Energy storage configuration optimization method and system combined with self-adaptive regulation and control of power grid

Abstract

The invention discloses an energy storage configuration optimization method and system combined with power grid adaptive regulation and control, and particularly relates to the field of energy storage configuration optimization. The authentication behavior snapshot comprises an authentication initiator identifier, a target identity identifier, a role switching mark, an authentication timestamp and an authentication type label; and constructing an authentication trajectory diagram structure according to the authentication behavior snapshot, wherein the trajectory diagram comprises a plurality of authentication nodes and authentication jump paths thereof. By constructing a track graph structure based on an authentication behavior snapshot and combining a double endorsement mechanism and path continuity judgment, whether an abnormal chain of skipping pseudo-legality exists in an authentication path can be identified, so that potential energy storage nodes which are falsely authenticated are frozen and isolated in time; and an attacker is prevented from tampering with a master control path through edge equipment to realize role spoofing tampering.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage configuration optimization, and more specifically, to a method and system for energy storage configuration optimization combined with grid adaptive regulation. Background Art

[0002] Adaptive grid control of energy storage configuration means that the system adjusts the charging and discharging strategies and control roles of each energy storage node based on real-time grid load fluctuations, changes in renewable energy output, and dynamic changes in regional power demand, achieving optimal distribution of electricity in time and space, thereby improving grid stability and energy utilization efficiency.

[0003] In a highly autonomous grid adaptive control architecture, distributed energy storage nodes must dynamically switch between master and slave roles based on system load to achieve redundant control and fault-tolerant optimization. Distributed energy storage nodes include BESS, home energy storage, and electric vehicle regeneration stations. Therefore, the authentication mechanism is no longer static, but instead implements a role-transferable identity system through a dynamic negotiation chain. Once an attacker invades an edge energy storage device, they can simulate the role switching process to construct a "relay authorization jump point," bypassing the master control authentication path and obtaining the right to execute control instructions through "boundary deception." This will directly threaten the security foundation of grid energy storage configuration optimization. Summary of the Invention

[0004] To overcome the above-mentioned shortcomings of the prior art, embodiments of the present invention provide a method and system for optimizing energy storage configuration in combination with adaptive grid control. By constructing a trajectory graph structure based on authentication behavior snapshots, combined with a double endorsement mechanism and path continuity judgment, it is possible to identify whether there are abnormal chains of "jumping to pseudo-legitimacy" in the authentication path, thereby timely freezing and isolating energy storage nodes with potential forged authentication, thereby solving the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a method for optimizing energy storage configuration combined with grid adaptive regulation;

[0006] The method is used to prevent unauthorized role takeover in a distributed energy storage network with dynamic master-slave role switching, and the method includes:

[0007] Generate an authentication behavior snapshot based on the authentication behavior of the energy storage node, wherein the authentication behavior snapshot includes an authentication initiator identifier, a target identity identifier, a role switching flag, an authentication timestamp, and an authentication type tag;

[0008] Constructing an authentication trajectory graph structure according to the authentication behavior snapshot, wherein the trajectory graph includes multiple authentication nodes and their authentication jump paths;

[0009] During each authentication jump, the first endorsement signature is obtained from the previous authentication node, and the second endorsement signature is obtained from at least one auxiliary node;

[0010] Determining whether the authentication jump path satisfies a preset role switching authorization path set in the boundary model;

[0011] If the jump path does not meet the authorization structure requirements of the boundary model, the corresponding trajectory path is frozen, and the energy storage device corresponding to the authentication node is moved into the verification pool to perform isolation control;

[0012] If the authentication behavior path is interpreted as a new type of path, it is included in the authorization authentication path set through the boundary model extension mechanism.

[0013] In a preferred embodiment, the authentication behavior snapshot is used to represent an information set of the complete state of the authentication process, and the authentication behavior snapshot includes authentication behavior elements, which include identity identification elements, behavior attribute elements, and role instruction elements; wherein the identity identification elements include the authentication initiator identifier and the target identifier, the behavior attribute elements include the authentication timestamp and the authentication type tag, and the role instruction elements include the role switching flag;

[0014] The authentication behavior snapshot is generated by the authentication behavior collection mechanism by performing a structured combination of various elements, and is embedded into the authentication trajectory graph structure in the form of authentication records, forming the basis for expressing the status of each authentication node;

[0015] During the execution of the authentication path, the authentication behavior snapshot is called as the structural input for judging the legitimacy of the trajectory path, and the integrity of the node behavior chain is judged through the continuity and combination of snapshots.

