Power grid data collaborative editing method and system

Abstract

The invention discloses a power grid data collaborative editing method and system, and relates to the technical field of power grid information communication, and the method comprises the steps: collecting and analyzing historical operation data of a user, and designing a caching mechanism according to the historical operation data; determining a cache priority score through the cache mechanism, and performing data caching and preloading according to the priority score; and performing power grid data collaborative editing based on the cache mechanism and the operation conversion algorithm, and processing conflicts occurring in the collaborative editing process. By establishing the centralized cloud data center and adopting the distributed database technology, timely updating and rapid processing of data are ensured. A data compression algorithm and an intelligent caching mechanism of the client greatly reduce local storage requirements, and meanwhile, the data access speed is increased. The system also considers the relevance of the data and the actual use frequency of the user, and dynamically adjusts the cache priority. The real-time collaborative editing function ensures the data consistency when multiple users edit at the same time based on an operation conversion algorithm.

Description

Technical Field

[0001] The present invention relates to the field of power grid information communication technology, and in particular to a method and system for collaborative editing of power grid data. Background Art

[0002] In order to fully and effectively support the effective management of power grid resource information by units and departments at all levels, it is necessary to build power grid resource maintenance applications covering the State Grid Corporation, provincial companies, and municipal companies. After the completion of infrastructure projects, technical renovation and overhaul projects, user projects, and other projects, operation and maintenance personnel and dispatching personnel must update the relevant equipment graphics and ledger information to achieve integrated maintenance of the graphics, topology, and equipment ledgers of power grid equipment and facilities, and realize the horizontal and vertical "one network" of the entire company's power grid. Operation and maintenance personnel need to maintain all equipment information managed by the operation and maintenance department, realize the interconnection of equipment ledgers and assets, support asset lifecycle management, realize the interconnection of power grid and customer information, and support comprehensive applications such as fault repair and line loss refinement.

[0003] Most of the existing version management methods are based on stand-alone and task isolation, that is, the client downloads a copy of the entire network data and stores it in the local database. The user performs graphic editing based on the local data. After the local editing is completed, the complex version data is merged and written to the database during the submission process. In this way, as the power grid data grows, the local file is too large, resulting in lower and lower data query performance, and serious graphics roaming performance jams. In multi-task submission, the data merging process is too complicated, which often results in the submitted data being visible only the next day. In addition, the tasks are completely isolated, and it is impossible to achieve multi-person collaborative editing, display and publishing, which reduces work efficiency. Summary of the invention

[0004] In view of the problems existing in the existing management methods, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that with the growth of power grid data, local files are too large, resulting in lower and lower data query performance, serious graphics roaming performance lag, and in multi-task submission, the data merging link is too complicated, which often results in the submitted data being seen only the next day, and the tasks are completely isolated, making it impossible to achieve collaborative editing, display and publishing by multiple people, thereby reducing work efficiency.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for collaboratively editing power grid data, which includes:

[0008] Collect and analyze user historical operation data, and design a cache mechanism based on the historical operation data;

[0009] Determine a cache priority score through the cache mechanism, and cache and preload data according to the priority score;

[0010] Based on the cache mechanism and operation conversion algorithm, collaborative editing of power grid data is performed, and conflicts arising during the collaborative editing process are handled.

[0011] As a preferred solution of the method for collaborative editing of power grid data of the present invention, the cache mechanism determines the cache priority score according to the user's role, current time and region, as shown in the following formula:

[0012]

[0013] Where C(d) is the cache priority score of data d, N is the number of user behavior features, and w i is the weight of the i-th feature, RF i (d) is the predicted probability of the random forest model for data d based on the i-th feature, λ is the time decay factor, t i is the time from the last update of feature i to now, M is the number of data feature dimensions, S j is the importance score of the jth data feature, L j (d) is the scoring function of data d on the jth feature, α is the correlation influence factor, R j is the correlation between data d and other cached data on the jth feature, β is the user preference sensitivity parameter, U(d) is the user's usage frequency of data d, and θ is the usage frequency threshold.

[0014] As a preferred solution of the method for collaboratively editing power grid data of the present invention, the determination of the importance score of the data characteristic includes:

[0015] Determine the sum of the weighted importance of each data feature under each factor;

[0016] Determine the sum of the weighted importance of all data features under all factors;

[0017] The sum of the weighted importance of each data feature under each factor is divided by the weighted importance of all data features under all factors to obtain a preliminary relative importance score;

[0018] The relative importance scores are adjusted using the historical volatility of data characteristics to obtain the final importance score.

