti-misoperation measures. Attached Figure Description

[0055] Figure 1 is a schematic diagram of the process of the present invention. Detailed Implementation

[0056] In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0057] Referring to Figure 1, the present invention provides the following embodiments:

[0058] The method for preventing errors in the entire process of distribution network scheduling based on an intelligent error prevention model includes the following steps:

[0059] S1. Construct a general intelligent analytical error prevention model. The general intelligent analytical error prevention model is based on the power grid topology model of the IEC61970 standard. It simulates the thinking process of knowledge acquisition, identification, reasoning and judgment to establish a general expert knowledge base. It integrates five layers of error prevention logic: ontology state error prevention, relational state error prevention, comprehensive analysis error prevention, operation process error prevention, management rules and permission error prevention. It also embeds an intelligent error prevention reasoning algorithm and outputs error prevention results through comprehensive judgment of multi-dimensional error prevention factors.

[0060] The intelligent error-prevention reasoning algorithm adopts the fuzzy comprehensive evaluation method, and the formula is:

[0061]

[0062] in:

[0063] To prevent misjudgment of the result vector, Indicates that the operation belongs to the first The degree of membership of the category of prevention of misconduct. To prevent accidental violations, the total number of violation categories;

[0064] To prevent errors, the factor weight vector, and , To prevent errors in the total number of factors, Corresponding to the status of the main body, the status of associated devices, etc. Weights of class factors;

[0065] It is a fuzzy relation matrix. Indicates the first Class factors on the first Membership degree of the category of prevention of misconduct;

[0066] For fuzzy synthesis operators, a weighted average operator is used. .

[0067] Through weight vector Quantify the importance of different error prevention factors using a fuzzy relation matrix. This method characterizes the correlation strength between single factors and error types, and then integrates multi-dimensional information through a weighted average synthesis operator. It simulates the human brain's comprehensive judgment process in complex scenarios, addressing the limitations of single-factor judgment. It achieves precise fusion of multi-dimensional error prevention factors, avoiding the limitations of traditional black-and-white binary judgments, significantly improving the accuracy of error identification in ambiguous and unclear scenarios, and reducing the probability of missed and false positives.

[0068] S2. Based on the general intelligent analytical anti-misoperation model and intelligent anti-misoperation reasoning algorithm, and combined with the real-time risk assessment algorithm, the impact of the operation on the power grid is quantified. According to the quantification results, the operation is divided into fault type, impact type, and no impact type, and control strategies of blocking, reminder confirmation, and permission are executed respectively.

[0069] The formula for the real-time risk assessment algorithm is:

[0070]

[0071] in: For operational risk values, ; For the first The weights of risk-related factors, and , The total number of risk impact factors, including three core factors: power grid security, equipment loss, and power supply reliability; For the first The state value of the class factor, It is calculated from equipment operating parameters and power grid topology. For the first Risk coefficient of class factors According to the power safety regulations, it is preset;

[0072] when The operation was identified as a fault. For fault-related risk thresholds; when When it is determined to be an operation that affects others, For the impact risk threshold; when When it is determined to be an operation with no impact, and .

[0073] Through weight Prioritize risk factors and utilize state values. Capture the real-time operating status of the power grid, combined with preset risk coefficients Quantifying the impact of operations on different dimensions enables quantitative risk assessment. Breaking away from the rigid traditional legal / illegal judgment model, it precisely categorizes operations by risk value, both forcibly blocking high-risk fault-related operations and reserving flexible decision-making space for impactful operations, thus balancing power grid safety control with dispatching efficiency.

[0074] S3. Before submitting the draft ticket for review, the anti-misoperation model and intelligent anti-misoperation reasoning algorithm are called to perform verification based on the power grid operation mode required by the operation ticket. If it is determined to be an erroneous operation, the submission for review is prohibited.