[0016] In a preferred embodiment, the authentication trajectory graph structure is used to represent the authentication node behavior jump relationship, each authentication node in the authentication trajectory graph structure is composed of an authentication behavior snapshot, and its jump edge is generated by authentication jump data;

[0017] The authentication jump data includes authentication behavior snapshot index information, previous jump node identification information and endorsement signature identification, and the authentication jump data is used to establish the directionality and credibility of the jump edge;

[0018] The authentication trajectory graph structure continuously expands the combination of nodes and edges during the continuous updating of authentication behavior, and embeds the endorsement signature identifier in each jump into the jump edge information to identify its trust source; based on the authentication trajectory graph structure, structural mapping processing is performed on all authentication paths, and the behavior path sequence formed by the nodes and jump edges in the authentication trajectory graph structure is used as input to perform boundary model matching judgment to support the subsequent risk assessment process.

[0019] In a preferred embodiment, each authentication jump satisfies a double endorsement condition, wherein the double endorsement condition includes endorsement by the source node and endorsement by the auxiliary node;

[0020] The source node endorsement is signed by the previous authentication node in the jump path based on the current authentication behavior snapshot and is bound to the corresponding authentication jump data;

[0021] The auxiliary node endorsement is generated by an energy storage node with the same role level. The energy storage node generates a response signature after performing a consistency comparison on the authentication behavior snapshot based on the historical status data collected in its behavior observation window;

[0022] A consistency check is performed on the source node endorsement and the auxiliary node endorsement. If any endorsement chain is found to have inconsistent direction, missing signature, or snapshot matching failure, the authentication jump behavior is marked as a potential risk path; the potential risk path is suspended, and the jump edge formed by it is frozen in the authentication trajectory graph structure and recorded in the risk assessment result set.

[0023] In a preferred embodiment, the boundary model is used to determine a set of rules for determining the validity of an authentication jump path structure. The set of rules is constructed based on authentication path structure constraint logic. The authentication path structure constraint logic includes: role level transfer rules, authentication jump depth limit, node continuity requirements, and path time window matching rules.

[0024] Receive path information from the authentication trajectory graph structure, and combine it with the role switching flag and timestamp data in the corresponding authentication behavior snapshot to perform matching analysis based on the authentication path structure constraint logic; when the path does not meet the structure constraint logic and its structural combination is in the risk-high-risk interval in the path sample, calculate its risk score through risk assessment;

[0025] If the risk score is higher than the preset threshold, the authentication jump path will be marked as a structural abnormal path, and the relevant jump edges will be frozen, and behavioral isolation will be performed on the nodes.

[0026] In a preferred embodiment, the energy storage device corresponding to the authentication node whose trajectory is frozen performs isolation processing, and the isolation processing includes disconnecting the authentication chain contact relationship, temporarily freezing the control authority, and limiting the authentication role transfer capability;

[0027] At the beginning of the isolation process, the authentication behavior snapshot chain associated with the authentication node is saved and entered into the verification pool, and its behavior response process is recorded as a state trajectory archive. Based on the state trajectory, a behavior replay process is constructed to verify the state deviation performance of the energy storage device under different control paths, and the deviation results are used as the input basis for credibility determination.

[0028] The risk assessment generates a credibility score based on the state deviation performance. If the credibility score is lower than the set credibility threshold, the energy storage device is transferred to a permanent isolation state; if the score is in the pre-accepted deviation range, the energy storage device is subject to the observation mechanism and operates within the limited authentication frequency and response time window.

[0029] In a preferred embodiment, the boundary model expansion is performed based on the joint judgment of new path identification, behavior evolution determination, and risk assessment; the new path identification extracts candidate paths based on the occurrence frequency below a preset threshold in the authentication trajectory graph structure and having offset characteristics, and records the associated authentication behavior snapshot sequence;

[0030] Perform consistency comparison on the authentication behavior snapshot sequence through behavior evolution judgment, match it to the historical authentication trajectory evolution curve and calculate the path credibility score;

[0031] Based on the path credibility score and the node historical offset record, a joint risk assessment is conducted to determine whether the candidate path is scalable.