[0019] As a preferred solution of the method for collaborative editing of power grid data of the present invention, the sum of the weighted importance of each data characteristic under various factors is determined as shown in the following formula:

[0020]

[0021] The weighted importance of all data characteristics under all factors is determined as follows:

[0022]

[0023] The final importance score is determined as follows:

[0024]

[0025] Where K is the number of factors considered, wk is the weight of the kth factor, reflecting the importance of the factor in the overall evaluation, and I k (j) is the importance score of the kth factor to the jth data feature, obtained through historical data analysis, E j is the urgency of the jth data feature, M is the total number of data features, γ is the volatility factor, V j is the historical volatility of the j-th data feature.

[0026] As a preferred solution of the method for collaboratively editing power grid data of the present invention, the collaborative editing of power grid data according to the operation conversion algorithm includes:

[0027] Record the editing operation sequence of multiple users;

[0028] Transform operations to ensure consistent results when applied in different orders;

[0029] Take appropriate handling strategies based on the degree of conflict in the operation.

[0030] As a preferred solution of the method for collaborative editing of power grid data of the present invention, the processing strategy includes:

[0031] When two users edit a certain power grid parameter at the same time, it is determined whether the two operations modify different attributes. If they do, there is no conflict. The two operations are applied in the order in which they are received, and the two user interfaces are updated in real time using the cache mechanism to display the latest power grid information. If it is determined that there is a potential conflict between the two operations, the two operations are applied and a warning is issued at the same time, prompting the two users to verify whether the current latest data matches. When there is a serious conflict between the operations of the two users, that is, the two operations cannot be executed at the same time, a conflict warning is issued to the two users, informing them of the specific reason.

[0032] When the operations of two users overlap but do not conflict, the modification operation is applied first, and the intelligent cache mechanism is used to update the user interface of the modification operation before adding. If the added content conflicts with the modified power grid data, the user is prompted and the conflicting part is highlighted;

[0033] When the operation of one of two users includes the operation of the other, that is, one is an overall operation and the other is a specific operation, the overall operation will be applied first, and then the specific operation will be performed, and the user performing the overall operation will be prompted. If the included operation conflicts with the overall operation, the specific operation will not be applied, and the user performing the specific operation will be prompted to confirm.

[0034] As a preferred solution of the method for collaborative editing of power grid data of the present invention, wherein: data caching and preloading according to the priority score includes:

[0035] Sort the data items in descending order according to the calculated priority scores, select the first K data items for caching, store the highest priority data in memory, store the second highest priority data on the local hard disk, and add the remaining high priority data to the preloading queue and pre-fetch it from the cloud when idle.

[0036] In a second aspect, an embodiment of the present invention provides a power grid data collaborative editing system, which includes:

[0037] Data acquisition module, used to collect and analyze user historical operation data;

[0038] A cache module, used to build a cache mechanism according to the historical operation data, determine a cache priority score through the cache mechanism, and cache and preload data according to the priority score;

[0039] The collaborative editing module is used to perform collaborative editing of power grid data based on the cache mechanism and operation conversion algorithm, and to handle conflicts arising during the collaborative editing process.

[0040] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned collaborative editing method for power grid data is implemented.

[0041] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned method for collaborative editing of power grid data is implemented.

[0042] The beneficial effect of the present invention is that by establishing a centralized cloud data center and adopting distributed database technology, timely updating and rapid processing of data are ensured. The data compression algorithm and intelligent caching mechanism of the client greatly reduce the local storage requirements, while improving the data access speed. In particular, the intelligent caching mechanism uses random forest models, multi-dimensional data prediction and dynamic loading strategies to accurately preload data according to user behavior, data characteristics and timeliness. The system also takes into account the relevance of the data and the actual frequency of use of the user, and dynamically adjusts the cache priority. In addition, the real-time collaborative editing function is based on the operation conversion algorithm to ensure data consistency when multiple users are editing at the same time. The combination of these technologies not only significantly improves system performance and solves the problem of jamming, but also ensures the timeliness, integrity and consistency of data, providing strong technical support for power grid management. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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 work. Among them:

[0044] Figure 1 Scenario graph of the collaborative editing method for power grid data. DETAILED DESCRIPTION

[0045] 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.

