A method and terminal for utilizing a knowledge graph to digitize a power system

Abstract

The application provides a method and a terminal for a knowledge graph intelligent power system, business field nodes and business arcs are constructed according to business data, the business field nodes include start time, and the business arcs include execution time; the earliest possible start time and the latest must finish time corresponding to each business arc are calculated according to the start time and the execution time; a process time difference is obtained according to the earliest possible start time and the latest must finish time, and the business arc with the process time difference of 0 is marked as a key business arc; the state of the business is recorded in the form of the business field nodes, the business process is recorded in the form of the business arcs, the state of the business is changed through different business arcs, and the corresponding start time is set for each business field node, the key business is monitored, and the knowledge graph is used in the actual application of the power system.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more particularly to a method and terminal for digitizing a power system using knowledge graphs. Background Technology

[0002] With the development of smart grids, digital technology is playing an increasingly important role in the power industry, especially in system planning, particularly in the exploration and construction of new power system concepts.

[0003] The data types in power grids are complex, and the relationships between them are diverse. Current technologies typically use graphs to describe them. A graph is a gridded data structure composed of nodes and connections between them. Nodes can be assigned to represent any object, and nodes of the same type should ideally represent a category of objects. The connections between nodes represent relationships between them, which can be all-encompassing. The most common relationships include those between objects and their attributes, as well as spatial and temporal relationships between objects. Graphs allow us to view whether any node has relationships, analyze all relationships of a node, and statistically analyze central and important nodes. However, current graph descriptions often stop at data organization and simple queries, failing to fully utilize the advantages of graphs. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method and terminal for digitizing power systems using knowledge graphs, so as to realize the use of knowledge graphs in practical applications of power systems.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] A method for digitizing a power system using knowledge graphs includes the following steps:

[0007] Business field nodes and business arcs are constructed based on business data. The business field nodes include a start time, and the business arcs include an execution time.

[0008] Calculate the earliest start time and the latest completion time for each business arc based on the start time and the execution time.

[0009] The process time difference is obtained based on the earliest start time and the latest completion time, and the business arc with a process time difference of 0 is marked as a critical business arc.

[0010] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0011] A terminal for a digital power system utilizing knowledge graphs includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps:

[0012] Business field nodes and business arcs are constructed based on business data. The business field nodes include a start time, and the business arcs include an execution time.

[0013] Calculate the earliest start time and the latest completion time for each business arc based on the start time and the execution time.

[0014] The process time difference is obtained based on the earliest start time and the latest completion time, and the business arc with a process time difference of 0 is marked as a critical business arc.

[0015] The beneficial effects of this invention are as follows: the status of a business is recorded as business field nodes, and the business process is recorded as business arcs. The status of a business changes through different business arcs, and a corresponding start time is set for each business field node. The start time can be obtained according to the plan. Each business arc has a corresponding execution time. Thus, the earliest start time and the latest completion time of each business in a complete business chain can be known. In this way, business arcs with a process time difference of 0 are obtained and marked as critical business arcs. This allows the identification of critical businesses that will affect the completion of the entire goal from the complex business. Key businesses can be monitored, and knowledge graphs can be used in the actual application of power systems. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of a method for digitalizing a power system using knowledge graphs, according to an embodiment of the present invention.

[0017] Figure 2 This is a flowchart of another step in a method for digitalizing a power system using knowledge graphs, according to an embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram of a service field node-service arc connection according to an embodiment of the present invention;

[0019] Figure 4 This is a schematic diagram of the structure of a terminal for a knowledge graph-based intelligent power system according to an embodiment of the present invention;

[0020] Label Explanation:

[0021] 1. A terminal for digitalizing a power system using knowledge graphs; 2. A processor; 3. A memory. Detailed Implementation

[0022] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0023] Please refer to Figure 1 A method for digitizing a power system using knowledge graphs includes the following steps:

[0024] Business field nodes and business arcs are constructed based on business data. The business field nodes include a start time, and the business arcs include an execution time.

[0025] Calculate the earliest start time and the latest completion time for each business arc based on the start time and the execution time.

[0026] The process time difference is obtained based on the earliest start time and the latest completion time, and the business arc with a process time difference of 0 is marked as a critical business arc.

