Intelligent substation secondary equipment remote commissioning method based on digital twinning

Abstract

The application discloses a remote debugging method for secondary equipment of an intelligent substation based on digital twinning, relates to the technical field of remote debugging, and is used for solving the problem of lagged emergency task response, acquiring a debugging arrangement flow sequence in remote debugging of secondary equipment of an intelligent substation, extracting online role information through a user login interface, judging a debugging scene according to the number of roles, setting an analysis time period if parallel debugging is adopted, generating a role mismatch risk factor by counting the number of operations and the number of role changes, marking risks of users and forming cross-role information, triggering a cooperative constraint mechanism when the mark reaches a threshold, setting a locking time length for the flow and monitoring debugging data in real time, automatically locking the authority of a high-risk user or performing secondary distribution of a task when an emergency task is detected, and ensuring that the debugging flow is stable and controlled.

Description

Technical Field

[0001] This invention relates to the field of remote commissioning technology, and more specifically, to a method for remote commissioning of secondary equipment in intelligent substations based on digital twins. Background Technology

[0002] As a core component of the digitalization, automation, and networking of power systems, smart substations feature a wide variety of secondary equipment, complex business logic, and highly coupled data interaction relationships between devices. With the evolution of operation and maintenance systems towards centralization and remote operation, remote commissioning is gradually becoming a key means of lifecycle management for secondary equipment.

[0003] The existing technology has the following shortcomings: Currently, existing technologies mainly rely on static permission models and manually formulated debugging process control strategies. Because they cannot identify and analyze debugging scenarios in real time based on the dynamic changes in the online user role structure and debugging orchestration process sequence, it is difficult to effectively monitor fluctuations in user operation frequency and role change behavior in multi-role parallel debugging scenarios. This can easily lead to role mismatch operations, debugging command conflicts, and unauthorized operations, resulting in increased uncontrollability of the debugging process and delayed emergency response. Therefore, a remote debugging method for secondary equipment in intelligent substations based on digital twins is proposed.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a remote commissioning method for secondary equipment in intelligent substations based on digital twins. This method addresses the problems mentioned in the background by employing commissioning orchestration process sequence parsing, dynamic user role modeling, calculation of role mismatch risk factors in parallel scenarios, and collaborative constraint triggering mechanisms.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a remote commissioning method for secondary equipment in an intelligent substation based on digital twins, comprising the following steps: Step S1: In the remote commissioning of secondary equipment in intelligent substations, obtain the sequence of commissioning task arrangement, obtain the role information of online users on the current commissioning platform through the user login interface, count the number of online roles based on the role information, and judge the commissioning scenario. Step S2: When debugging the scenario as a parallel scenario, set a parallel analysis period, count the number of role operations of online users and calculate the user operation frequency during the parallel analysis period, collect the number of role changes in the orchestration process sequence, and generate the role mismatch risk factor of online users in combination with the user operation frequency. Step S3: Mark online users based on role mismatch risk factors and generate cross-role information. After marking online users, trigger the collaborative constraint mechanism. Set the lock duration of the current orchestration process sequence in the collaborative constraint mechanism. Monitor the debugging task data in real time within the lock duration. Step S4: Capture emergency debugging tasks based on debugging task data, call the source attribute of the emergency debugging task, and combine cross-role information to select whether to lock the debugging permissions of online users or to reassign the emergency debugging task.

[0007] In a preferred embodiment, in step S1, the orchestration sequence of debugging tasks is obtained through the debugging task information database. The orchestration sequence refers to the execution order structure generated by the debugging platform based on the device under test, debugging strategy and on-site requirements, which consists of multiple debugging tasks in the execution order. The user login interface is used to obtain the role information of the online users on the current debugging platform. The user information includes the professional role type, current role status, and current debugging task.

[0008] In a preferred embodiment, in step S1, under the current debugging task, the number of each professional role type whose role status is "participation" is counted and used as the number of online roles; The debugging scenario is determined based on the distribution of roles: if the number of online roles in the current debugging task is greater than 1, then the debugging scenario is determined to be a parallel debugging scenario. If the number of online characters is equal to 1, then the debugging scenario is determined to be a serial debugging scenario.

