A dynamic configuration method and system for energy router

Abstract

The present invention discloses a dynamic configuration method and system for an energy router, and relates to the technical field of routing configuration. The method comprises: receiving an energy routing task instruction of a power grid; feature recognition, obtaining new routing task feature data; loading an energy routing data source; performing routing scheduling on an energy router based on routing scheduling collaborative constraints, obtaining a new energy routing scheduling decision; performing routing configuration analysis according to an energy routing configuration channel, obtaining a new task routing configuration scheme, and generating an updated task routing configuration scheme in combination with a fixed task routing configuration scheme; continuously monitoring an energy router, obtaining a routing continuous monitoring data source, and adaptively adjusting the updated task routing configuration scheme in combination with routing dynamic constraint rules. The method solves the technical problem in the prior art that routing configuration is difficult to adjust and optimize according to real-time conditions, resulting in poor configuration flexibility, and achieves the technical effect of improving the flexibility of router configuration.

Description

Technical Field

[0001] The present invention relates to the technical field of routing configuration, and in particular to a dynamic configuration method and system for an energy router. Background Art

[0002] Energy router is an intelligent energy management device that can realize the input, output, conversion and storage of different energy carriers. It is the core device of energy Internet. Its functional features mainly include power transmission, energy management and information interaction. The configuration of energy router is of great significance for optimizing energy distribution and transmission, improving energy management efficiency, enhancing the stability and reliability of energy system, promoting the development of energy Internet and improving energy security. Through reasonable configuration, energy router can better meet energy demand and promote sustainable development in the energy field. The existing energy router configuration method cannot automatically adjust the configuration of energy router according to real-time energy demand and network status. There are problems such as poor configuration flexibility, low adaptability and poor accuracy of energy router. Summary of the invention

[0003] The embodiments of the present application provide a method and system for dynamically configuring an energy router, which solves the technical problem in the prior art that routing configuration is difficult to adjust and optimize according to real-time conditions, resulting in poor configuration flexibility.

[0004] In view of the above problems, an embodiment of the present application provides a method and system for dynamic configuration of an energy router.

[0005] A first aspect of an embodiment of the present application provides a method for dynamically configuring an energy router, the method comprising:

[0006] According to the power grid energy routing management terminal, receiving the energy routing task instruction of the power grid, wherein the energy routing task instruction includes A newly added energy routing tasks of the power grid, wherein A is a positive integer;

[0007] Perform feature identification according to the A newly added energy routing tasks to obtain feature data of the A newly added routing tasks;

[0008] According to the power grid energy routing management terminal, an energy routing data source is loaded, wherein the energy routing data source includes H energy routing data streams corresponding to H energy routers of the power grid, each energy routing data stream includes basic router data, real-time network environment information and router fixed task information corresponding to the energy router, and H is the total number of energy routers of the power grid;

[0009] Based on the routing scheduling coordination constraint, routing scheduling is performed on the H energy routers according to the A newly added routing task feature data and the energy routing data source to obtain a newly added energy routing scheduling decision;

[0010] Based on the A newly added routing task characteristic data and the newly added energy routing scheduling decision, a routing configuration analysis is performed according to a pre-built energy routing configuration channel to obtain a newly added task routing configuration scheme, and an updated task routing configuration scheme is generated in combination with a fixed task routing configuration scheme;

[0011] Based on the continuous monitoring of the H energy routers by the power grid energy routing management terminal, a routing continuous monitoring data source is obtained, and the routing configuration scheme of the update task is adaptively adjusted in combination with routing dynamic constraint rules.

[0012] A second aspect of an embodiment of the present application provides a dynamic configuration system for an energy router, the system comprising:

[0013] A receiving module, the receiving module is used to receive an energy routing task instruction of a power grid according to the power grid energy routing management terminal, wherein the energy routing task instruction includes A newly added energy routing tasks of the power grid, wherein A is a positive integer;

[0014] An identification module, the identification module is used to perform feature identification according to the A newly added energy routing tasks to obtain feature data of the A newly added routing tasks;

[0015] A loading module, the loading module is used to load an energy routing data source according to the power grid energy routing management terminal, wherein the energy routing data source includes H energy routing data streams corresponding to H energy routers of the power grid, each energy routing data stream includes router basic data, real-time network environment information and router fixed task information corresponding to the energy router, and H is the total number of energy routers of the power grid;

[0016] A scheduling module, the scheduling module is used to perform routing scheduling on the H energy routers based on the A newly added routing task feature data and the energy routing data source based on routing scheduling coordination constraints, and obtain a newly added energy routing scheduling decision;

[0017] A configuration module, the configuration module is used to perform routing configuration analysis based on the A newly added routing task feature data and the newly added energy routing scheduling decision according to a pre-built energy routing configuration channel, obtain a newly added task routing configuration scheme, and generate an updated task routing configuration scheme in combination with a fixed task routing configuration scheme;

[0018] A monitoring module, wherein the monitoring module is used to continuously monitor the H energy routers based on the power grid energy routing management terminal, obtain a routing continuous monitoring data source, and adaptively adjust the update task routing configuration scheme in combination with routing dynamic constraint rules.

[0019] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0020] The energy routing task instructions of the power grid are received through the power grid energy routing management end, which includes A new energy routing tasks, and the features of the A new energy routing tasks are identified to obtain the feature data of the A new routing tasks. Then, the power grid energy routing management end loads the energy routing data source, which contains the data streams corresponding to H energy routers. Each energy routing data stream includes the basic data of the router, the real-time network environment information and the fixed task information of the router. Based on the routing scheduling collaborative constraint, combined with the feature data of A new routing tasks and the energy routing data source, the H energy routers are routed and scheduled, and the scheduling decision of the new energy routing is obtained. Subsequently, according to the feature data of A new routing tasks and the scheduling decision of the new energy routing, the routing configuration analysis is performed according to the pre-built energy routing configuration channel, and the routing configuration scheme of the new task is obtained. At the same time, combined with the fixed task routing configuration scheme, the updated task routing configuration scheme is generated. Finally, through the continuous monitoring of the H energy routers by the power grid energy routing management end, the routing continuous monitoring data source is obtained, and combined with the routing dynamic constraint rules, the updated task routing configuration scheme is adaptively adjusted. The invention solves the technical problem in the prior art that the routing configuration is difficult to adjust and optimize according to the real-time situation, resulting in poor configuration flexibility, and achieves the technical effect of improving the flexibility of router configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of 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.