[0032] If a candidate path exhibits a continuous behavior pattern in its evolutionary trend, and its state deviation fluctuates within the path historical deviation range standard, and its path credibility score is compared with the extended credibility score threshold and is determined to be lower than the extended credibility score threshold, then the candidate path is included in the boundary model authorized path set and an extended label is set for it to track the consistency of its subsequent evolutionary path in structural trends.

[0033] In a preferred embodiment, a trajectory evolution record is constructed during the authentication behavior execution cycle, and all path changes in the authentication trajectory graph structure are continuously tracked; a trajectory freezing record table is established to organize the freezing reasons, recovery conditions and evolution results of all frozen paths, and a behavior label set is constructed;

[0034] The trajectory evolution record provides an input data source for future path credibility assessment. The path prediction model is trained based on the trajectory evolution record. The path prediction model is used to predict the future evolution trend of the authentication behavior path.

[0035] If a certification path is identified as a trusted path after multiple rounds of freezing, the path will be marked as a gray behavior path, and its future jumps must meet the triple endorsement requirements and be accompanied by risk score information for reference; the sensitivity level of the gray behavior path will be dynamically updated, and the tolerance strategy for certification jump behavior will be adjusted accordingly to reduce the risk of path deception behavior interfering with the certification system.

[0036] An energy storage configuration optimization system combined with grid adaptive regulation includes a snapshot generation module, a trajectory mapping module, an endorsement verification module, a path determination module, an abnormality isolation module, and a path expansion module;

[0037] The snapshot generation module generates an authentication behavior snapshot based on the authentication behavior of the energy storage node, wherein the authentication behavior snapshot includes an authentication initiator identifier, a target identifier, a role switching flag, an authentication timestamp, and an authentication type tag; the snapshot generation module is used to describe the complete behavior information of the authentication status;

[0038] The trajectory mapping module constructs an authentication trajectory graph structure according to the authentication behavior snapshot, wherein the trajectory graph includes multiple authentication nodes and their authentication jump paths; the trajectory mapping module is used to represent the authentication behavior association relationship between energy storage devices;

[0039] The endorsement verification module is used to obtain the first endorsement signature from the previous authentication node and the second endorsement signature from at least one auxiliary node during each authentication jump process; the endorsement verification module is used to confirm the trusted source of the authentication jump path;

[0040] The path determination module is used to determine whether the authentication jump path meets the role switching authorization path set preset in the boundary model, and output the path matching result through the path determination module;

[0041] The abnormal isolation module includes: if the jump path does not meet the authorization structure requirements of the boundary model, freezing the corresponding trajectory path, moving the energy storage device corresponding to the authentication node into the verification pool, and performing isolation control;

[0042] The path extension module includes: if the authentication behavior path is interpreted as a new type of path, it is included in the authorization authentication path set through the boundary model extension mechanism.

[0043] Technical effects and advantages of the present invention:

[0044] 1. By building a trajectory graph structure based on authentication behavior snapshots, combined with a double endorsement mechanism and path continuity judgment, it is possible to identify whether there are abnormal chains of "jumping to pseudo-legitimacy" in the authentication path, thereby promptly freezing and isolating energy storage nodes with potential forged authentication, preventing attackers from tampering with the master control path through edge devices to achieve role fraud and takeover;

[0045] 2. By modeling the authentication behavior path as a trajectory graph structure and using boundary models to constrain dimensions such as role level jumps, authentication depth, and time windows, the structural rationality of energy storage nodes during the master-slave switching process is judged, thereby ensuring that the dynamic adjustment process of grid load regulation has a stable authentication structure foundation;

[0046] 3. After an abnormal node is identified and isolated, the system performs multi-path state replay. By analyzing its state response deviation under typical control paths, it generates a credibility score, which serves as the basis for permanent isolation or recovery. This improves the ability to identify and handle "low-frequency but suspicious behavior" in grid energy storage configurations.

[0047] 4. By introducing a path credibility score and historical trajectory curve fitting mechanism, the behavioral evolution of low-frequency deviation paths is judged, and an extended credibility threshold is set as the basis for judgment. This allows the ability to dynamically incorporate new authentication structures without sacrificing authentication security, maintaining the adaptability of the control system to new energy configuration forms.

[0048] 5. By constructing a behavioral evolution vector based on the historical freezing times, recovery records, and risk score fluctuations of the frozen path, a sensitivity score is generated and the authentication endorsement strength and jump frequency tolerance of the path are dynamically adjusted accordingly to prevent gray paths from repeatedly exploiting vulnerabilities to deceive boundaries. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The figure is a flow chart of the method steps of the present invention.