[0009] In a preferred embodiment, in step S2, when the debugging scenario is a parallel scenario, a parallel analysis period is preset. During the parallel analysis period, the number of role operations of each online user is obtained through the user operation log. The number of role operations refers to the cumulative number of times the online user performs debugging task operations. The ratio of the number of character operations to the parallel analysis period is used as the user operation frequency; Collect operation records of the orchestration process sequence through user operation logs, and compare the professional role types in two adjacent operation records; When two adjacent operation records have different professional role types, it is determined that an operation switch has occurred between the online user and other different professional role types, and this operation switch is counted as a role change for the online user. The number of times online users changed roles within the parallel analysis period is counted as the number of times online users changed roles.

[0010] In a preferred embodiment, in step S2, the role change frequency and user operation frequency are standardized to obtain the role change coefficient and the user operation coefficient, respectively. Calculate the online user role mismatch risk factor based on role change coefficient and user operation coefficient: ,in, and To preset the adjustment weight, For the change coefficient of the character, Here, e is the user operation coefficient, and e is the natural constant. Role mismatch risk factors.

[0011] In a preferred embodiment, in step S3, cluster analysis is performed based on role mismatch risk factors to label online users: The risk factor of role mismatch is used as the input feature for clustering to construct a cluster dataset, and then a binary clustering algorithm is called to analyze the cluster dataset; The binary clustering algorithm iteratively calculates the Euclidean distance between the role mismatch risk factor of each online user and the two cluster centers, and uses the minimum distance criterion to assign online users to the corresponding cluster centers; After clustering is completed, the mean of the role mismatch risk factor of the two clusters is calculated. The cluster with the higher mean is taken as the high-risk category, and online users in it are screened out and labeled for risk.

[0012] In a preferred embodiment, in step S3, online users are marked according to the role mismatch risk factor to generate cross-role information of the corresponding online users; Cross-role information is a single tagged data used to characterize the permission boundary offsets that exist between online users and multiple business roles; After marking online users, count the number of marked online users and use the number of marked online users as the marked user count value; When the number of marked users is greater than or equal to the preset threshold for the number of marked users, the collaborative constraint mechanism is triggered; otherwise, the collaborative constraint mechanism is not activated. In the collaborative constraint mechanism, a preset lock duration is allocated for the current orchestration process sequence, and the lock duration is written into the process status record to keep the current orchestration process sequence under control. During the locked period, the system monitors the debugging task data in real time. The debugging task data is the total number of all debugging tasks corresponding to the current orchestration process sequence accumulated during the locked period.

[0013] In a preferred embodiment, in step S4, when an increase in the total number of debugging tasks is detected compared to the previous monitoring period, it is determined that the new debugging tasks initiated by online users within the lockout period and not included in the current orchestration process sequence are emergency debugging tasks. When a new emergency debugging task is detected, the source attributes of the emergency debugging task are called, including the identity of the user who initiated the task, the type of professional role, the current role status, whether it has been marked as a high-risk user, and whether it is under a collaborative constraint mechanism, etc.

[0014] In a preferred embodiment, in step S4, after obtaining the source attribute of the emergency debugging task, different handling measures are taken for online users based on cross-role information: If the online users in the source information of the emergency debugging task are marked as high-risk and are under the control of the collaborative constraint mechanism triggered by the number of marked users reaching a threshold, then the debugging permission will be locked. If the source user of the emergency debugging task is not marked as high-risk or the collaborative constraint mechanism is not triggered, the emergency debugging task will proceed to a secondary allocation process.