[0022] Figure 1 A schematic diagram of a flow chart of a dynamic configuration method of an energy router provided in an embodiment of the present application;

[0023] Figure 2 A schematic diagram of the structure of a dynamic configuration system for an energy router provided in an embodiment of the present application.

[0024] Explanation of the reference numerals: receiving module 11 , identifying module 12 , loading module 13 , scheduling module 14 , configuring module 15 , monitoring module 16 . DETAILED DESCRIPTION

[0025] The embodiments of the present application provide a method and system for dynamically configuring an energy router, thereby solving the technical problem in the prior art that routing configuration is difficult to adjust and optimize according to real-time conditions, resulting in poor configuration flexibility.

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0027] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] like Figure 1 As shown, an embodiment of the present application provides a method for dynamically configuring an energy router, wherein the method includes:

[0030] According to the power grid energy routing management terminal, receiving the energy routing task instruction of the power grid, wherein the energy routing task instruction includes A newly added energy routing tasks of the power grid, wherein A is a positive integer;

[0031] The grid energy routing management terminal is responsible for managing and coordinating energy routing tasks in the grid. When the grid needs to perform energy routing tasks, it will send energy routing task instructions to the grid energy routing management terminal. The energy routing task instructions contain A new energy routing tasks, where A is a positive integer representing the number of new tasks.

[0032] Perform feature identification according to the A newly added energy routing tasks to obtain feature data of the A newly added routing tasks;

[0033] A new energy routing tasks are analyzed and processed, and characteristic data of A new routing tasks are extracted, including information such as task type, target, energy demand, and time requirement.

[0034] According to the power grid energy routing management terminal, an energy routing data source is loaded, wherein the energy routing data source includes H energy routing data streams corresponding to H energy routers of the power grid, each energy routing data stream includes basic router data, real-time network environment information and router fixed task information corresponding to the energy router, and H is the total number of energy routers of the power grid;

[0035] The energy routing data source is loaded through the power grid energy routing management end. The energy routing data source is the H energy routing data streams corresponding to the H energy routers in the power grid. Each energy router is responsible for transmitting and distributing energy in the power grid and generating related routing data streams. The energy routing data stream includes the basic router data, real-time network environment information and router fixed task information corresponding to the energy router. The basic router data includes the basic configuration, performance parameters, connection relationship, etc. of the energy router. The real-time network environment information reflects the current operating status and network environment of the power grid, such as network delay, bandwidth utilization, energy transmission efficiency, etc., providing a dynamic basis for routing scheduling. The fixed task information of the router refers to the tasks that have been assigned to the energy router.

[0036] Based on the routing scheduling coordination constraint, routing scheduling is performed on the H energy routers according to the A newly added routing task feature data and the energy routing data source to obtain a newly added energy routing scheduling decision;

[0037] According to the routing scheduling coordination constraints, combined with the characteristic data of the task and the energy routing data source, the H energy routers can be more effectively routed, that is, the most suitable router is selected for each task, so as to obtain the new energy routing scheduling decision. The new energy routing scheduling decision is used to allocate and adjust the resources of the energy router to meet the needs of the new tasks.

[0038] Furthermore, based on the routing scheduling coordination constraint, routing scheduling is performed on the H energy routers according to the A newly added routing task feature data and the energy routing data source to obtain a newly added energy routing scheduling decision, including:

[0039] Perform data cleaning according to the energy routing data source to obtain an energy routing standard data source;

[0040] Taking the energy routing standard data source as a routing scheduling constraint, allocating the A newly added energy routing tasks based on the A newly added routing task characteristic data, and obtaining an initial energy routing scheduling decision;

[0041] Performing a routing scheduling collaborative analysis on the initial energy routing scheduling decision according to a preset routing scheduling collaborative weight to obtain an initial routing scheduling collaborative coefficient;

[0042] Determining whether the initial routing scheduling coordination coefficient satisfies the routing scheduling coordination constraint;

[0043] If the initial routing scheduling coordination coefficient satisfies the routing scheduling coordination constraint, the initial energy routing scheduling decision is added to the newly added energy routing scheduling decision.

[0044] The energy routing data source is cleaned, including removing outliers, filling missing data, correcting erroneous data, etc., so as to generate the energy routing standard data source. With the energy routing standard data source as the routing scheduling constraint, A new energy routing tasks are allocated to obtain the initial energy routing scheduling decision. The initial energy routing scheduling decision includes the allocated energy router corresponding to each new energy routing task and the real-time network environment information corresponding to the allocated energy router. According to the preset routing scheduling coordination weight, the initial energy routing scheduling decision is subjected to routing scheduling coordination analysis. The routing scheduling coordination weight reflects the relative importance between different scheduling objectives. By combining these weights, the system can comprehensively evaluate the initial scheduling decision and calculate the initial routing scheduling coordination coefficient. The initial routing scheduling coordination coefficient reflects the comprehensive performance of the scheduling decision on multiple objectives. Next, it is determined whether the initial routing scheduling coordination coefficient meets the routing scheduling coordination constraint. The routing scheduling coordination constraint is the preset routing scheduling coordination coefficient range. If the coefficient meets the constraint condition, it means that the initial scheduling decision is feasible and can be added to the new energy routing scheduling decision.