[0027] As can be seen from the above description, the beneficial effects of the present invention are as follows: the status of the business is recorded in the form of business field nodes, and the business process is recorded in the form of business arcs. The status of the business changes through different business arcs, and a corresponding start time is set for each business field node. The start time can be obtained according to the plan. The business arc has a corresponding execution time. Thus, the earliest start time and the latest completion time of each business in a complete business chain can be known. In this way, the business arc with a process time difference of 0 is obtained and marked as a key business arc. Then, the key business that will affect the completion of the whole goal can be found from the complex business. The key business can be monitored in a key manner, and the knowledge graph can be used in the actual application of the power system.

[0028] Furthermore, calculating the earliest possible start time for each business arc based on the start time and the execution time includes:

[0029] Starting from the business arc corresponding to the business field node with the earliest start time, the earliest start time corresponding to each business arc is recursively calculated.

[0030] As described above, since the earliest start time of each business arc is related to the end time of its preceding steps, the earliest start time is calculated starting from the business arc corresponding to the business field node with the earliest start time. Since the start time of the earliest business arc is determined, the earliest start time of subsequent business arcs can be calculated by recursively going down.

[0031] Furthermore, the business field node also includes a first node number, and the business arc also includes a second node number; and the first node number and the second node number increase sequentially with the execution flow.

[0032] The earliest possible start time for each business arc is calculated based on the start time and the execution time, including:

[0033] Te(i) = max{Te(h) + t1(h,i)|h <i,(h,i)εA};

[0034] In the formula, Te() represents the earliest possible start time, i represents the number of the current business arc, t1(h,i) represents the time required for business arc h to complete business arc i, A represents the set of all business arcs, and max represents finding the maximum value.

[0035] As described above, since vertex numbers are typically used in knowledge graphs, the first and second node numbers of business field nodes and business arcs are incremented according to the execution flow order, thus using the numbers as identifiers for recursive calculations. Since whether the current business arc i can start execution depends on whether the longest-running step in its preceding steps has been completed, the maximum time is used to take the completion time of the longest-running step in its preceding steps as the earliest time that the current business arc i can start, which can ensure the normal execution of business arc i after this time.

[0036] Furthermore, the step of calculating the latest required completion time for each business arc based on the start time and the execution time includes:

[0037] Starting from the service arc corresponding to the service field node with the latest start time, the latest required completion time for each service arc is recursively calculated.

[0038] As described above, the latest starting time, which is the required completion time in the entire business chain consisting of the business arc, can be understood as the total project duration. By recursively working backward from this starting point, the latest completion time of each business arc can be calculated, which means that failure to complete the arc will affect the normal completion time of the next process. This allows for precise control over the time of each business arc.

[0039] Furthermore, the step of calculating the latest required completion time for each business arc based on the start time and the execution time includes:

[0040] Tl(j)=min{Tl(k)-t2(j,k)|k>j,(j,k)εA}

[0041] In the formula, Tl() represents the latest time that must be completed, j represents the current business arc, t2(j,k) represents the time required to complete business arc j to business arc k, A represents the set of all business arcs, and min represents finding the minimum value.

[0042] As described above, due to the overall project duration limitation, the completion of each node is subject to the project duration limitation. The latest completion time is set, and failure to complete the task by this time will affect the completion of the overall project duration. Using this as a reference for controlling the completion time of business operations can ensure the completion of the overall project duration to the greatest extent possible, and clarify the time points of each operation to prevent the overall project duration from being exceeded.

[0043] Furthermore, the process time difference obtained based on the earliest possible start time and the latest required completion time includes:

[0044] Traverse all business arcs. When the target business arc is encountered, obtain the earliest start time and the latest completion time corresponding to the target business arc, and calculate the difference between the earliest start time and the latest completion time to obtain the process time difference.

[0045] As described above, for each business arc, we will eventually obtain the earliest start time and the latest completion time. Calculate the time difference of this process, which is the part that can be flexibly adjusted in the middle. If the time difference of this process is 0, it means that there is no flexible time for this process, and it needs to be focused on to avoid affecting the overall project duration.

[0046] Furthermore, the step of obtaining the process time difference based on the earliest possible start time and the latest required completion time also includes:

[0047] Receive service arc chain information, wherein the service arc chain information includes more than one service arc connected at the beginning and end;

[0048] The earliest start time of the first business arc in the business arc chain is obtained as the earliest start time of the business arc chain.

[0049] The latest possible completion time of the last business arc in the business chain is obtained to obtain the latest possible completion time of the business arc chain.

[0050] The process time difference corresponding to the business arc chain is obtained based on the earliest start time and the latest completion time of the business arc chain.