[0015] The technical effects and advantages of this invention are as follows: This invention, in the remote commissioning of secondary equipment in intelligent substations, first acquires the orchestration sequence of commissioning tasks and extracts the role information of online users through a user login interface. Based on the number of roles, it determines whether the commissioning scenario is serial or parallel. If it is a parallel scenario, a parallel analysis period is set, the number of operations performed by online users is counted to calculate the operation frequency, and the number of role changes in the process sequence is collected. Combining these two factors, a role mismatch risk factor is generated. Based on this factor, users are risk-marked and cross-role information is generated. When the number of marked users reaches a threshold, a collaborative constraint mechanism is triggered to set a lock duration for the current orchestration process and monitor commissioning task data in real time. When an emergency commissioning task is detected, its source attribute is called, and combined with the cross-role information, the commissioning permissions of high-risk users are automatically locked, or the emergency task is reassigned to ensure the controllability and stability of the commissioning process. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the implementation of the remote commissioning method for secondary equipment in an intelligent substation based on digital twins, as described in this invention.

[0017] Figure 2 This is a schematic diagram illustrating the steps of the remote commissioning method for secondary equipment in an intelligent substation based on digital twins according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1, as Figures 1 to 2 As shown, the remote commissioning method for secondary equipment in a smart substation based on digital twins includes the following steps: Step S1: In the remote commissioning of secondary equipment in intelligent substations, obtain the sequence of commissioning task arrangement, obtain the role information of online users on the current commissioning platform through the user login interface, count the number of online roles based on the role information, and judge the commissioning scenario. Step S2: When debugging a parallel scenario, set a parallel analysis period, count the number of role operations of online users during the parallel analysis period, calculate the user operation frequency, collect the number of role changes in the orchestration process sequence, and generate the role mismatch risk factor of online users by combining the user operation frequency. Step S3: Mark online users based on role mismatch risk factors and generate cross-role information. After marking online users, trigger the collaborative constraint mechanism. Set the lock duration of the current orchestration process sequence in the collaborative constraint mechanism. Monitor the debugging task data in real time within the lock duration. Step S4: Capture emergency debugging tasks based on debugging task data, call the source attribute of the emergency debugging task, and combine cross-role information to select whether to lock the debugging permissions of online users or to reassign the emergency debugging task.

[0020] The specific implementation is as follows: In step S1, during the remote commissioning of secondary equipment in a smart substation, the remote commissioning process typically consists of multiple sequential steps, with each step involving different types of professional roles. When different roles operate in parallel on the same commissioning platform, it can affect the execution order of the commissioning process sequence, potentially leading to risks such as process reordering, task overwriting, or execution chaos. At the beginning of the commissioning process, the commissioning environment is analyzed to identify the roles of online users in the current commissioning scenario and determine whether they are in a parallel commissioning state. The debugging task orchestration sequence is obtained through the debugging task information database. The orchestration sequence refers to the execution order structure generated by the debugging platform based on the device under test, debugging strategy and field requirements. It consists of multiple debugging tasks in the execution order. After obtaining the orchestration process sequence, the role information of the online users on the current debugging platform is obtained through the user login interface. The user information includes the professional role type, current role status, and current debugging task. Among them, the professional role type refers to the data field of the professional division of labor attribute undertaken in the remote commissioning process of secondary equipment in intelligent substations, which is used to distinguish the scope of responsibilities of different online users in the commissioning task; The current role status is used to determine whether an online user is participating in debugging activities. It includes online status and participation status. Participation status means that the online user is participating in the execution process of the current debugging task. Online status means that the online user is connected to the debugging platform and does not mean that the user is participating in debugging operations. Debugging tasks refer to the debugging tasks that online users participate in during the remote debugging process of the device. They are used to identify whether different professional roles are operating on the same debugging task stage. Online users are categorized and statistically analyzed based on their professional role types. Under the current debugging task, the number of each professional role type whose current role status is "participating" is counted and used as the number of online roles, reflecting the participation structure of different professional roles in the remote debugging process. The debugging scenario is judged based on the role distribution: if the number of online roles in the current debugging task is greater than 1, it means that there is a situation of parallel participation across roles in the debugging process. Different roles may switch tasks between debugging steps and affect the debugging process sequence. Therefore, the debugging scenario is judged as a parallel debugging scenario. If the number of online roles is equal to 1, it means that the debugging task is executed by a single role and the debugging steps are performed in the order of the orchestration process. Therefore, the debugging scenario is judged to be a serial debugging scenario. For serial debugging scenarios, maintain the original orchestration process sequence; for parallel debugging scenarios, proceed to the subsequent role mismatch risk analysis.