[0045] Furthermore, the initial energy routing scheduling decision is subjected to routing scheduling coordination analysis according to the preset routing scheduling coordination weight to obtain the initial routing scheduling coordination coefficient, including:

[0046] Based on the multi-element routing matching evaluation index, the routing matching evaluation of each newly added energy routing task is performed according to the initial energy routing scheduling decision to obtain A task routing scheduling matching coefficients, wherein the multi-element routing matching evaluation index includes the task routing performance matching degree, the task routing network fitness degree and the task routing time margin, and the multi-element routing matching evaluation index has a corresponding identification routing matching evaluation weight condition;

[0047] A routing scheduling matching weight condition is generated by calculating the proportion of the routing scheduling matching coefficients of the A tasks, and a weighted calculation is performed on the routing scheduling matching coefficients of the A tasks according to the routing scheduling matching weight condition to generate an initial routing scheduling matching evaluation coefficient;

[0048] Based on the preset time zone operator, task balance identification of each energy router is performed according to the initial energy routing scheduling decision to obtain H routing task balance coefficients;

[0049] By calculating the proportions of the H routing task balancing coefficients, a routing task balancing weight condition is generated, and weighted calculation is performed on the H routing task balancing coefficients according to the routing task balancing weight condition to generate an initial routing balancing evaluation coefficient;

[0050] The initial route scheduling matching evaluation coefficient and the initial route balancing evaluation coefficient are weightedly calculated based on the preset route scheduling coordination weight to generate the initial route scheduling coordination coefficient.

[0051] The multi-element routing matching evaluation indexes include task routing performance matching, task routing network fitness and task routing time margin, which measure the effect of routing scheduling from different perspectives. Task routing performance matching measures the matching degree between the newly added energy routing task and the performance of the energy router. Task routing network fitness evaluates the adaptability of the task in the network environment. Task routing time margin considers the matching degree between the time requirement of the task and the routing scheduling decision. Each evaluation index has a corresponding identification routing matching evaluation weight condition, which reflects the relative importance of different indicators in the evaluation process. Based on the multi-element routing matching evaluation index, the initial energy routing scheduling decision is evaluated for routing matching of each newly added energy routing task, and the characteristics of each task are compared and analyzed with the performance, network conditions and time requirements of the energy router. The matching degree between the task and each evaluation index is calculated, and A task routing scheduling matching coefficients are obtained. The proportion of the A task routing scheduling matching coefficients is calculated to generate the routing scheduling matching weight condition. Then, the matching coefficient is weighted according to the routing scheduling matching weight condition to generate the initial routing scheduling matching evaluation coefficient. The initial routing scheduling matching evaluation coefficient comprehensively reflects the overall performance of the initial scheduling decision in routing matching.

[0052] The preset time zone operator refers to the preset unit time zone. Task balancing identification based on the preset time zone operator can more accurately evaluate the task load of each energy router in different time periods, thereby realizing the reasonable allocation and scheduling of tasks. Specifically, the task data of each energy router in the time period defined by the different time zone operators, including the number of tasks, task type, task priority, etc., are collected. According to the collected task data and the definition of the time zone operator, the task load of each energy router in different time periods is calculated. For example, the average number of tasks, the maximum number of tasks, and the distribution of task types of each router in each time period can be calculated. By comparing the task loads between different routers, routers with heavier task loads and routers with lighter task loads can be identified. Then, based on these identification results, H routing task balancing coefficients are calculated. The routing task balancing coefficient reflects the balance of task allocation of each energy router. The higher the coefficient value, the more balanced the task allocation; otherwise, it means that there is an imbalance in task allocation. The routing task balancing coefficient is calculated to generate the routing task balancing weight condition. Then, the balancing coefficient is weighted according to the routing task balancing weight condition to generate the initial routing balancing evaluation coefficient, which represents the performance of the initial scheduling decision in terms of task balancing. Finally, the initial routing scheduling matching evaluation coefficient and the initial routing balancing evaluation coefficient are weighted based on the preset routing scheduling coordination weight to generate the initial routing scheduling coordination coefficient, which combines the evaluation of routing matching and task balancing, and provides an important basis for the subsequent scheduling decision optimization.

[0053] Further, judging whether the initial routing scheduling coordination coefficient satisfies the routing scheduling coordination constraint includes:

[0054] If the initial routing scheduling coordination coefficient does not satisfy the routing scheduling coordination constraint, mutate the initial energy routing scheduling decision to generate an adjusted energy routing scheduling decision;

[0055] Performing a routing scheduling collaborative analysis on the energy routing scheduling decision according to the preset routing scheduling collaborative weight to obtain an adjusted routing scheduling collaborative coefficient;

[0056] Determining whether the adjusted routing scheduling coordination coefficient satisfies the routing scheduling coordination constraint;

[0057] If the adjusted routing scheduling coordination coefficient satisfies the routing scheduling coordination constraint, the adjusted energy routing scheduling decision is added to the newly added energy routing scheduling decision.

[0058] If the initial routing scheduling coordination coefficient does not meet the constraints, the initial energy routing scheduling decision is mutated, and the adjusted energy routing scheduling decision is generated by adjusting the routing allocation strategy, changing the task allocation order, or adjusting the task priority. Then the routing scheduling coordination analysis is performed again to calculate the adjusted routing scheduling coordination coefficient and determine whether it meets the constraints. This process may require multiple iterations until an effective scheduling decision that meets the constraints is found. The adjusted energy routing scheduling decision that meets the constraints is added to the newly added energy routing scheduling decision.