[0075] S4. A dual mechanism of manual text review and power grid status simulation verification is adopted. The reviewer selects the corresponding power grid status and uses the error prevention model to deduce the operation ticket steps. If the deduction determines that it is an error or there is a risk of power grid impact, it is prohibited from passing and is sent back for modification.

[0076] S5. Before the operation ticket enters the execution state, the model parameters are updated based on the current real-time power grid data, and the intelligent anti-misconception reasoning algorithm and real-time risk assessment algorithm are re-executed. If there are any problems, the ticket is returned for modification.

[0077] S6. After each operation step is completed, the model input parameters are updated by calling real-time data from the entire network, and the intelligent anti-misoperation reasoning algorithm and real-time risk assessment algorithm are re-executed. If the risk of misoperation is determined, subsequent operations are blocked and an alarm is triggered.

[0078] By employing hierarchical constraints, real-time adaptation, and closed-loop flow mechanisms, misoperation prevention and control can be achieved throughout the entire power distribution network dispatching process.

[0079] Furthermore, the weight vector of the intelligent error-prevention reasoning algorithm described in S1 The steps to determine this using the Analytic Hierarchy Process (AHP) are as follows:

[0080] 1) Construct a hierarchical structure of error prevention factors, with the target layer being "error prevention judgment" and the criterion layer including the status of the entity and the status of related equipment. Misconception prevention factors, solution layer is Category of violations for preventing misunderstandings;

[0081] 2) Construct pairwise comparison judgment matrices , Indicates the first Class factors relative to the first The degree of importance of class factors ;

[0082] 3) Calculate the largest eigenvalue of the judgment matrix. And the corresponding feature vectors, after normalization, yield the weight vector. ;

[0083] 4) Pass the consistency test Verify the reasonableness of the weights, among which , This is the average random consistency index.

[0084] By hierarchically breaking down the complex problem of ranking the importance of prevention factors, it is transformed into pairwise comparison judgments. Consistency checks are then used to eliminate logical contradictions arising from subjective judgments, ensuring that weight allocation aligns with actual prevention and control needs. This avoids arbitrary subjective setting of weight vectors, making the quantification of the importance of each prevention factor more scientific and reasonable, and improving the reliability and persuasiveness of the intelligent prevention reasoning algorithm.

[0085] Furthermore, the risk coefficient of the real-time risk assessment algorithm A dynamic update mechanism is adopted, and the update formula is:

[0086]

[0087] in: for Time of the first Risk coefficient of class factors; For the first The initial risk coefficient of the class factor is set based on the power safety regulations; For the first The dynamic adjustment coefficient of the class factor, ; for Time of the first Class factor state values ​​and reference values The absolute value of the deviation.

[0088] Based on the deviation between the real-time state and the reference state of the power grid, the coefficient is dynamically adjusted. Adaptive adjustment of risk coefficients enables risk assessments to adapt to dynamic changes in the power grid status; it also makes risk assessment results more consistent with real-time power grid scenarios, improving the timeliness and accuracy of risk judgment.

[0089] Furthermore, the relational state-based error prevention logic is embedded with a topological correlation algorithm to determine the electrical correlation strength between devices, using the following formula:

[0090]

[0091] in: For equipment With equipment The degree of topological association; For equipment and The collection of electrical connection branches between them; For equipment The set of all connecting branches; branch road The electrical distance is determined by the branch impedance. Calculated , For branch resistance, For branch circuit reactance.

[0092] By quantifying the impact of electrical distance on the association strength of branches, and then calculating the association degree between devices by the ratio of the branch set, the key associated devices of the operating device can be accurately identified; the association identification accuracy of devices on the opposite side of the modeling boundary is improved, the boundary error prevention effect is strengthened, and the misoperation caused by the omission of associated devices is reduced.

[0093] Furthermore, in the comprehensive analytical error prevention logic, the membership degree of high-risk erroneous operations... By calculating the cosine similarity between the device state vector and the high-risk state template vector, the typical states of power grid equipment and historical high-risk misoperations are transformed into vectors. The degree of matching between the two is quantified by the cosine similarity measure, thereby achieving pattern recognition for high-risk scenarios.