[0021] It should be noted that the debug task information database is used to store the orchestration process sequence, debug task node information, and debug task identifier; the user login interface is used to receive online user identity information and role attribute information, and is the communication interface for the debug platform to obtain online role data.

[0022] This step obtains the current online user status and role composition before the debugging process begins, establishing a reliable basis for multi-role collaborative debugging, avoiding disordered process development in multi-role scenarios, improving the execution stability and controllability of the debugging process, and also providing a data foundation for subsequent emergency debugging task processing and permission allocation.

[0023] In step S2, when the debugging scenario is a parallel scenario, a parallel analysis period is preset. During the parallel analysis period, the number of role operations of each online user is obtained through the user operation log. The number of role operations refers to the cumulative number of times the online user triggers the debugging task operation, reflecting the actual number of operation behaviors generated by the online user in the current debugging process. The ratio of the number of character operations to the parallel analysis period is used as the user operation frequency; User operation frequency reflects the activity level of online users in parallel debugging scenarios; the higher the user operation frequency, the more frequently the current debugging task is operated, and the higher the possibility of concurrent impact on the debugging process sequence. By collecting operation records of the orchestration process sequence through user operation logs, and comparing the professional role types in two adjacent operation records, if the professional role types of two adjacent operation records are different, it is determined that an operation switch has occurred between the online user and other different professional role types, and this operation switch is counted as a role change of the online user.

[0024] For example, for any online user, when the online user is the executor of the previous or next operation record, and the professional role types of the two adjacent operation records are different, the corresponding cross-role operation switching event is counted as a role change for the online user. During the parallel analysis period, the number of role change events related to each online user is counted as the number of role changes for the online user. The number of online user role changes during the parallel analysis period is used as the online user role change count. The role change count refers to the number of times an online user switches between different professional role types during the debugging task. After standardizing the number of role changes and the frequency of user operations, we obtain the role change coefficient and the user operation coefficient. Calculate the online user role mismatch risk factor based on role change coefficient and user operation coefficient: ,in, and To preset the adjustment weight, For the change coefficient of the character, Here, e is the user operation coefficient, and e is the natural constant. Risk factors for role mismatch; The higher the value of the role mismatch risk factor, the higher the degree to which online users participate in cross-role switching in parallel debugging scenarios, the greater the frequency of the operation affecting the debugging process, and the greater the risk of the debugging process sequence being overwritten or the execution order being rearranged. The smaller the value of the role mismatch risk factor, the lower the participation of online users in the parallel debugging scenario, the weaker the intervention intensity of the debugging process, and the smaller the risk of conflict or reordering of the debugging task execution sequence.

[0025] It should be noted that the preset parallel analysis period can be set according to the execution rhythm of the debugging task, the average time of the debugging steps, and the operation frequency of different professional roles; the user operation log is used to store user execution operation records, debugging command issuance records, and debugging process status change records, etc.; the standardization processing methods include, but are not limited to, standard linear transformation based on interval scaling, statistical Z-Score standardization method, or normalization method based on nonlinear mapping function. The application methods of standardization processing will not be elaborated here; the preset adjustment weight can be set according to the degree of influence of different professional roles on the debugging task, actual debugging experience, or historical risk statistics.

[0026] This step involves statistical analysis of online user behavior during the parallel analysis period. It quantifies the frequency of user operations and the degree of role alternation in the debugging task, calculates the role mismatch risk factor, and provides a basis for risk judgment for triggering the collaborative constraint mechanism in the future, thereby realizing risk identification of multi-role parallel debugging driven by operation data.

[0027] In step S3, cluster analysis is performed based on role mismatch risk factors to label online users. Specifically, role mismatch risk factors are used as input features to construct a cluster dataset. Then, a binary clustering algorithm is called to analyze the cluster dataset. The binary clustering algorithm iteratively calculates the Euclidean distance between the role mismatch risk factor of each online user and the two cluster centers, and uses the minimum distance criterion to assign online users to the corresponding cluster centers, so that each cluster center corresponds to two convergence intervals of the role mismatch risk factor distribution.