[0059] Based on the A newly added routing task characteristic data and the newly added energy routing scheduling decision, a routing configuration analysis is performed according to a pre-built energy routing configuration channel to obtain a newly added task routing configuration scheme, and an updated task routing configuration scheme is generated in combination with a fixed task routing configuration scheme;

[0060] A neural network model-based energy routing configuration channel is constructed to analyze the feature data and scheduling decisions of the newly added routing tasks and generate routing configuration plans for the newly added tasks. Specifically, the feature data of the newly added routing tasks and the scheduling decisions of the newly added energy routing are used as training data and input into the recurrent neural network for training. The input layer receives the feature data of the newly added routing tasks, and the output layer outputs the routing configuration plan for the newly added tasks. The scheduling decisions of the newly added energy routing are used as the input of the model or as part of the hidden layer so that the model can learn the impact of the scheduling decisions on the routing configuration. The training data is used to continuously iterate the training until the model meets the requirements and the construction of the energy routing configuration channel is completed. The routing configuration analysis is performed according to the pre-built energy routing configuration channel to generate the routing configuration plan for the newly added tasks. The routing configuration plan for the newly added tasks details the routing path and configuration parameters that each newly added task should use. Combined with the fixed task routing configuration plan, an updated task routing configuration plan is generated. The fixed task routing configuration plan is the routing configuration corresponding to the assigned tasks of each energy router. By merging and coordinating the routing configuration plan for the newly added tasks with the fixed task routing configuration plan, a complete updated task routing configuration plan containing all task routing configuration information can be obtained.

[0061] Based on the continuous monitoring of the H energy routers by the power grid energy routing management terminal, a routing continuous monitoring data source is obtained, and the routing configuration scheme of the update task is adaptively adjusted in combination with routing dynamic constraint rules.

[0062] The grid energy routing management end will continuously monitor H energy routers to form a routing continuous monitoring data source, which reflects the real-time operation status of the router. The routing continuous monitoring data source is combined with the routing dynamic constraint rules for analysis. The routing dynamic constraint rules are formulated based on the real-time operation status of the power grid, changes in task requirements, and external environmental factors, and include a series of constraints that the router needs to comply with during operation. By comparing the monitoring data and constraint rules, the routing configuration scheme for the updated task is adaptively adjusted, including reallocating tasks to different routers, adjusting routing paths to avoid fault areas, and optimizing routing configuration parameters to improve transmission efficiency.

[0063] Furthermore, based on the continuous monitoring of the H energy routers by the power grid energy routing management terminal, a routing continuous monitoring data source is obtained, and the routing configuration scheme of the update task is adaptively adjusted in combination with the routing dynamic constraint rules, including:

[0064] According to the H energy routers, extracting the h-th energy router, where h is a positive integer and h belongs to H;

[0065] Extracting the hth real-time routing configuration decision corresponding to the hth energy router according to the updated task routing configuration scheme;

[0066] According to the routing continuous monitoring data source, extracting the hth routing continuous monitoring data stream corresponding to the hth energy router, wherein the hth routing continuous monitoring data stream includes the continuous routing natural environment monitoring data, the continuous routing network environment monitoring data and the continuous routing device monitoring data corresponding to the hth energy router;

[0067] Perform anomaly identification and evaluation based on the continuous routing environment monitoring data to obtain a routing environment anomaly identification result and a routing environment anomaly coefficient;

[0068] Determining whether the routing natural environment anomaly coefficient satisfies the preset routing natural environment anomaly constraint in the routing dynamic constraint rule;

[0069] If the routing natural environment anomaly coefficient does not meet the preset routing natural environment anomaly constraint, a routing configuration compensation analysis is performed based on the routing natural environment anomaly identification result to obtain environmental anomaly routing configuration compensation data, and the hth real-time routing configuration decision is optimized according to the environmental anomaly routing configuration compensation data.

[0070] The hth energy router is selected from H energy routers as the analysis object, where h is a positive integer and h belongs to the set H. According to the previously generated update task routing configuration scheme, the hth real-time routing configuration decision corresponding to the hth energy router is extracted, which contains the routing configuration information that the current router should execute. The routing continuous monitoring data source is used to extract the hth routing continuous monitoring data stream related to the hth energy router. The hth routing continuous monitoring data stream includes continuous routing natural environment monitoring data, continuous routing network environment monitoring data and continuous routing equipment monitoring data, wherein the continuous routing natural environment monitoring data includes temperature, dust density, etc. After obtaining the continuous routing natural environment monitoring data, anomaly identification and evaluation are performed. By comparing the monitoring data with the preset threshold or standard, the abnormal situation in the natural environment is identified, and the routing natural environment anomaly identification result and the routing natural environment anomaly coefficient are obtained. The routing natural environment anomaly identification result and the routing natural environment anomaly coefficient describe the degree and impact of the natural environment anomaly. It is judged whether the routing natural environment anomaly coefficient meets the preset routing natural environment anomaly constraint in the routing dynamic constraint rule. If the anomaly coefficient exceeds the preset constraint conditions, it means that the current natural environment anomaly has a significant impact on the operation of the router, and corresponding configuration compensation and optimization are required. If the routing natural environment anomaly coefficient does not meet the preset constraints, a routing configuration compensation analysis is performed based on the routing natural environment anomaly identification results. Through analysis, the environmental anomaly routing configuration compensation data is obtained, and the environmental anomaly routing configuration compensation data provides specific suggestions for optimizing the h-th real-time routing configuration decision. Finally, the h-th real-time routing configuration decision is optimized based on the environmental anomaly routing configuration compensation data.

[0071] Furthermore, after extracting the h-th routing continuous monitoring data flow corresponding to the h-th energy router, the method further includes:

[0072] Performing a network baseline state comparison based on the continuous routing network environment monitoring data, and determining a network state change comparison result and a network state change comparison coefficient;

[0073] Perform network anomaly identification and evaluation based on the continuous routing network environment monitoring data to obtain a network anomaly identification result and a network anomaly evaluation coefficient;

[0074] Perform network security analysis based on the network anomaly identification result to determine a network security evaluation coefficient;

[0075] Determine whether the network state change comparison coefficient, the network anomaly evaluation coefficient, and the network security evaluation coefficient satisfy the preset network feature multivariate constraint in the routing dynamic constraint rule;

[0076] If any one of the network state change comparison coefficient, the network anomaly evaluation coefficient and the network security evaluation coefficient does not satisfy the preset network feature multivariate constraint, generating network feature anomaly data based on the network state change comparison result and the network anomaly identification result;

[0077] The hth real-time routing configuration decision is adjusted based on the network characteristic abnormal data to generate an adjusted routing configuration decision.