[0051] As described above, different granularities of attention can be set according to different needs in actual production. This means that the finest individual business arcs can be controlled, or multiple interconnected business arcs can be turned into a business chain for unified control, thus improving the flexibility of knowledge graph applications.

[0052] Furthermore, the process time difference, derived from the earliest possible start time and the latest required completion time, also includes:

[0053] s(i,j)=Tl(j)-Te(i)-t3(i,j)

[0054] In the formula, s(i,j) represents the time difference of the process between the business field nodes between business arcs i and j, Tl(j) represents the latest time that business arc j must be completed, Te(i) represents the earliest time that business arc i can start, and t3(i,j) represents the time required for the completion of business arc i to business arc j.

[0055] As described above, the process time difference of a business chain starting with business arc i and ending with business arc j can be calculated. This means that not only can the process time difference of a single business arc be calculated, but the monitoring granularity can also be adjusted according to requirements.

[0056] Furthermore, after marking the business arc with a process time difference of 0 as a critical business arc, the process further includes:

[0057] The critical business arcs are connected in series to obtain the critical business route;

[0058] Special prompts are set for the aforementioned critical business routes.

[0059] As described above, for business arcs with zero process time difference, critical business routes are obtained by association. Special prompts are set for critical business routes for key time monitoring. This can further utilize the association of various nodes in the knowledge graph to achieve time control and ensure the completion of the overall project schedule.

[0060] Please refer to Figure 4 A terminal for a power system using knowledge graphs for digitalization includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned method for a power system using knowledge graphs for digitalization.

[0061] The method and terminal for digitalizing power systems using knowledge graphs described above are applicable to scenarios involving the construction of power system knowledge graphs or the further utilization of existing knowledge graphs. The following is a detailed description of specific implementation methods.

[0062] Please refer to Figures 1-3 Embodiment 1 of the present invention is as follows:

[0063] A method for digitizing a power system using knowledge graphs, specifically including:

[0064] S1. Construct business field nodes and business arcs based on business data. The business field nodes include a start time, and the business arcs include an execution time.

[0065] In one optional implementation, the service field node further includes a first node number, and the service arc further includes a second node number; and the first node number and the second node number increment sequentially with the execution flow.

[0066] S2. Calculate the earliest possible start time and the latest mandatory completion time for each business arc based on the start time and the execution time, including:

[0067] S21. Starting from the business arc corresponding to the business field node with the earliest start time, recursively calculate the earliest start time corresponding to each business arc.

[0068] Te(i) = max{Te(h) + t1(h,i)|h <i,(h,i)εA};

[0069] In the formula, Te() represents the earliest possible start time, i represents the current business arc, t1(h,i) represents the time required to complete business arc h to business arc i, A represents the set of all business arcs, and max represents finding the maximum value; Te(2), Te(3),... can be calculated sequentially from Te(1)=0 to the largest value; the earliest possible start time of each business can be calculated to assist in the analysis of the planning and arrangement of new power development business;

[0070] Each business has preceding and subsequent business processes, and all have time constraints for data governance. From the graph analysis, the earliest possible start time for a business is equal to the length of the longest unidirectional chain from the overall business set to that business process. Clearly, businesses starting with the same business process (business field node) have the same earliest start time. Let Te(i) represent the earliest possible start time for business i, which is equal to the length of the longest unidirectional chain from 1 to i. Given the properties of vertex numbering in the graph, the vertex numbers in the unidirectional chain from 1 to i should all be less than i.

[0071] In one alternative implementation, please refer to Figure 3 If the total construction period is 30 days, when calculating the earliest possible start time: E / A / B are all 0, then Te(h) for C is 0, and t1(h,i) are 8 days, 10 days, and 20 days respectively. The maximum value (max) of the 20th day is taken as the earliest possible start time for C.

[0072] S22. Starting from the service arc corresponding to the service field node with the latest start time, recursively calculate the latest completion time corresponding to each service arc.

[0073] Tl(j)=min{Tl(k)-t2(j,k)|k>j,(j,k)εA}

[0074] In the formula, Tl() represents the latest required completion time, j represents the current business arc, t2(j,k) represents the time required to complete business arc j to business arc k, A represents the set of all business arcs, and min represents finding the minimum value; the total number of i is n, then Tl(n) is the total project duration, so Tl(n) = Tl(n). Starting from Tl(n), Tl(n-1), Tl(n-2), ..., Tl(2), Tl(1) can be calculated sequentially from large to small using the recursive formula;

[0075] The latest possible completion time for a given task refers to the time at which failure to complete this task will affect the completion of the entire task set. In this context, it refers to the completion status of the data governance upon which the task depends. It should equal the total duration minus the length of the longest unidirectional chain from the data governance time of this task to the total duration. Clearly, tasks with the same completion date have the same latest possible completion time. Let Tl(j) represent the latest possible completion time of task j. Due to the numbering properties of the graph, the vertex numbers on the unidirectional chain from j to the total duration are all greater than j.