[0028] After clustering is completed, the mean of the role mismatch risk factor of the two clusters is calculated. The cluster with the higher mean is taken as the high-risk category, and online users in it are screened out and labeled for risk.

[0029] It should be noted that cluster analysis refers to the unsupervised partitioning process of a dataset containing role mismatch risk factors. It groups online users with similar numerical characteristics into the same category by measuring the numerical similarity between data samples. Binary clustering algorithm refers to a clustering operation method with a preset number of clusters of two. Iterative calculation refers to repeatedly executing the loop operation of "distance calculation - category assignment - cluster center update" during the clustering process until the change in the position of the cluster center in two consecutive rounds of calculation is lower than a set threshold. The cluster center refers to the center point used in the binary clustering algorithm to represent the typical feature value of each cluster category. It is the average or weighted average of the role mismatch risk factors of all online users in the cluster category. Euclidean distance refers to the distance function used to measure the numerical difference between the role mismatch risk factors of online users and the cluster centers. The minimum distance criterion refers to the rule in the category assignment stage that assigns each online user to the category corresponding to the cluster center with the smallest Euclidean distance.

[0030] When marking online users based on role mismatch risk factors, cross-role information for the corresponding online users is generated. Cross-role information is a single marker data used to characterize the permission boundary offset between online users and multiple business roles.

[0031] After marking online users, count the number of marked online users and use this count as the marked user count value.

[0032] Compare the number of marked users with a preset threshold for the number of marked users: When the number of marked users is greater than or equal to the preset threshold for the number of marked users, the collaborative constraint mechanism is triggered. Conversely, when the number of marked users is less than the preset threshold for the number of marked users, the collaborative constraint mechanism will not be activated to ensure that the debugging process can be carried out in a controlled environment when high-risk users appear in large numbers, thereby reducing process conflicts and debugging anomalies caused by role mismatch or unauthorized behavior.

[0033] It should be noted that the preset threshold for the number of marked users is obtained through parametric modeling of the concurrency characteristics of remote commissioning scenarios for secondary equipment in smart substations. Specifically, based on the operation logs of the historical commissioning platform, the distribution of the total number of online users in typical parallel commissioning scenarios is statistically analyzed, and the proportion of user roles is proportionally analyzed to obtain the possible scale of mixed role operations in each scenario. Subsequently, the proportion of commissioning anomalies caused by role conflicts, unauthorized editing, or process overwriting in historical data is calculated, and the number of risk-marked users corresponding to the anomalies is used as a reference sample. Probabilistic statistics are performed on the sample set to determine the sensitivity range of the number of marked users to commissioning risks. The lower limit of this sensitivity range is used as a candidate value for the threshold of the number of marked users, and combined with the maximum concurrent user capacity of the current commissioning platform, the threshold of the number of marked users is obtained through proportional conversion.

[0034] In the collaborative constraint mechanism, a preset lock duration is allocated for the current orchestration process sequence, and the lock duration is written into the process status record to ensure that the current orchestration process sequence is in a controlled state within the lock duration. The process status record is used to record and manage a structured data set of the execution status of the current orchestration process sequence in the debugging platform.

[0035] During the locked period, the debugging task data is monitored in real time. The debugging task data is the total number of all debugging tasks corresponding to the current orchestration process sequence accumulated during the locked period. It is updated in real time as the locked period progresses and is used to reflect the overall status and trend of the debugging tasks of the current orchestration process sequence during the locked period.

[0036] In step S4, when monitoring debugging task data in real time, that is, the total number of debugging tasks corresponding to the current orchestration process sequence accumulated within the locked duration, when it is detected that the total number of debugging tasks has increased compared to the previous monitoring period, it is determined that the new debugging tasks initiated by online users within the locked duration and not included in the current orchestration process sequence are emergency debugging tasks.