[0046] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0047] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0048] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0049] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0050] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] Example 1

[0052] Reference Figure 1 , which is the first embodiment of the present invention, and provides a method for collaborative editing of power grid data, comprising:

[0053] Establish a centralized cloud data center to store the grid resource information of the entire network and ensure that the data of all levels of units (State Grid Corporation, provincial companies, and municipal companies) are updated in a timely manner. Use distributed database technology to improve data processing capabilities and access speed.

[0054] Furthermore, the client uses a data compression algorithm to save only key metadata and user personalized settings locally, greatly reducing local storage requirements. At the same time, a set of intelligent caching mechanisms is implemented, which can dynamically predict and load the required data based on the user's current working area (such as a specific geographical range or grid level) and specific task type (such as equipment maintenance, fault diagnosis, etc.).

[0055] Preferably, the intelligent cache mechanism adopts a multi-dimensional data prediction and dynamic loading strategy. First, the system establishes a user behavior model by collecting and analyzing the user's historical operation data, including commonly used work areas, task types, operation sequences and other information.

[0056] When a user logs into the system, the intelligent caching mechanism immediately makes a preliminary prediction of the required data set based on the user's role (such as operation and maintenance personnel, dispatcher) and the current time (taking into account that different time periods may have different work priorities). As the user begins to work, the system tracks the user's operations in real time, such as opening a power grid map of a specific area, viewing information on a certain type of equipment, etc., and continuously updates the prediction model.

[0057] For example, if a user frequently accesses substation information in a certain geographic area, the system will automatically preload the line and equipment data around the area. If a user is performing equipment maintenance tasks, the system will prioritize caching the equipment's historical maintenance records, technical parameters, and related drawings. For fault diagnosis tasks, the system will preload real-time operating data and historical fault information for the relevant area.

[0058] The intelligent cache mechanism also takes into account the timeliness and relevance of data. For data with high real-time requirements, such as the power grid operation status, the system will set a shorter cache update cycle. For interrelated data, such as a line and its connected substation information, the system will preload them together to improve the consistency of data access.

[0059] Specifically, the intelligent caching mechanism can be expressed in the form of a mathematical model. Specifically, random forests are used to predict the data required by users:

[0060]

[0061] Among them, P(d) represents the probability that the predicted data d is needed, T is the number of decision trees, and x represents the user behavior characteristics.

[0062] Since multiple user behaviors are required and the timeliness of data is important, user behaviors and timeliness are added to the random forest model, and weights are introduced for user behaviors. The corresponding data that is cached faster is selected based on the importance of the behavioral features, as follows:

[0063]

[0064] Among them, w i is the behavioral feature weight, RF i (d) represents the random forest model based on the i-th feature, e - λ ti is the time decay factor, which reduces the weight of old data.

[0065] Furthermore, considering different characteristics of data, such as equipment maintenance records, technical parameters, etc., the importance of characteristics and the score of data on specific characteristics are used to evaluate the importance of current data for the current user, so as to determine whether to cache it first. Specifically, as shown in the following formula:

[0066]

[0067] Finally, find the data that the current user needs to cache first, preload its related data together, and add the part that continuously updates the model, that is, dynamically adjust the priority of cached data based on the actual frequency of data use by the user. Specifically:

[0068]

[0069] Where C(d) is the cache priority score of data d, N is the number of user behavior features, and w i is the weight of the i-th feature, RF i (d) is the predicted probability of the random forest model for data d based on the i-th feature, λ is the time decay factor, t i is the time from the last update of feature i to now, M is the number of data feature dimensions, S j is the importance score of the jth data feature, L j (d) is the scoring function of data d on the jth feature, α is the correlation influence factor, R j is the correlation between data d and other cached data on the jth feature, β is the user preference sensitivity parameter, U(d) is the user's usage frequency of data d, and θ is the usage frequency threshold.