[0078] Comparison of network baseline status and abnormal identification and evaluation based on continuous routing network environment monitoring data is to ensure that energy routers can make corresponding adjustments when the network environment changes. Comparison of continuous routing network environment monitoring data with network baseline status. Network baseline status refers to the standard state or expected state of the network during normal operation. By comparing real-time monitoring data with the baseline status, changes in network status can be determined, including changes in connection stability, transmission speed, latency, etc. After the comparison, the network status change comparison results and network status change comparison coefficients are obtained. The network status change comparison results and network status change comparison coefficients quantitatively describe the degree and trend of changes in network status. Next, network abnormality identification and evaluation are performed based on continuous routing network environment monitoring data. Network abnormalities may include network congestion, packet loss, node failure and other problems. By comparing monitoring data with preset abnormality thresholds or standards, abnormalities in the network can be identified and network abnormality evaluation coefficients can be calculated. The network abnormality evaluation coefficient reflects the degree and scope of network abnormalities. In order to gain a deeper understanding of the network status, network security analysis is also performed based on the network abnormality identification results. Network security analysis involves detecting and evaluating potential threats and attacks in the network. Through analysis, the network security evaluation coefficient is determined, and the network security evaluation coefficient quantitatively describes the security performance of the network. Then, according to the preset network feature multi-constraints in the routing dynamic constraint rules, the preset network feature multi-constraints include network state change comparison coefficient constraints, network anomaly evaluation coefficient constraints, and network security evaluation coefficient constraints, it is judged whether the network state change comparison coefficient, network anomaly evaluation coefficient, and network security evaluation coefficient meet the constraint conditions. If any coefficient does not meet the preset network feature multi-constraints, it means that there is a significant change or anomaly in the current network state, which may have an adverse effect on the operation of the energy router. At this time, based on the network state change comparison results and network anomaly identification results, network feature anomaly data is generated. The network feature anomaly data describes the specific situation and characteristics of the network anomaly, and provides a basis for subsequent routing configuration adjustments. Finally, based on the network feature anomaly data, the h-th real-time routing configuration decision is adjusted, including changing the routing path, adjusting the transmission strategy, increasing network redundancy, and other measures to cope with changes and anomalies in the network state. Through adjustment, an adjusted routing configuration decision is generated, and the generated adjusted routing configuration decision is more in line with the actual situation of the current network environment.

[0079] Furthermore, after extracting the h-th routing continuous monitoring data flow corresponding to the h-th energy router, the method further includes:

[0080] Performing abnormality identification according to the continuous routing device monitoring data to obtain a routing device abnormality identification result;

[0081] Performing fault prediction based on the routing device abnormality identification result to generate a routing device fault prediction risk coefficient;

[0082] Determining whether the routing device failure prediction risk coefficient satisfies the routing failure risk constraint in the routing dynamic constraint rule;

[0083] If the routing device failure prediction risk coefficient does not satisfy the routing failure risk constraint, generating a redundant routing protection instruction;

[0084] If the routing device failure prediction risk coefficient satisfies the routing failure risk constraint, the h-th real-time routing configuration decision is optimized according to the routing device anomaly identification result.

[0085] Based on the continuous routing device monitoring data, abnormality identification and fault prediction are performed to ensure the stable operation of the energy router and respond to potential faults in a timely manner. The continuous routing device monitoring data is used for abnormality identification, and the continuous routing device monitoring data is the monitoring data of the hth energy router. By comparing the real-time monitoring data with the preset normal range or threshold, the abnormal conditions that may exist in the device, such as excessive temperature, unstable voltage, etc., can be identified. Fault prediction is performed based on the abnormal identification results of the routing device, and the routing device fault prediction risk coefficient is generated. Fault prediction is to predict the type and probability of possible future failures of the device by analyzing the abnormal conditions and historical operation data of the device. The routing device fault prediction risk coefficient quantitatively describes the risk level of device failure. According to the routing fault risk constraint within the routing dynamic constraint rule, it is judged whether the routing device fault prediction risk coefficient meets the constraint condition. The routing fault risk constraint is formulated based on the security and stability requirements of the power grid energy routing system to ensure that the equipment failure risk is within a controllable range. If the routing device fault prediction risk coefficient does not meet the routing fault risk constraint, it means that the device has a high failure risk, which may have a serious impact on the execution of the routing task. At this time, a redundant routing protection instruction is generated. Redundant routing protection instructions are used to automatically switch to redundant energy routers when the main routing fails, ensuring the continuity and stability of routing tasks. If the routing device failure prediction risk coefficient meets the routing failure risk constraint, it means that the device failure risk is within the controllable range and will not have a serious impact on the execution of routing tasks. At this time, the routing configuration of the hth real-time routing configuration decision is optimized based on the routing device anomaly identification results, including adjusting the device's operating parameters, optimizing routing paths, increasing equipment maintenance, etc., in order to improve the equipment's operating efficiency and stability and further reduce the risk of failure.