[0037] When a new emergency debugging task is detected, the source attributes of the emergency debugging task are called, which include the identity of the user who initiated the task, the type of professional role, the current role status, whether the user has been marked as a high-risk user, and whether the user is under a collaborative constraint mechanism.

[0038] Specifically, by tracing back the user operation logs, the operation record of creating a new emergency debugging task is located, the identity identifier of the executing user is extracted, and the user's professional role type, real-time role status, whether they have been marked as a high-risk user, and whether they are within the scope of the triggered collaborative constraint mechanism are queried in the current debugging scenario.

[0039] After obtaining the source attributes of the emergency debugging task, different handling measures are taken for online users based on cross-role information: First, determine whether the online users in the source information of the emergency debugging task have been marked as high-risk and whether they are under the control of the collaborative constraint mechanism triggered by the number of marked users reaching a threshold. If the online users meet the above conditions, it is determined that the cross-role operation behavior of the online users has threatened the stability and sequence controllability of the current debugging process. At this time, the debugging permission lock operation is performed, that is, the online users' operation permissions such as editing and issuing instructions in the current orchestration process sequence are suspended, and their status is forcibly set to read-only mode. At the same time, a high-risk operation alarm is sent to the debugging platform administrator. The debugging permission lock operation continues until the lock time expires, or is manually lifted by the debugging platform administrator to ensure that the core debugging process is not unexpectedly disturbed during the risk window period.

[0040] If the user originating from the emergency debugging task is not marked as high-risk or the collaborative constraint mechanism has not been triggered, the operational risk is deemed controllable. In this case, the emergency debugging task reassignment process begins. Specifically, based on the number of online roles and the responsibilities of each professional role as counted in step S1, combined with the execution progress of the current orchestration sequence, the emergency debugging task is dynamically assigned to the most suitable online user who is currently participating. The assignment logic prioritizes professional roles matching the nature of the task and avoids assigning tasks to online users marked as high-risk. After the task is reassigned, the orchestration sequence is updated, and this reassignment event is recorded for auditing purposes.

[0041] Through the above process, while responding to sudden debugging needs, the process order and risk isolation in a multi-role collaborative environment are maintained, achieving a balance between flexible handling of debugging tasks and stable process control.

[0042] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0043] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0044] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0045] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0046] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A remote commissioning method for secondary equipment in intelligent substations based on digital twins, characterized in that: Includes the following steps: Step S1: In the remote commissioning of secondary equipment in intelligent substations, obtain the sequence of commissioning task arrangement, obtain the role information of online users on the current commissioning platform through the user login interface, count the number of online roles based on the role information, and judge the commissioning scenario. Step S2: When debugging the scenario as a parallel scenario, set a parallel analysis period, count the number of role operations of online users and calculate the user operation frequency during the parallel analysis period, collect the number of role changes in the orchestration process sequence, and generate the role mismatch risk factor of online users in combination with the user operation frequency. In step S2, the role change frequency and user operation frequency are standardized to obtain the role change coefficient and user operation coefficient, respectively. Calculate the online user role mismatch risk factor based on role change coefficient and user operation coefficient: ,in, and To preset the adjustment weight, For the change coefficient of the character, Here, e is the user operation coefficient, and e is the natural constant. Risk factors for role mismatch; Step S3: Mark online users based on role mismatch risk factors and generate cross-role information. After marking online users, trigger the collaborative constraint mechanism. Set the lock duration of the current orchestration process sequence in the collaborative constraint mechanism. Monitor the debugging task data in real time within the lock duration. In step S3, cross-role information is a single tagged data used to characterize the permission boundary offset between online users and multiple business roles; After marking online users, count the number of marked online users and use the number of marked online users as the marked user count value; When the number of marked users is greater than or equal to the preset threshold for the number of marked users, the collaborative constraint mechanism is triggered; otherwise, the collaborative constraint mechanism is not activated. Step S4: Capture emergency debugging tasks based on debugging task data, call the source attribute of the emergency debugging task, and combine cross-role information to select whether to lock the debugging permissions of online users or to reassign the emergency debugging task.