[0050] Figure 2 Schematic diagram of the system module of the present invention.

[0051] Figure 3 Flowchart of the authentication path establishment and endorsement verification of the present invention.

[0052] Figure 4 Flowchart of structural risk scoring and novel pathway expansion of the present invention.

[0053] Figure 5 This is a flow chart of the behavior replay and trust judgment of a frozen device of the present invention.

[0054] Figure 6 This is a flow chart of the gray path sensitive tracking and jump control strategy adjustment of the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] Refer to the instruction manual Figure 1-6 According to an embodiment of the present invention, a method for optimizing energy storage configuration in combination with grid adaptive regulation is provided; the method is used to prevent unauthorized role takeover in a distributed energy storage network with dynamic master-slave role switching, and the method includes:

[0057] Generate an authentication behavior snapshot based on the authentication behavior of the energy storage node, wherein the authentication behavior snapshot includes an authentication initiator identifier, a target identity identifier, a role switching flag, an authentication timestamp, and an authentication type tag;

[0058] Constructing an authentication trajectory graph structure according to the authentication behavior snapshot, wherein the trajectory graph includes multiple authentication nodes and their authentication jump paths;

[0059] During each authentication jump, the first endorsement signature is obtained from the previous authentication node, and the second endorsement signature is obtained from at least one auxiliary node;

[0060] Determining whether the authentication jump path satisfies a preset role switching authorization path set in the boundary model;

[0061] If the jump path does not meet the authorization structure requirements of the boundary model, the corresponding trajectory path is frozen, and the energy storage device corresponding to the authentication node is moved into the verification pool to perform isolation control;

[0062] If the authentication behavior path is interpreted as a new type of path, it is included in the authorization authentication path set through the boundary model extension mechanism.

[0063] The authentication behavior snapshot is used to represent an information set of the complete status of the authentication process. The authentication behavior snapshot includes authentication behavior elements, which include identity identification elements, behavior attribute elements, and role instruction elements. The identity identification elements include the authentication initiator identifier and the target identifier, the behavior attribute elements include the authentication timestamp and the authentication type tag, and the role instruction elements include the role switching flag.

[0064] The authentication behavior snapshot is generated by the authentication behavior collection mechanism by performing a structured combination of various elements, and is embedded into the authentication trajectory graph structure in the form of authentication records, forming the basis for expressing the status of each authentication node;

[0065] During the execution of the authentication path, the authentication behavior snapshot is called as the structural input for judging the legitimacy of the trajectory path, and the integrity of the node behavior chain is judged through the continuity and combination of snapshots.

[0066] The authentication trajectory graph structure is used to represent the authentication node behavior jump relationship. Each authentication node in the authentication trajectory graph structure is composed of an authentication behavior snapshot, and its jump edge is generated by authentication jump data.

[0067] The authentication jump data includes authentication behavior snapshot index information, previous jump node identification information and endorsement signature identification, and the authentication jump data is used to establish the directionality and credibility of the jump edge;

[0068] The authentication trajectory graph structure continuously expands the combination of nodes and edges during the continuous updating of authentication behavior, and embeds the endorsement signature identifier in each jump into the jump edge information to identify its trust source; based on the authentication trajectory graph structure, structural mapping processing is performed on all authentication paths, and the behavior path sequence formed by the nodes and jump edges in the authentication trajectory graph structure is used as input to perform boundary model matching judgment to support the subsequent risk assessment process.

[0069] Each authentication jump satisfies the dual endorsement condition, which includes the endorsement of the source node and the endorsement of the auxiliary node;

[0070] The source node endorsement is signed by the previous authentication node in the jump path based on the current authentication behavior snapshot and is bound to the corresponding authentication jump data;

[0071] The auxiliary node endorsement is generated by an energy storage node with the same role level. The energy storage node generates a response signature after performing a consistency comparison on the authentication behavior snapshot based on the historical status data collected in its behavior observation window;

[0072] A consistency check is performed on the source node endorsement and the auxiliary node endorsement. If any endorsement chain is found to have inconsistent direction, missing signature, or snapshot matching failure, the authentication jump behavior is marked as a potential risk path. The potential risk path is suspended, and the jump edge formed by it is frozen in the authentication trajectory graph structure and recorded in the risk assessment result set.