[0070] Preferably, S j The calculation includes, for each data feature j, calculating the sum of its weighted importance under each factor k:

[0071]

[0072] Then calculate the sum of the weighted importance of all data features under all factors:

[0073]

[0074] The sum of the individual weighted importance is divided by the sum of the weighted importance of all factors to obtain a preliminary relative importance score. Taking into account the historical volatility of the data characteristics, the relative importance score is adjusted to finally obtain the importance score of the jth data characteristic:

[0075]

[0076] Where K is the number of factors considered, wk is the weight of the kth factor, reflecting the importance of the factor in the overall evaluation, and I k (j) is the importance score of the kth factor to the jth data feature, obtained through historical data analysis, E jis the urgency of the jth data feature, M is the total number of data features, γ is the volatility factor, V j is the historical volatility of the j-th data feature.

[0077] More specifically, R j and L j (d) With S j The calculation of is similar and will not be described in detail.

[0078] For each data item, use the formula to calculate its cache priority score, sort all data items in descending order according to the calculated priority score, select the first K data items from the top of the sorted list for caching, and store the highest priority data in memory for fast access, store the second highest priority data on the local hard disk, and add the remaining high priority data to the preload queue and pre-fetch from the cloud when the system is idle. This can greatly improve graphics roaming performance, solve the problem of jamming, and ensure the timeliness and integrity of data.

[0079] Furthermore, the intelligent caching mechanism ensures data consistency during the collaborative editing process. When multiple users edit the power grid data in the same area at the same time, the intelligent cache can quickly synchronize the latest data status, allowing all users to operate based on the latest data.

[0080] Specifically, the real-time collaborative editing function based on the operation conversion algorithm converts the user's editing operations into a form that can be correctly applied on this shared state. Specifically, when user A and user B edit the document at the same time, the system will record the operation sequence of the two users. Then, the system will convert these operations so that no matter in what order they are applied to the document, the final result is consistent.

[0081] Preferably, when two users edit the parameters of a certain power grid equipment at the same time, the operation conversion algorithm determines whether the two operations modify different attributes. If they are different attributes, there is no conflict. The two operations are applied in the order of reception, and the two user interfaces are updated in real time using the intelligent caching mechanism to display the latest substation information. If it is determined that there is a potential conflict between the two operations, the two operations are applied, and the two user interfaces are updated in real time using the intelligent caching mechanism, and a warning is issued at the same time, prompting the two users to verify whether the current latest data matches. When there is a serious conflict between the operations of the two users, that is, the two operations cannot be executed at the same time, a conflict warning is issued to the two users, informing them of the specific reason.

[0082] When the operations of two users overlap but do not conflict, the modification operation is applied first, and the intelligent cache mechanism is used to update the user interface of the modification operation before adding. If the added content conflicts with the modified power grid data, the user is prompted and the conflicting part is highlighted;

[0083] When the operation of one of two users includes the operation of the other, that is, one is an overall operation and the other is a specific operation, the overall operation will be applied first, and then the specific operation will be performed, and the user performing the overall operation will be prompted. If the included operation conflicts with the overall operation, the specific operation will not be applied, and the user performing the specific operation will be prompted to confirm.

[0084] In summary, real-time collaborative editing requires a lot of system resources to handle concurrent operations and conflict detection. Smart caching reduces unnecessary data transmission and processing by optimizing data storage and access, thereby freeing up more system resources to support the core functions of collaborative editing and conflict resolution.

[0085] Furthermore, this embodiment also provides a power grid data collaborative editing system, including:

[0086] Data acquisition module, used to collect and analyze user historical operation data;

[0087] A cache module, used to build a cache mechanism according to the historical operation data, determine a cache priority score through the cache mechanism, and cache and preload data according to the priority score;

[0088] The collaborative editing module is used to perform collaborative editing of power grid data based on the cache mechanism and operation conversion algorithm, and to handle conflicts arising during the collaborative editing process.

[0089] This embodiment also provides a computer device, which is suitable for the collaborative editing method of power grid data, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the collaborative editing method of power grid data proposed in the above embodiment.

[0090] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. 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. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0091] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for collaboratively editing power grid data as proposed in the above embodiment is implemented.

[0092] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0093] Example 2

[0094] This is the second embodiment of the present invention. This embodiment provides a method for collaborative editing of power grid data. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0095] In order to verify the effectiveness of the intelligent caching mechanism in the power grid data management system, the research team conducted a comprehensive experiment. The experimental environment includes a simulated large-scale power grid data center, covering national, provincial and municipal power grid resource information. The experiment used 100 high-performance servers to build a centralized cloud data center and used Apache Cassandra as the distributed database system.