[0094] The calculation formula is:

[0095]

[0096] in: This is the current state vector of the power grid equipment. For the first Operating status values ​​for each device (Operating = 1, Hot Standby = 0.8, Cold Standby = 0.5, Maintenance = 0.2); For the first The state template vector for high-risk misoperations is obtained by training from historical fault data; It is the dot product of vectors; , Let be the vector magnitude.

[0097] Furthermore, the operational process-based error prevention logic embeds a step compliance verification algorithm, the formula of which is:

[0098]

[0099] in: For procedural compliance index; This represents the total number of steps in the operation ticket.

[0100] For the first Importance weight of steps and ; For the first The completion status indicator for the step; if completed... If not completed ;

[0101] when Time-locking subsequent steps, This is the threshold for compliance with procedures.

[0102] Furthermore, in the error prevention analysis of the ticket drafting process, the power grid operation parameters include bus voltage. Line load rate Transformer load rate ,in Rated voltage, This represents the actual power of the line. The rated power of the line. This is the actual capacity of the transformer. This refers to the rated capacity of the transformer.

[0103] In the simulation verification during the review phase, the power grid state simulation is generated using the Monte Carlo sampling method. A typical operating scenario ( For each scenario, the error prevention reasoning algorithm and risk assessment algorithm are executed. When more than [a certain number of scenarios are encountered], [the algorithm is executed]. The scenario satisfies and The review was approved at that time.

[0104] In the pre-execution simulation verification, the current power grid status data includes telemetry data. HeYaoXin Data ,in For voltage, For current, For active power, Reactive power The circuit breaker status is (closed = 1, open = 0). The disconnect switch is in the following states: Closed = 1, Open = 0. The grounding switch is in the following state: closed = 1, open = 0.

[0105] This makes the whole-process error prevention analysis more aligned with the core prevention and control needs of each stage. In the drafting stage, it ensures that the operation plan is adapted to the target operation mode. In the review stage, it improves the scenario adaptability of the operation ticket. In the pre-execution stage, it ensures that the operation matches the real-time power grid status, further reducing the risk of misoperation throughout the whole process.

[0106] Furthermore, the management rules and permission-based error prevention logic are embedded with a permission verification algorithm, the formula of which is:

[0107]

[0108] in:

[0109] This indicates that the permissions are valid. This indicates that the user has invalid access permissions. Assigning operator privilege levels, This represents the minimum privilege level required for the operation. For operation time, For the operator's shift; Number the operating equipment. A set of authorized equipment for operators.

[0110] The main purpose of error prevention analysis based on the intelligent error prevention model is to provide constraints or operational reminders based on the rationality of the operation mode under the actual wiring and operation mode of the power grid.

[0111] Verification and analysis of dispatch instructions or remote control operations are divided into three types: First, maloperations that cause grid faults or violate operating procedures, such as those specified in the five prevention measures, are subject to a lockout warning and strict lockout. Second, operations that are not faulty but affect the grid, whether expected or not, such as causing power outages, are analyzed to determine the impact, a warning is issued, and the dispatcher or operator determines whether to lock out the operation. Third, instructions or remote control operations that do not affect the grid are permitted.

[0112] The error prevention analysis during the drafting process involves performing verification analysis under the required operating mode during the drafting process. If any erroneous operation is found, the drafting process will be prohibited from being submitted for review.

[0113] During the review of ballots, in addition to manual review of the text, reviewers can use a certain power grid state to simulate the verification of the ballot. If there is any error, the ballot will not be approved and can be returned for the drafter to rewrite.

[0114] When an operation ticket enters the execution state, the system will automatically perform a simulation verification to check whether each operation step in the operation ticket will cause misoperation or undesirable effects under the current power grid conditions. If a problem is found, it can be returned for modification.

[0115] During execution, a fault-prevention analysis is performed using real-time data from the entire network at each step. The main reason for this is to prevent situations where changes in the power grid during execution cause the operation steps to change from non-misoperation to misoperation or cause undesirable effects.