[0073] What needs to be further explained about the above scheme is that the jump action behavior snapshot difference function ΔS(X,Y) is constructed to measure the semantic consistency between the "current jump" and the "endorsement reference snapshot", which forms the behavioral basis of the subsequent judgment logic; c The current authentication behavior is a snapshot structure; S s The snapshot structure of the previous hop recorded by the source node; It is the snapshot structure of the i-th auxiliary node; the snapshot structure includes: identity pair ID f , ID t , role flag R, timestamp T, authentication tag L; X is the current authentication behavior snapshot; Y is the snapshot referenced by the endorsement source;

[0074] Define the snapshot difference function:

[0075]

[0076] Where: Enc(·) is the identity pair encoding function, encoding the initiator / target into a vector; ||·|| H is the Hamming distance, which is used to express the discrete difference between identity pairs; φ(T X ,TY )=log(1+|T X -T Y |) is the time window offset function; Hamming (L X ,L Y ) is the degree of label heterogeneity; λ1, λ2, λ3, and λ4 represent the importance of each dimension difference respectively;

[0077] Define the double endorsement validity joint function Unified evaluation of the endorsement information of the source node and multiple auxiliary nodes, and output of the credibility score;

[0078] Proposed ΔS s =ΔS(S c ,S s ); N a is the total number of auxiliary nodes; Θ(·) is the step logic function; construct the joint validity function:

[0079] exist The first term in the formula expresses "whether the source node endorsement is credible", and the second term expresses "the average consistency of the auxiliary nodes". Each term in the formula is converted to the inverse of the trust distance to enhance the sensitivity to differences. Θ1 and Θ2 are expressed in the following form:

[0080] where α k Control function steepness, δ k is the trust threshold set by the system; x represents the trust score input corresponding to a certain type of endorsement; k represents the index number of the endorsement type, which is used to distinguish between the source node endorsement k=1 and the auxiliary node endorsement k=2;

[0081] pass Defines the logical result indicating whether the current authentication jump is judged as a trusted path under double endorsement verification. The final jump trust judgment condition is:

[0082]

[0083] where γ min is the minimum credibility score threshold set; when When the authentication jump path is marked as a potential risk path, it is written into the frozen list and the risk assessment result set.

[0084] The boundary model is used to determine a set of rules for determining the validity of the authentication jump path structure. The rule set is constructed based on the authentication path structure constraint logic, which includes: role level transfer rules, authentication jump depth limit, node continuity requirements and path time window matching rules;

[0085] Receive path information from the authentication trajectory graph structure, and combine it with the role switching flag and timestamp data in the corresponding authentication behavior snapshot to perform matching analysis based on the authentication path structure constraint logic; when the path does not meet the structure constraint logic and its structural combination is in the risk-high-risk interval in the path sample, calculate its risk score through risk assessment;

[0086] If the risk score is higher than the preset threshold, the authentication jump path will be marked as a structural abnormal path, and the relevant jump edges will be frozen, and the node will be isolated.

[0087] Regarding the above, it is necessary to further explain that based on the path deviation under the structural hierarchy and integrating the constraints of multiple structural dimensions, the path structure deviation function Ψ is constructed dev ;Ψ dev =η1·f lvl (ΔR)+η2·f dep (D)+η3·f con (ρ)+η4·f win (τ);

[0088] where Ψ dev is the path structure deviation score, which is used to measure the degree of deviation between the jump path and the system-set structure rules. The larger the value of the path structure deviation score, the more serious the deviation. η1, η2, η3, η4 are the corresponding weight functions in the formula; f lvl (ΔR) is the role level transfer function, which represents the nonlinear amplification score of the difference between the source and target role levels in the jump; ΔR = |R s -R t |, where R s ,R t are the role level identifiers of the source node and the target node, respectively, expressed using integer values; f dep (D) is the jump depth function, where D represents the jump depth of the path, the number of nodes; f con (ρ) is the node continuity function, which is used to measure the degree of structural breakage in the trajectory path; ρ∈[0,1] is the path continuity rate, 1 is completely continuous, and 0 is completely broken; f win (τ) is the time window mismatch function, where τ=|T s -T t |, represents the time difference between jump nodes, which is obtained from the timestamp in the snapshot;

[0089] The structural deviation is mapped to the structural risk score in the system, which represents the probability trend of the path causing structural inconsistency failure. The risk score function is constructed: R struct =tanh(Ψ dev·Φ(freq));

[0090] where R struct is the structural risk score, which has a range of (0, 1). The closer the value of the structural risk score is to 1, the higher the path risk. tanh(·) is the hyperbolic tangent function, which is used to compress the risk within an interval and maintain boundary controllability. Φ(freq) is the inverse function of the frequency of the path in the authentication trajectory, which avoids false positives for paths with high frequency. It is defined as:

[0091] Convert risk scores into trigger logic for system countermeasures and build a freeze trigger judgment function

[0092]

[0093] in To freeze the trigger instruction output, if If θ is 1, the path is frozen; freeze The structural risk tolerance threshold set for the system controls the sensitivity of the freeze trigger.