2. The remote commissioning method for secondary equipment in a smart substation based on digital twins according to claim 1, characterized in that: In step S1, the orchestration sequence of the debugging tasks is obtained through the debugging task information database. The orchestration sequence refers to the execution order structure generated by the debugging platform based on the device under test, the debugging strategy and the field requirements, which consists of multiple debugging tasks in the order of execution. The role information of online users on the current debugging platform is obtained through the user login interface. The user information includes professional role type, current role status, and current debugging task.

3. The remote commissioning method for secondary equipment in a smart substation based on digital twins according to claim 2, characterized in that: In step S1, under the current debugging task, the number of each professional role type with the role status of "participation" is counted and used as the number of online roles; The debugging scenario is determined based on the distribution of roles: if the number of online roles in the current debugging task is greater than 1, then the debugging scenario is determined to be a parallel debugging scenario. If the number of online characters is equal to 1, then the debugging scenario is determined to be a serial debugging scenario.

4. The remote commissioning method for secondary equipment in a smart substation based on digital twins according to claim 1, characterized in that: In step S2, when the debugging scenario is a parallel scenario, a parallel analysis period is preset. During the parallel analysis period, the number of role operations of each online user is obtained through the user operation log. The number of role operations refers to the cumulative number of times the online user performs debugging task operations. The ratio of the number of character operations to the parallel analysis period is used as the user operation frequency; Collect operation records of the orchestration process sequence through user operation logs, and compare the professional role types in two adjacent operation records; When two adjacent operation records have different professional role types, it is determined that an operation switch has occurred between the online user and other different professional role types, and this operation switch is counted as a role change for the online user. The number of times online users changed roles within the parallel analysis period is counted as the number of times online users changed roles.

5. The remote commissioning method for secondary equipment in a smart substation based on digital twins according to claim 1, characterized in that: In step S3, cluster analysis is performed based on role mismatch risk factors to label online users: The risk factor of role mismatch is used as the input feature for clustering to construct a cluster dataset, and then a binary clustering algorithm is called to analyze the cluster dataset; The binary clustering algorithm iteratively calculates the Euclidean distance between the role mismatch risk factor of each online user and the two cluster centers, and uses the minimum distance criterion to assign online users to the corresponding cluster centers; After clustering is completed, the mean of the role mismatch risk factor of the two clusters is calculated. The cluster with the higher mean is taken as the high-risk category, and online users in it are screened out and labeled for risk.

6. The remote commissioning method for secondary equipment in a smart substation based on digital twins according to claim 1, characterized in that: In step S3, online users are marked according to the role mismatch risk factor, and cross-role information of the corresponding online users is generated; In the collaborative constraint mechanism, a preset lock duration is allocated for the current orchestration process sequence, and the lock duration is written into the process status record to keep the current orchestration process sequence under control. During the locked period, the system monitors the debugging task data in real time. The debugging task data is the total number of all debugging tasks corresponding to the current orchestration process sequence accumulated during the locked period.

7. The remote commissioning method for secondary equipment in a smart substation based on digital twins according to claim 1, characterized in that: In step S4, when the total number of debugging tasks is detected to have increased compared to the previous monitoring period, it is determined that the new debugging tasks initiated by online users within the lockout period and not included in the current orchestration process sequence are emergency debugging tasks. When a new emergency debugging task is detected, the source attributes of the emergency debugging task are called, including the identity of the user who initiated the task, the type of professional role, the current role status, whether it has been marked as a high-risk user, and whether it is under a collaborative constraint mechanism, etc.

8. The remote commissioning method for secondary equipment in a smart substation based on digital twins according to claim 1, characterized in that: In step S4, after obtaining the source attribute of the emergency debugging task, different handling measures are taken for online users based on cross-role information: If the online users in the source information of the emergency debugging task are marked as high-risk and are under the control of the collaborative constraint mechanism triggered by the number of marked users reaching a threshold, then the debugging permission will be locked. If the source user of the emergency debugging task is not marked as high-risk or the collaborative constraint mechanism is not triggered, the emergency debugging task will proceed to a secondary allocation process.

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