[0076] In one alternative implementation, please refer to Figure 3 When calculating the latest required completion time, assuming the total project duration is 30 days, the latest required completion time for C / D, i.e., Tl(k), is 30 days, and t2(j,k) is 5 days and 7 days respectively. The minimum value (min), the 23rd day, is taken as its latest required completion time. Each business has preceding and subsequent business processes; the subsequent process cannot be executed only after all three have been completed. The latest required completion time is calculated based on several important business processes (their failure to complete will affect the entire business set), and some preceding and subsequent business processes are not strongly correlated and can be carried out in parallel.

[0077] S3. Calculate the process time difference based on the earliest possible start time and the latest required completion time, and mark the business arcs with a process time difference of 0 as critical business arcs, including:

[0078] S31. Traverse all business arcs. When the target business arc is reached, obtain the earliest start time and the latest completion time corresponding to the target business arc, and calculate the difference between the earliest start time and the latest completion time to obtain the process time difference.

[0079] s(i,j)=Tl(j)-Te(i)-t3(i,j);

[0080] In the formula, s(i,j) represents the process time difference between business field nodes between business arc i and business arc j, Tl(j) represents the latest time that business arc j must be completed, Te(i) represents the earliest time that business arc i can start, and t3(i,j) represents the time required for the completion of business arc i to business arc j.

[0081] In one optional implementation, service arc chain information is received, the service arc chain information including more than one service arc connected first and last; the earliest start time of the first service arc in the service arc chain is obtained as the earliest start time of the service arc chain; the latest completion time of the last service arc in the service chain is obtained as the latest completion time of the service arc chain; and the process time difference corresponding to the service arc chain is obtained based on the earliest start time and the latest completion time of the service arc chain.

[0082] S32. Mark the business arc with a process time difference of 0 as a critical business arc; S33. Connect the critical business arcs to obtain a critical business route; set special prompts for the critical business route.

[0083] Embodiment 2 of the present invention is as follows:

[0084] Please refer to Figure 2 A method for digitizing a power system using knowledge graphs, which differs from Embodiment 1 in that:

[0085] S1. Constructing business field nodes and business arcs based on business data includes: extracting business field nodes and business arcs according to business processes; business processes have sequential and parallel relationships; the division of business is based on the development plan of the new power system, including power analysis, load forecasting, power grid diagnosis, investment planning, project pre-planning, project whole process, project post-evaluation, etc., all of which can be bound to business arcs, forming a graph through business process links, that is, associating business field nodes with business arcs according to the process;

[0086] In one optional implementation, the business is categorized into primary categories based on development planning, including power natural resource analysis, regional energy consumption and carbon dioxide analysis, scientific division of planning areas, scientific load forecasting, digital and intelligent grid diagnosis, digital and intelligent planning operations, digital and intelligent pre-project work, digital and intelligent project management throughout the entire process, and post-project evaluation. These are described in a digital mapping sequence of business processes, progressing from simple to complex based on automation, standardization, digitalization, and intelligence, resulting in a total of five levels: Level 1 task (i.e., the primary category mentioned above), Level 2 task, Level 3 function, Level 4 support points, and Level 5 function. After a single business description is completed, all levels may not be included; that is, only Levels 1 to 3 descriptions may be included. Process subfields are decomposed according to the process of implementing business tasks and ordered according to calling relationships and temporal relationships. In other words, a single business arc includes one or more process subfields.

[0087] Step S2 includes the following:

[0088] S01. Construct knowledge nodes based on various knowledge contents; knowledge nodes are the core technologies of digitalization and intelligence in new power systems, and are the distillation of the company's core processes and core algorithms. They are assembled according to algorithms, and the core indicators include voltage, power, load rate, etc. The assembled algorithms fully refer to the temporal and spatial hierarchy and order. Knowledge points are divided into diagnostic, trend analysis, feature extraction, load analysis, time distribution analysis, spatial distribution analysis, and simulation calculation. Knowledge points have inclusion relationships and some overlap relationships.