[0094] The energy storage device corresponding to the authentication node whose trajectory is frozen is isolated, which includes disconnecting the authentication chain contact relationship, temporarily freezing the control authority, and limiting the ability to transfer the authentication role;

[0095] At the beginning of the isolation process, the authentication behavior snapshot chain associated with the authentication node is saved and entered into the verification pool, and its behavior response process is recorded as a state trajectory archive. Based on the state trajectory, a behavior replay process is constructed to verify the state deviation performance of the energy storage device under different control paths, and the deviation results are used as the input basis for credibility determination.

[0096] The risk assessment generates a credibility score based on the state deviation performance. If the credibility score is lower than the set credibility threshold, the energy storage device is put into permanent isolation. If the score is within the pre-accepted deviation range, the energy storage device is put into observation and operates within the limited authentication frequency and response time window.

[0097] Construct the state response difference sequence of the frozen node under different control paths for subsequent replay analysis, based on which the state offset sequence O Δ To describe the state offset sequence generation;

[0098] O Δ ={Δo (1) ,Δo (2) ,…,Δo (m)},

[0099] The state offset sequence OΔ represents the state difference of the frozen node under m typical control paths; Δo( i ) is the single state offset value on the i-th path; The device response status under the current simulation control path, such as SOC change and response lag time; is the historical normal status value of the path; m is the number of typical paths set by the system;

[0100] Constructing the state shift intensity function Γ dev , Γ dev It is used to integrate the degree of deviation under all paths and amplify the deviation effect in a complex nonlinear way;

[0101]

[0102] where Γ dev is the state offset strength of the frozen node; ψ i is the importance function of the i-th path, which changes dynamically according to the historical participation of the device in the path; tanh(κ i ·Δo (i) ) is a smooth nonlinear offset function, which is used to suppress abnormal mutations and enhance the sensitivity to continuous offset; γ i is the offset amplification factor, which is used to enhance the expression of persistent anomalies under certain paths; ln(1+·) is the nonlinear compression mapping, which is used to enhance the dominant influence of large offset terms;

[0103] Construct a credibility scoring function that is ultimately used for decision-making and determines whether to permanently isolate:

[0104]

[0105] in The closer the device credibility score is to 1, the more credible the behavior is; iso The permanent isolation credibility threshold preset for the system; δ obs The credibility threshold preset for the system to enter the observation mechanism; It is an isolation policy instruction. An isolation policy instruction of 1 indicates permanent isolation. An isolation policy instruction of 0 indicates entering the observation mechanism, regulating the frequency limit combined with the response window contraction. An isolation policy instruction of -1 indicates that the authentication authority can be restored.

[0106] The overall logical flow of the above parts is explained as follows:

[0107] (1) The frozen node extracts the snapshot history, replays the response under multi-path control, and then obtains the offset sequence; (2) The nonlinear function fuses the offsets and then obtains the comprehensive offset strength; (3) It is mapped into a credibility score and then determines whether the isolation, observation or recovery conditions are met.

[0108] The boundary model expansion is based on the joint judgment and execution of new path identification, behavior evolution judgment and risk assessment; the new path identification extracts candidate paths based on the authentication trajectory graph structure with a frequency lower than a preset threshold and having offset characteristics, and records the associated authentication behavior snapshot sequence;

[0109] Perform consistency comparison on the authentication behavior snapshot sequence through behavior evolution judgment, match it to the historical authentication trajectory evolution curve and calculate the path credibility score;

[0110] Based on the path credibility score and the node historical offset record, a joint risk assessment is conducted to determine whether the candidate path is scalable.