[0086] In summary, the embodiments of the present application have at least the following technical effects:

[0087] The energy routing task instructions of the power grid are received through the power grid energy routing management end, which includes A new energy routing tasks, and the features of the A new energy routing tasks are identified to obtain the feature data of the A new routing tasks. Then, the power grid energy routing management end loads the energy routing data source, which contains the data streams corresponding to H energy routers. Each energy routing data stream includes the basic data of the router, the real-time network environment information and the fixed task information of the router. Based on the routing scheduling collaborative constraint, combined with the feature data of A new routing tasks and the energy routing data source, the H energy routers are routed and scheduled, and the scheduling decision of the new energy routing is obtained. Subsequently, according to the feature data of A new routing tasks and the scheduling decision of the new energy routing, the routing configuration analysis is performed according to the pre-built energy routing configuration channel, and the routing configuration scheme of the new task is obtained. At the same time, combined with the fixed task routing configuration scheme, the updated task routing configuration scheme is generated. Finally, through the continuous monitoring of the H energy routers by the power grid energy routing management end, the routing continuous monitoring data source is obtained, and combined with the routing dynamic constraint rules, the updated task routing configuration scheme is adaptively adjusted. The invention solves the technical problem in the prior art that the routing configuration is difficult to adjust and optimize according to the real-time situation, resulting in poor configuration flexibility, and achieves the technical effect of improving the flexibility of router configuration.

[0088] Embodiment 2

[0089] Based on the same inventive concept as the dynamic configuration method of an energy router in the aforementioned embodiment, Figure 2 As shown, the present application provides a dynamic configuration system for an energy router, and the system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0090] A receiving module 11, wherein the receiving module 11 is used to receive an energy routing task instruction of a power grid according to the power grid energy routing management terminal, wherein the energy routing task instruction includes A newly added energy routing tasks of the power grid, wherein A is a positive integer;

[0091] An identification module 12, the identification module 12 is used to perform feature identification according to the A newly added energy routing tasks to obtain feature data of the A newly added routing tasks;

[0092] A loading module 13, the loading module 13 is used to load an energy routing data source according to the power grid energy routing management terminal, wherein the energy routing data source includes H energy routing data streams corresponding to H energy routers of the power grid, each energy routing data stream includes basic router data, real-time network environment information and router fixed task information corresponding to the energy router, and H is the total number of energy routers of the power grid;

[0093] A scheduling module 14, the scheduling module 14 is used to perform routing scheduling on the H energy routers based on the A newly added routing task feature data and the energy routing data source based on routing scheduling coordination constraints, and obtain a newly added energy routing scheduling decision;

[0094] Configuration module 15, the configuration module 15 is used to perform routing configuration analysis based on the A newly added routing task feature data and the newly added energy routing scheduling decision according to the pre-built energy routing configuration channel, obtain the newly added task routing configuration plan, and generate an updated task routing configuration plan in combination with the fixed task routing configuration plan;

[0095] The monitoring module 16 is used to continuously monitor the H energy routers based on the power grid energy routing management terminal, obtain the routing continuous monitoring data source, and adaptively adjust the update task routing configuration scheme in combination with the routing dynamic constraint rules.

[0096] Furthermore, the scheduling module 14 is used to execute the following method:

[0097] Perform data cleaning according to the energy routing data source to obtain an energy routing standard data source;

[0098] Taking the energy routing standard data source as a routing scheduling constraint, allocating the A newly added energy routing tasks based on the A newly added routing task characteristic data, and obtaining an initial energy routing scheduling decision;

[0099] Performing a routing scheduling collaborative analysis on the initial energy routing scheduling decision according to a preset routing scheduling collaborative weight to obtain an initial routing scheduling collaborative coefficient;

[0100] Determining whether the initial routing scheduling coordination coefficient satisfies the routing scheduling coordination constraint;

[0101] If the initial routing scheduling coordination coefficient satisfies the routing scheduling coordination constraint, the initial energy routing scheduling decision is added to the newly added energy routing scheduling decision.

[0102] Furthermore, the scheduling module 14 is used to execute the following method:

[0103] Based on the multi-element routing matching evaluation index, the routing matching evaluation of each newly added energy routing task is performed according to the initial energy routing scheduling decision to obtain A task routing scheduling matching coefficients, wherein the multi-element routing matching evaluation index includes the task routing performance matching degree, the task routing network fitness degree and the task routing time margin, and the multi-element routing matching evaluation index has a corresponding identification routing matching evaluation weight condition;

[0104] A routing scheduling matching weight condition is generated by calculating the proportion of the routing scheduling matching coefficients of the A tasks, and a weighted calculation is performed on the routing scheduling matching coefficients of the A tasks according to the routing scheduling matching weight condition to generate an initial routing scheduling matching evaluation coefficient;

[0105] Based on the preset time zone operator, task balance identification of each energy router is performed according to the initial energy routing scheduling decision to obtain H routing task balance coefficients;

[0106] By calculating the proportions of the H routing task balancing coefficients, a routing task balancing weight condition is generated, and weighted calculation is performed on the H routing task balancing coefficients according to the routing task balancing weight condition to generate an initial routing balancing evaluation coefficient;

[0107] The initial route scheduling matching evaluation coefficient and the initial route balancing evaluation coefficient are weightedly calculated based on the preset route scheduling coordination weight to generate the initial route scheduling coordination coefficient.

[0108] Furthermore, the scheduling module 14 is used to execute the following method:

[0109] If the initial routing scheduling coordination coefficient does not satisfy the routing scheduling coordination constraint, mutate the initial energy routing scheduling decision to generate an adjusted energy routing scheduling decision;

[0110] Performing a routing scheduling collaborative analysis on the energy routing scheduling decision according to the preset routing scheduling collaborative weight to obtain an adjusted routing scheduling collaborative coefficient;

[0111] Determining whether the adjusted routing scheduling coordination coefficient satisfies the routing scheduling coordination constraint;

[0112] If the adjusted routing scheduling coordination coefficient satisfies the routing scheduling coordination constraint, the adjusted energy routing scheduling decision is added to the newly added energy routing scheduling decision.