[0116] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for preventing errors in the entire process of distribution network scheduling based on an intelligent error prevention model, characterized in that, Includes the following steps: S1. Construct a general intelligent analytical error prevention model. This model is based on the power grid topology model of the IEC 61970 standard, simulating the thought process of knowledge acquisition, identification, reasoning, and judgment to establish a general expert knowledge base. It integrates five layers of error prevention logic: ontology-state error prevention, relational-state error prevention, comprehensive analytical error prevention, operation process error prevention, management rule and permission-based error prevention, and embeds an intelligent error prevention reasoning algorithm. It outputs error prevention results through comprehensive judgment of multi-dimensional error prevention factors. S2. Based on the general intelligent analytical error prevention model and intelligent error prevention reasoning algorithm, and combined with a real-time risk assessment algorithm, quantify the impact of operations on the power grid. Based on the quantification results, operations are divided into fault-related, impact-related, and non-impact-related categories, and corresponding control strategies of blocking, reminder confirmation, and permission are executed. S3. After drafting the ticket but before submitting it for review, based on… The operation ticket requires a specific power grid operating mode. The error prevention model and intelligent error prevention reasoning algorithm are used for verification. If the error is determined to be erroneous, submission for review is prohibited. S4. A dual mechanism of manual text review and power grid status simulation verification is adopted. Reviewers select the corresponding power grid status and use the error prevention model to deduce the operation ticket steps. If the deduction determines an error or a risk to the power grid, the ticket is prohibited from passing and returned for modification. S5. Before the operation ticket enters the execution state, the model parameters are updated based on the current real-time power grid data, and the intelligent error prevention reasoning algorithm and real-time risk assessment algorithm are re-executed. If problems exist, the ticket is returned for modification. S6. After each operation step is completed, the model input parameters are updated using real-time data from the entire network, and the intelligent error prevention reasoning algorithm and real-time risk assessment algorithm are re-executed. If an error risk is determined, subsequent operations are blocked and an alarm is triggered.

2. The method for preventing errors in the entire process of distribution network scheduling based on an intelligent error prevention model according to claim 1, characterized in that, The intelligent error-prevention reasoning algorithm adopts the fuzzy comprehensive evaluation method, and the formula is: in: To prevent misjudgment of the result vector, Indicates that the operation belongs to the first The degree of membership of the category of prevention of misconduct. To prevent accidental violations, the total number of violation categories; To prevent errors, the factor weight vector, and ， To prevent errors in the total number of factors, Corresponding to the status of the main body, the status of associated devices, etc. Weights of class factors; It is a fuzzy relation matrix. Indicates the first Class factors on the first Membership degree of the category of prevention of misconduct; For fuzzy synthesis operators, a weighted average operator is used. 。 3. The method for preventing errors in the entire process of distribution network scheduling based on an intelligent error prevention model according to claim 1, characterized in that, The formula for the real-time risk assessment algorithm is: in: For operational risk values, ； For the first The weights of risk-related factors, and ， The total number of risk impact factors, including three core factors: power grid security, equipment loss, and power supply reliability; For the first The state value of the class factor, It is calculated from equipment operating parameters and power grid topology. For the first Risk coefficient of class factors According to the preset requirements of the power safety regulations; when The operation was identified as a fault. For fault-related risk thresholds; when When it is determined to be an operation that affects others, For the impact risk threshold; when When it is determined to be an operation with no impact, and 。 4. The method for preventing errors in the entire process of distribution network scheduling based on an intelligent error prevention model according to claim 1, characterized in that, The weight vector of the intelligent error-prevention reasoning algorithm described in S1 The steps determined using the Analytic Hierarchy Process (AHP) are as follows: 1) Construct a hierarchical structure of error prevention factors, with the target layer being "error prevention judgment" and the criterion layer including the entity status, associated equipment status, etc. Misconception prevention factors, solution layer is Classification of violations for prevention; 2) Construct pairwise comparison judgment matrix ， Indicates the first Class factors relative to the first The degree of importance of class factors 3) Calculate the largest eigenvalue of the judgment matrix. And the corresponding feature vectors, after normalization, yield the weight vector. 4) Pass the consistency test Verify the reasonableness of the weights, among which ， This is the average random consistency index.