[0089] Knowledge nodes cover mathematical knowledge, sets, graphs, electromagnetic fields, circuits, network structures, diagnostics, load, and investment knowledge, including topology, graphs, power formulas, Ohm's theorem, and power flow calculations. Each knowledge node is an iterative structure composed of knowledge units and knowledge calculation algorithms. Knowledge nodes are bound to data, which is knowledge-related data content, generally historical data, and then form a data domain. Knowledge calculations are linked according to logical processing relationships. For example, if I = U / R is a knowledge node, then the data U and R can be linked to I through division. The next layer of commonly used knowledge units and knowledge calculations at the same layer are assembled into components, which can serve as knowledge units for the upper layer, forming basic calculation components such as voltage, power, and load rate. The most basic layer is the indicator layer, with core power indicators including voltage and power. Knowledge nodes are an important part of the business process. Through the construction of a full knowledge graph, important knowledge nodes can be statistically analyzed, and business processes can be linked through knowledge nodes to analyze the degree of correlation between business processes. Core power indicators (including but not limited to voltage, power, and load rate mentioned above) are the core of the algorithm.

[0090] In one optional implementation, process nodes are composed of knowledge nodes. Process nodes are sequentially associated within each other and linked by first and last knowledge nodes, forming a business field graph together with business field nodes and business arcs. Each process subfield consists of one or more knowledge nodes. The description here uses a resource description framework, i.e., a triple of (business field, process subfield, knowledge node set), in XML format, with the knowledge node set nested within the process subfield.

[0091] S02. Construct data domain nodes based on the data values ​​corresponding to the business field nodes and the knowledge nodes. Data domains are crucial to the new power system; they provide feedback on business performance, nourish knowledge points, and serve as a reference for digital and intelligent evaluation. Data domains focus on data collection and storage, as well as data location. Data domain nodes are connected through knowledge nodes; after all knowledge nodes are connected, the data domains are aggregated to form data clusters, revealing the relationships between data domains. The importance of data can also be analyzed by the number of times they are associated, and strongly correlated data domains in the construction of the new power system can be observed.

[0092] Each business unit is associated with a data domain node, which forms the foundation for the digitalization and intelligentization of the new power system. These nodes are categorized into internal and external data. Power grid companies are crucial links in energy circulation and key participants in achieving carbon emission reduction. A significant proportion of these data domain nodes involve external data, specifically categorized as electricity trading data, spot trading data, power grid operation data, financial data, and project data. Data items within these domain nodes are created based on knowledge points, broadly classified according to business specialization, and arranged according to the development stages of the digitalization and intelligentization of the new power system. Detailed resource descriptions include data collection and storage descriptions, spatial location descriptions (e.g., where electricity meters collect user electricity consumption data), data accuracy descriptions, update frequency descriptions, format descriptions, and modal descriptions. Data quality indicators are also included, such as accuracy, efficiency, coverage, and connectivity. The descriptions of data items also include links to knowledge nodes and business field nodes, and statistical analysis of graph relationships.

[0093] The steps between S1 and S2 also include:

[0094] S03. Organize the business field nodes to form business-related knowledge nodes, and analyze the data aggregation of each knowledge node into a data domain, as shown in Table 1 below:

[0095] Table 1

[0096]

[0097] S04. Calculate the weights of the graph. The graph is a directed graph consisting of nodes, arcs, and weights. A node represents a business state, which is the start or end of one or more business fields and is the time boundary between adjacent businesses. Nodes are represented by circles and numbers inside, with the numbers indicating the node number. An arc represents a business, indicated by a business field number. A business requires certain human, material, and time resources. The weight represents the time or resources required to complete a certain business; in this paper, it is the number of days required for associated data governance.

[0098] Please refer to Figure 4 Embodiment 3 of the present invention is as follows:

[0099] A terminal 1 for a knowledge graph-based digital power system includes a processor 2, a memory 3, and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, it implements the steps in Embodiment 1.