[0096] The client uses an adaptive data compression algorithm based on Huffman coding, and implements an intelligent caching mechanism based on the random forest model. This mechanism uses multi-dimensional data prediction and dynamic loading strategies to accurately preload data based on user behavior, data characteristics, and timeliness.

[0097] During the experiment, the daily operations of 1,000 users with different roles (including operation and maintenance personnel, dispatchers, etc.) were simulated within 30 days. The system recorded information such as the user's work area, task type, and operation sequence to build a user behavior model. The research team paid special attention to key indicators such as data access speed, local storage requirements, system response time, and collaborative editing efficiency.

[0098] In order to comprehensively evaluate the system performance, the experiment set up six different scenarios: traditional system (without smart cache), basic smart cache (considering only user roles), enhanced smart cache (considering user roles and time factors), advanced smart cache (adding data correlation analysis), complete smart cache (including all optimization strategies) and ideal situation (assuming perfect prediction). In each scenario, the system randomly selected 100 users for a 5-day test and recorded various performance indicators.

[0099] The following are the experimental data results:

[0100] Table 1 Experimental data table

[0101]

[0102]

[0103] By comparing the experimental data, it can be clearly seen that the intelligent caching mechanism has significantly improved system performance in many aspects, demonstrating significant innovation and advantages.

[0104] First, in terms of average data access time, it has been gradually reduced from 210 milliseconds in the traditional system to 90 milliseconds in the complete intelligent cache system, a reduction of 57.1%. This significant improvement is mainly due to the multi-dimensional data prediction and dynamic loading strategy of the intelligent cache mechanism, which enables the system to accurately predict the data required by users and load it in advance, greatly reducing data retrieval and transmission time.

[0105] Secondly, local storage requirements were reduced from 45.2GB to 18.3GB, a reduction of 59.5%. This achievement is due to the synergy of the data compression algorithm and intelligent caching mechanism adopted by the client. By only saving key metadata and user personalized settings locally, while intelligently predicting and loading required data, the system significantly reduces redundant data storage, saving storage space and improving data management efficiency.

[0106] The system response time was shortened from 3.5 seconds to 1.4 seconds, an increase of 60%. This improvement directly reflects the excellent performance of the intelligent caching mechanism in improving user experience. By preloading the data that may be needed, the system greatly reduces the waiting time and makes user operation smoother, especially when dealing with complex tasks such as equipment maintenance and fault diagnosis.

[0107] The conflict rate of collaborative editing has been reduced from 8.7% to 3.2%, a reduction of 63.2%. This significant improvement is attributed to the combination of intelligent caching mechanism and real-time collaborative editing function. By quickly synchronizing the latest data status, the system greatly reduces the probability of conflict when multiple users are editing at the same time and improves the efficiency of collaboration. In particular, the system performs well when dealing with high-concurrency editing tasks, such as multiple dispatchers updating power grid operating parameters at the same time.

[0108] It is worth noting that the complete intelligent cache system is close to the ideal situation in all indicators, which fully proves the advancement and practicality of the present invention. For example, in terms of cache hit rate, the complete intelligent cache system reached 89%, which is only 6 percentage points lower than the ideal situation, showing the high efficiency of the system in practical applications.

[0109] In addition, by comparing different levels of intelligent caching systems, it can be seen that each step of optimization brings obvious performance improvement. In particular, the transition from advanced intelligent caching to complete intelligent caching has significant improvements in various indicators, which proves the importance of considering factors such as data relevance, timeliness, and actual user usage frequency in the present invention.

[0110] In general, this intelligent caching mechanism successfully solves the problems faced by traditional power grid data management systems, such as slow data access, large local storage requirements, slow system response, and frequent collaborative editing conflicts, by integrating multi-dimensional data analysis and prediction strategies. It not only improves the overall performance of the system, but also greatly improves the user experience, providing strong technical support for efficient management and collaborative operation of power grid data. This innovative solution has broad application prospects in large-scale data management and real-time collaboration in the power industry.

[0111] 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.

Claims

1. A method for collaborative editing of power grid data, Features: include, Collect and analyze user historical operation data, and design a cache mechanism based on the historical operation data; Determine a cache priority score through the cache mechanism, and cache and preload data according to the priority score; Based on the cache mechanism and operation conversion algorithm, collaborative editing of power grid data is performed, and conflicts arising during the collaborative editing process are handled.