[0113] Furthermore, the monitoring module 16 is used to perform the following method:

[0114] According to the H energy routers, extracting the h-th energy router, where h is a positive integer and h belongs to H;

[0115] Extracting the hth real-time routing configuration decision corresponding to the hth energy router according to the updated task routing configuration scheme;

[0116] According to the routing continuous monitoring data source, extracting the hth routing continuous monitoring data stream corresponding to the hth energy router, wherein the hth routing continuous monitoring data stream includes the continuous routing natural environment monitoring data, the continuous routing network environment monitoring data and the continuous routing device monitoring data corresponding to the hth energy router;

[0117] Perform anomaly identification and evaluation based on the continuous routing environment monitoring data to obtain a routing environment anomaly identification result and a routing environment anomaly coefficient;

[0118] Determining whether the routing natural environment anomaly coefficient satisfies the preset routing natural environment anomaly constraint in the routing dynamic constraint rule;

[0119] If the routing natural environment anomaly coefficient does not meet the preset routing natural environment anomaly constraint, a routing configuration compensation analysis is performed based on the routing natural environment anomaly identification result to obtain environmental anomaly routing configuration compensation data, and the hth real-time routing configuration decision is optimized according to the environmental anomaly routing configuration compensation data.

[0120] Furthermore, the monitoring module 16 is used to perform the following method:

[0121] Performing a network baseline state comparison based on the continuous routing network environment monitoring data, and determining a network state change comparison result and a network state change comparison coefficient;

[0122] Perform network anomaly identification and evaluation based on the continuous routing network environment monitoring data to obtain a network anomaly identification result and a network anomaly evaluation coefficient;

[0123] Perform network security analysis based on the network anomaly identification result to determine a network security evaluation coefficient;

[0124] Determine whether the network state change comparison coefficient, the network anomaly evaluation coefficient, and the network security evaluation coefficient satisfy the preset network feature multivariate constraint in the routing dynamic constraint rule;

[0125] If any one of the network state change comparison coefficient, the network anomaly evaluation coefficient and the network security evaluation coefficient does not satisfy the preset network feature multivariate constraint, generating network feature anomaly data based on the network state change comparison result and the network anomaly identification result;

[0126] The hth real-time routing configuration decision is adjusted based on the network characteristic abnormal data to generate an adjusted routing configuration decision.

[0127] Furthermore, the monitoring module 16 is used to perform the following method:

[0128] Performing abnormality identification according to the continuous routing device monitoring data to obtain a routing device abnormality identification result;

[0129] Performing fault prediction based on the routing device abnormality identification result to generate a routing device fault prediction risk coefficient;

[0130] Determining whether the routing device failure prediction risk coefficient satisfies the routing failure risk constraint in the routing dynamic constraint rule;

[0131] If the routing device failure prediction risk coefficient does not satisfy the routing failure risk constraint, generating a redundant routing protection instruction;

[0132] If the routing device failure prediction risk coefficient satisfies the routing failure risk constraint, the h-th real-time routing configuration decision is optimized according to the routing device anomaly identification result.

[0133] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0134] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0135] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A dynamic configuration method for an energy router, characterized in that: The method comprises: According to the power grid energy routing management terminal, receiving the energy routing task instruction of the power grid, wherein the energy routing task instruction includes A newly added energy routing tasks of the power grid, wherein A is a positive integer; Perform feature identification according to the A newly added energy routing tasks to obtain feature data of the A newly added routing tasks; According to the power grid energy routing management terminal, an energy routing data source is loaded, wherein the energy routing data source includes H energy routing data streams corresponding to H energy routers of the power grid, each energy routing data stream includes basic router data, real-time network environment information and router fixed task information corresponding to the energy router, and H is the total number of energy routers of the power grid; Based on the routing scheduling coordination constraint, routing scheduling is performed on the H energy routers according to the A newly added routing task feature data and the energy routing data source to obtain a newly added energy routing scheduling decision; Based on the A newly added routing task characteristic data and the newly added energy routing scheduling decision, a routing configuration analysis is performed according to a pre-built energy routing configuration channel to obtain a newly added task routing configuration scheme, and an updated task routing configuration scheme is generated in combination with a fixed task routing configuration scheme; Based on the power grid energy routing management terminal, the H energy routers are continuously monitored to obtain a routing continuous monitoring data source, and the routing configuration scheme of the update task is adaptively adjusted in combination with routing dynamic constraint rules; Among them, based on the continuous monitoring of the H energy routers by the power grid energy routing management terminal, the routing continuous monitoring data source is obtained, and the routing configuration scheme of the update task is adaptively adjusted in combination with the routing dynamic constraint rules, including: According to the H energy routers, extracting the h-th energy router, where h is a positive integer and h belongs to H; Extracting the hth real-time routing configuration decision corresponding to the hth energy router according to the updated task routing configuration scheme; According to the routing continuous monitoring data source, extracting the hth routing continuous monitoring data stream corresponding to the hth energy router, wherein the hth routing continuous monitoring data stream includes the continuous routing natural environment monitoring data, the continuous routing network environment monitoring data and the continuous routing device monitoring data corresponding to the hth energy router; Perform anomaly identification and evaluation based on the continuous routing environment monitoring data to obtain a routing environment anomaly identification result and a routing environment anomaly coefficient; Determining whether the routing natural environment anomaly coefficient satisfies the preset routing natural environment anomaly constraint in the routing dynamic constraint rule; If the routing natural environment anomaly coefficient does not meet the preset routing natural environment anomaly constraint, a routing configuration compensation analysis is performed based on the routing natural environment anomaly identification result to obtain environmental anomaly routing configuration compensation data, and the hth real-time routing configuration decision is optimized according to the environmental anomaly routing configuration compensation data.

2. The method according to claim 1, characterized in that Based on the routing scheduling coordination constraint, routing scheduling is performed on the H energy routers according to the A newly added routing task feature data and the energy routing data source to obtain a newly added energy routing scheduling decision, including: Perform data cleaning according to the energy routing data source to obtain an energy routing standard data source; Taking the energy routing standard data source as a routing scheduling constraint, allocating the A newly added energy routing tasks based on the A newly added routing task characteristic data, and obtaining an initial energy routing scheduling decision; Performing a routing scheduling collaborative analysis on the initial energy routing scheduling decision according to a preset routing scheduling collaborative weight to obtain an initial routing scheduling collaborative coefficient; Determining whether the initial routing scheduling coordination coefficient satisfies the routing scheduling coordination constraint; If the initial routing scheduling coordination coefficient satisfies the routing scheduling coordination constraint, the initial energy routing scheduling decision is added to the newly added energy routing scheduling decision.