[0100] In summary, this invention provides a method and terminal for digitalizing power systems using knowledge graphs. It comprehensively analyzes business field nodes, knowledge nodes, and node relationships using a knowledge graph structure to describe the digitalization of new power systems. Through knowledge graph technology, it abstracts business field nodes from the strategic goals of new power development, algorithm knowledge nodes from core technology algorithms for power grid diagnostic planning, and data blueprint knowledge nodes from master data and data association relationships to construct power grid knowledge business field nodes. Then, it abstracts knowledge relationships from process relationships, digital algorithms, and data links, establishing a knowledge graph from business field nodes to knowledge nodes to data domain nodes, creating a data domain associated with each business. It applies knowledge graph technology to calculate the weights of each new power development business, promoting the analysis of key business routes, thereby interpreting the development path of the new power system, effectively monitoring the progress of the project, and enabling the application of knowledge graphs in an audit environment. Through the knowledge graph, it is possible to view whether any node has relationships, analyze all relationships of a node, and statistically analyze central nodes and important nodes in the knowledge graph. The knowledge graph for the digital and intelligent analysis of new power systems is quite complex, with various types of nodes and relationships. This invention first divides the knowledge graph into domains: business domain, algorithm domain, and data domain. The node types are consistent across different domains, and there are connections between nodes within a domain and between nodes between domains. The domains cover all areas of digital and intelligent analysis of new power systems, and the nodes cover the comprehensive connotation of the development of digital and intelligent analysis of new power systems.

[0101] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for utilizing a knowledge graph to digitize a power system, characterized in that, The method comprises the steps of: constructing a business field node and a business arc according to business data, the business field node comprising a start time, and the business arc comprising an execution time; calculating an earliest possible start time and a latest must finish time corresponding to each of the business arcs according to the start time and the execution time; obtaining a process time difference according to the earliest possible start time and the latest must finish time, and marking a business arc with the process time difference of 0 as a critical business arc; the step of calculating the earliest possible start time corresponding to each of the business arcs according to the start time and the execution time comprises: starting from the business arc corresponding to the business field node with the earliest start time, recursively calculating the earliest possible start time corresponding to each of the business arcs; the step of calculating the latest must finish time corresponding to each of the business arcs according to the start time and the execution time comprises: starting from the business arc corresponding to the business field node with the latest start time, recursively calculating the latest must finish time corresponding to each of the business arcs; the step of obtaining the process time difference according to the earliest possible start time and the latest must finish time comprises: traversing all the business arcs, when a target business arc is traversed, obtaining the earliest possible start time and the latest must finish time corresponding to the target business arc, and obtaining the process time difference by subtracting the earliest possible start time from the latest must finish time; the step of obtaining the process time difference according to the earliest possible start time and the latest must finish time further comprises: receiving business arc chain information, the business arc chain information comprising a plurality of business arcs connected in sequence; obtaining the earliest possible start time of the first business arc in the business arc chain as the earliest possible start time of the business arc chain; obtaining the latest must finish time of the last business arc in the business arc chain as the latest must finish time of the business arc chain; obtaining the process time difference corresponding to the business arc chain according to the earliest possible start time and the latest must finish time of the business arc chain.

2. The method for utilizing a knowledge graph to intelligently manage a power system according to claim 1, wherein, the business field node further comprises a first node number, and the business arc further comprises a second node number; and the first node number and the second node number increase in sequence with the execution flow; the step of calculating the earliest possible start time corresponding to each of the business arcs according to the start time and the execution time comprises: Te(i) = max{Te(h)+t1(h,i)|h<i,(h,i)∈A}; wherein, Te() represents the earliest possible start time, i represents the number of the current business arc, t1(h,i) represents the time required for completing the business arc h to the business arc i, A represents a set of all business arcs, and max represents the maximum value.

3. The method for utilizing a knowledge graph to intelligently manage a power system according to claim 1, wherein, the step of calculating the latest must finish time corresponding to each of the business arcs according to the start time and the execution time comprises: Tl(j)= min {Tl(k)-t2(j,k)|k>j,(j,k)∈A} In the formula, Tl() represents the latest must finish time, j represents the number of the current service arc, t2(j, k) represents the time required for the completion of the service arc j to the service arc k, A represents the set of all service arcs, and min represents the minimum value.

4. The method for utilizing a knowledge graph to intelligently manage a power system according to claim 1, wherein, The process time difference further comprises: s(i, j) = Tl(j) - Te(i) - t3(i, j) In the formula, s(i, j) represents the process time difference of the service node between the service arc i and the service arc j, Tl(j) represents the latest must finish time of the service arc j, Te(i) represents the earliest can start time of the service arc i, and t3(i, j) represents the time required for the completion of the service arc i to the service arc j.

5. The method for utilizing a knowledge graph to intelligently manage a power system according to claim 1, wherein, The method further comprises: serially connecting the critical service arcs to obtain a critical service route; setting a prompt for the critical service route. 6.A terminal for a knowledge graph-based intelligent power system, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein, The processor executes the computer program to realize the steps in the method for intelligently utilizing a knowledge graph in a power system according to any one of claims 1-5.

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