2. The method for collaboratively editing power grid data according to claim 1, Features: The cache mechanism determines the cache priority score based on the user's role, current time and region, as shown in the following formula: Where C(d) is the cache priority score of data d, N is the number of user behavior features, and w i is the weight of the i-th feature, RF i (d) is the predicted probability of the random forest model for data d based on the i-th feature, λ is the time decay factor, t i is the time from the last update of feature i to now, M is the number of data feature dimensions, S j is the importance score of the jth data feature, L j (d) is the scoring function of data d on the jth feature, α is the correlation influence factor, R j is the correlation between data d and other cached data on the jth feature, β is the user preference sensitivity parameter, U(d) is the user's usage frequency of data d, and θ is the usage frequency threshold.

3. The method for collaboratively editing power grid data according to claim 2, Features: Determination of the importance score of the data feature includes: Determine the sum of the weighted importance of each data feature under each factor; Determine the sum of the weighted importance of all data features under all factors; The sum of the weighted importance of each data feature under each factor is divided by the weighted importance of all data features under all factors to obtain a preliminary relative importance score; The relative importance scores are adjusted using the historical volatility of data characteristics to obtain the final importance score.

4. The method for collaboratively editing power grid data according to claim 3, Features: The determination of the sum of weighted importance of each data feature under various factors is shown in the following formula: The weighted importance of all data characteristics under all factors is determined as follows: The final importance score is determined as follows: Where K is the number of factors considered, wk is the weight of the kth factor, reflecting the importance of the factor in the overall evaluation, and I k (j) is the importance score of the kth factor to the jth data feature, obtained through historical data analysis, E j is the urgency of the jth data feature, M is the total number of data features, γ is the volatility factor, V j is the historical volatility of the j-th data feature.

5. The method for collaboratively editing power grid data according to claim 4, Features: The grid data is collaboratively edited according to the operation conversion algorithm including: Record the editing operation sequence of multiple users; Transform operations to ensure consistent results when applied in different orders; Take appropriate handling strategies based on the degree of conflict in the operation.

6. The method for collaboratively editing power grid data according to claim 5, Features: The treatment strategy includes: When two users edit a certain power grid parameter at the same time, it is determined whether the two operations modify different attributes. If they do, there is no conflict. The two operations are applied in the order in which they are received, and the two user interfaces are updated in real time using the cache mechanism to display the latest power grid information. If it is determined that there is a potential conflict between the two operations, the two operations are applied and a warning is issued at the same time, prompting the two users to verify whether the current latest data matches. When there is a serious conflict between the operations of the two users, that is, the two operations cannot be executed at the same time, a conflict warning is issued to the two users, informing them of the specific reason. When the operations of two users overlap but do not conflict, the modification operation is applied first, and the intelligent cache mechanism is used to update the user interface of the modification operation before adding. If the added content conflicts with the modified power grid data, the user is prompted and the conflicting part is highlighted; When the operation of one of two users includes the operation of the other, that is, one is an overall operation and the other is a specific operation, the overall operation will be applied first, and then the specific operation will be performed, and the user performing the overall operation will be prompted. If the included operation conflicts with the overall operation, the specific operation will not be applied, and the user performing the specific operation will be prompted to confirm.

7. The method for collaboratively editing power grid data according to claim 5, Features: Data caching and preloading according to the priority score includes, Sort the data items in descending order according to the calculated priority scores, select the first K data items for caching, store the highest priority data in memory, store the second highest priority data on the local hard disk, and add the remaining high priority data to the preloading queue and pre-fetch it from the cloud when idle.

8. A power grid data collaborative editing system, based on the power grid data collaborative editing method according to any one of claims 1 to 7, Features: include, Data acquisition module, used to collect and analyze user historical operation data; A cache module, used to build a cache mechanism according to the historical operation data, determine a cache priority score through the cache mechanism, and cache and preload data according to the priority score; The collaborative editing module is used to perform collaborative editing of power grid data based on the cache mechanism and operation conversion algorithm, and to handle conflicts arising during the collaborative editing process.

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

10. A computer-readable storage medium having a computer program stored thereon, Features: When the computer program is executed by a processor, the steps of the method for collaborative editing of power grid data according to any one of claims 1 to 7 are implemented.

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