3. The method according to claim 2, characterized in that The initial energy routing scheduling decision is subjected to routing scheduling coordination analysis according to the preset routing scheduling coordination weight to obtain the initial routing scheduling coordination coefficient, including: Based on the multi-element routing matching evaluation index, the routing matching evaluation of each newly added energy routing task is performed according to the initial energy routing scheduling decision to obtain A task routing scheduling matching coefficients, wherein the multi-element routing matching evaluation index includes the task routing performance matching degree, the task routing network fitness degree and the task routing time margin, and the multi-element routing matching evaluation index has a corresponding identification routing matching evaluation weight condition; A routing scheduling matching weight condition is generated by calculating the proportion of the routing scheduling matching coefficients of the A tasks, and a weighted calculation is performed on the routing scheduling matching coefficients of the A tasks according to the routing scheduling matching weight condition to generate an initial routing scheduling matching evaluation coefficient; Based on the preset time zone operator, task balance identification of each energy router is performed according to the initial energy routing scheduling decision to obtain H routing task balance coefficients; By calculating the proportions of the H routing task balancing coefficients, a routing task balancing weight condition is generated, and weighted calculation is performed on the H routing task balancing coefficients according to the routing task balancing weight condition to generate an initial routing balancing evaluation coefficient; The initial route scheduling matching evaluation coefficient and the initial route balancing evaluation coefficient are weightedly calculated based on the preset route scheduling coordination weight to generate the initial route scheduling coordination coefficient.

4. The method according to claim 2, characterized in that Determining whether the initial routing scheduling coordination coefficient satisfies the routing scheduling coordination constraint includes: If the initial routing scheduling coordination coefficient does not satisfy the routing scheduling coordination constraint, mutate the initial energy routing scheduling decision to generate an adjusted energy routing scheduling decision; Performing a routing scheduling collaborative analysis on the energy routing scheduling decision according to the preset routing scheduling collaborative weight to obtain an adjusted routing scheduling collaborative coefficient; Determining whether the adjusted routing scheduling coordination coefficient satisfies the routing scheduling coordination constraint; If the adjusted routing scheduling coordination coefficient satisfies the routing scheduling coordination constraint, the adjusted energy routing scheduling decision is added to the newly added energy routing scheduling decision.

5. The method according to claim 1, characterized in that After extracting the h-th routing continuous monitoring data stream corresponding to the h-th energy router, the method further includes: Performing a network baseline state comparison based on the continuous routing network environment monitoring data, and determining a network state change comparison result and a network state change comparison coefficient; Perform network anomaly identification and evaluation based on the continuous routing network environment monitoring data to obtain a network anomaly identification result and a network anomaly evaluation coefficient; Perform network security analysis based on the network anomaly identification result to determine a network security evaluation coefficient; Determine whether the network state change comparison coefficient, the network anomaly evaluation coefficient, and the network security evaluation coefficient satisfy the preset network feature multivariate constraint in the routing dynamic constraint rule; If any one of the network state change comparison coefficient, the network anomaly evaluation coefficient and the network security evaluation coefficient does not satisfy the preset network feature multivariate constraint, generating network feature anomaly data based on the network state change comparison result and the network anomaly identification result; The hth real-time routing configuration decision is adjusted based on the network characteristic abnormal data to generate an adjusted routing configuration decision.

6. The method according to claim 1, characterized in that After extracting the h-th routing continuous monitoring data stream corresponding to the h-th energy router, the method further includes: Performing abnormality identification according to the continuous routing device monitoring data to obtain a routing device abnormality identification result; Performing fault prediction based on the routing device abnormality identification result to generate a routing device fault prediction risk coefficient; Determining whether the routing device failure prediction risk coefficient satisfies the routing failure risk constraint in the routing dynamic constraint rule; If the routing device failure prediction risk coefficient does not satisfy the routing failure risk constraint, generating a redundant routing protection instruction; If the routing device failure prediction risk coefficient satisfies the routing failure risk constraint, the h-th real-time routing configuration decision is optimized according to the routing device anomaly identification result.

7. A dynamic configuration system for an energy router, characterized in that: A method for dynamically configuring an energy router according to any one of claims 1 to 6, the system comprising: A receiving module, the receiving module is used to receive an energy routing task instruction of a power grid according to the power grid energy routing management terminal, wherein the energy routing task instruction includes A newly added energy routing tasks of the power grid, wherein A is a positive integer; An identification module, the identification module is used to perform feature identification according to the A newly added energy routing tasks to obtain feature data of the A newly added routing tasks; A loading module, the loading module is used to load an energy routing data source according to the power grid energy routing management terminal, wherein the energy routing data source includes H energy routing data streams corresponding to H energy routers of the power grid, each energy routing data stream includes router basic data, real-time network environment information and router fixed task information corresponding to the energy router, and H is the total number of energy routers of the power grid; A scheduling module, the scheduling module is used to perform routing scheduling on the H energy routers based on the A newly added routing task feature data and the energy routing data source based on routing scheduling coordination constraints, and obtain a newly added energy routing scheduling decision; A configuration module, the configuration module is used to perform routing configuration analysis based on the A newly added routing task feature data and the newly added energy routing scheduling decision according to a pre-built energy routing configuration channel, obtain a newly added task routing configuration scheme, and generate an updated task routing configuration scheme in combination with a fixed task routing configuration scheme; A monitoring module, wherein the monitoring module is used to continuously monitor the H energy routers based on the power grid energy routing management terminal, obtain a routing continuous monitoring data source, and adaptively adjust the update task routing configuration scheme in combination with routing dynamic constraint rules.

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