5. The method for preventing errors in the entire process of distribution network scheduling based on an intelligent error prevention model according to claim 3, characterized in that, The risk coefficient of the real-time risk assessment algorithm A dynamic update mechanism is adopted, and the update formula is: in: for Time of the first Risk coefficient of class factors; For the first The initial risk coefficient of the class factor is set based on the power safety regulations; For the first The dynamic adjustment coefficient of the class factor, ； for Time of the first Class factor state values ​​and reference values The absolute value of the deviation.

6. The method for preventing errors in the entire process of distribution network scheduling based on an intelligent error prevention model according to claim 1, characterized in that, The relational state-based anti-misoperation logic is embedded in the topological correlation algorithm to determine the electrical correlation strength between devices. The formula is as follows: in: For equipment With equipment The degree of topological association; For equipment and The collection of electrical connection branches between them; For equipment The set of all connecting branches; branch road The electrical distance is determined by the branch impedance. Calculated ， For branch resistance, For branch circuit reactors.

7. The method for preventing errors in the entire process of distribution network scheduling based on an intelligent error prevention model according to claim 1, characterized in that, In the comprehensive analytical error prevention logic, the membership degree of high-risk erroneous operations... The cosine similarity between the device state vector and the high-risk state template vector is calculated using the following formula: in: This is the current state vector of the power grid equipment. For the first Operating status values ​​for each device (Operating = 1, Hot Standby = 0.8, Cold Standby = 0.5, Maintenance = 0.2); For the first The state template vector for high-risk misoperations is obtained by training from historical fault data; It is the dot product of vectors; 、 Let be the vector magnitude.

8. The method for preventing errors in the entire process of distribution network scheduling based on an intelligent error prevention model according to claim 1, characterized in that, The operational flow-based error prevention logic embeds a step compliance verification algorithm, the formula of which is: in: For procedural compliance index; This represents the total number of steps in the operation ticket. For the first Importance weight of steps and ； For the first The completion status indicator for the step; if completed... If not completed ;when Time-locking subsequent steps, This is the threshold for compliance with procedures.

9. The method for preventing errors in the entire process of distribution network scheduling based on an intelligent error prevention model according to claim 1, characterized in that, In the error prevention analysis of the ticket drafting process, the power grid operation parameters include bus voltage. Line load factor Transformer load rate ,in Rated voltage, This represents the actual power of the line. The rated power of the line, This is the actual capacity of the transformer. The rated capacity of the transformer; in the simulation verification during the review phase, the power grid state simulation is generated using the Monte Carlo sampling method. A typical operating scenario ( For each scenario, the error prevention reasoning algorithm and risk assessment algorithm are executed. When more than [a certain number of scenarios are encountered], [the algorithm is executed]. The scenario satisfies and Upon approval, the current power grid status data in the pre-execution simulation verification includes telemetry data. HeYaoXin Data ,in For voltage, For current, For active power, Reactive power The circuit breaker status is (closed = 1, open = 0). The disconnect switch is in the following states: Closed = 1, Open = 0. The grounding switch is in the following state: closed = 1, open = 0.

10. The method for preventing errors in the entire process of distribution network scheduling based on an intelligent error prevention model according to claim 1, characterized in that, The management rules and permission-based error prevention logic are embedded in the permission verification algorithm, and the formula is as follows: in: This indicates that the permissions are valid. This indicates that the user has invalid access permissions. Assigning operator privilege levels, This represents the minimum privilege level required for the operation. For operation time, For the operator's shift; Number the operating equipment. A set of authorized equipment for operators.

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