[0111] If a candidate path exhibits a continuous behavior pattern in its evolutionary trend, and its state deviation fluctuates within the path historical deviation range standard, and its path credibility score is compared with the extended credibility score threshold and is determined to be lower than the extended credibility score threshold, the candidate path is included in the boundary model authorized path set and an extended label is set for it to track the consistency of its subsequent evolutionary path in terms of structural trend;

[0112] The new path deviation trajectory vector is modeled and the path behavior change vector is defined as:

[0113] in is the deviation trajectory vector of the new authentication path; ΔR is the jump amplitude of the current path relative to the standard role level, such as the difference between the role level of the previous legal path and the current path; ΔT is the jump time window offset; ΔL is the structural difference of the label sequence, expressed after quantization; ΔD is the change in the depth of the path structure, such as the change amplitude of the number of node layers; Used for subsequent comparison of behavior curves with historical path vectors;

[0114] Define the similarity between the new path and the most matching path in the historical authentication trajectory set, and construct the historical trajectory comparison similarity function Ω sim ;

[0115]

[0116] in is the set of historical authentication trajectory vectors; is the offset vector of the i-th historical authentication trajectory; is the offset of the new path in the jth behavioral dimension; j is the behavior dimension importance function, which is a dynamic function that represents the contribution of this dimension to path credibility under the current semantic context; tanh 2 (·) is the hyperbolic tangent square function, which is used to emphasize the fine-grained changes when the offset is small; in addition, sin The maximum similarity value is taken in the formula to capture the path closest to the historical structure for benchmarking judgment;

[0117] The path credibility score is calculated by combining the deviation amplitude with the historical trajectory fitting degree.

[0118] Path credibility score The higher the value of , the more credible the new path is; bias is the offset penalty term, which is used to express the overall offset strength of the path; j is the dimension penalty function; β j is the exponential amplification parameter, β j Used to enhance the pulling effect of high-bias dimensions on the overall penalty term;

[0119] Constructing a trusted extension judgment function Finally, the score is compared with the threshold to determine whether the new path should be included in the authorization boundary:

[0120]

[0121] in Indicates whether to extend the flag; ε expand The path trust score extension threshold set for the boundary model; if extended, the path is added to the boundary authorization model and marked with an extension tag for subsequent structure tracking;

[0122] Structural process summary: (1) Vector modeling of new path structure features → obtain (2) Compare the similarity with the historical authentication evolution path set → get Ω sim ; (3) Calculate the path structure deviation penalty at the same time, and then get Λ bias ; (4) Finally, the fusion calculation of the trust score Compare with the threshold and output whether to expand.

[0123] Construct a trajectory evolution record during the authentication behavior execution cycle and continuously track all path changes in the authentication trajectory graph structure; establish a trajectory freezing record table, organize the freezing reasons, recovery conditions and evolution results of all frozen paths, and construct a behavior label set;

[0124] The trajectory evolution record provides an input data source for future path credibility assessment. The path prediction model is trained based on the trajectory evolution record. The path prediction model is used to predict the future evolution trend of the authentication behavior path.

[0125] If a certification path is deemed a trusted path after multiple freezes, it will be marked as a gray behavior path. Future redirections must meet triple endorsement requirements and include risk score information for future reference. The sensitivity level of the gray behavior path will be dynamically updated, and the tolerance strategy for authentication redirection behavior will be adjusted accordingly to reduce the risk of path fraud interfering with the certification system.

[0126] Define the behavior evolution record vector of path p; construct trajectory evolution record modeling

[0127] in is the trajectory evolution history of path p; N freeze The cumulative number of times the path is frozen by the system; N recover The number of times the account is re-identified as trustworthy by the system after being frozen; The trend mean of all risk scores for the path; is the nonlinear fluctuation degree of risk score; T last The timestamp of the most recent restoration authentication of the path;

[0128] Constructing grey path sensitive scoring function S gray , S gray Used to determine whether there is a high probability of deception due to repeated freezing and restoring:

[0129]

[0130] Among them S gray is the gray sensitivity score of the path; ψ1, ψ2, ψ3, ψ4 are behavioral characteristic trade-off functions, which can be dynamically set according to the risk evolution model in practical applications; φ(t)=log(1+t 2 ) is the time decay function, which is used to indicate that the longer the recovery time is, the lower the sensitivity is; T now is the current moment; S gray Integrate four indicators: growth trend of freezing times, recovery frequency, score volatility, and active time to ensure the overall credibility of path behavior;

[0131] According to the sensitivity score, the minimum endorsement strength required for subsequent jump behavior of the path is set, and the endorsement strength requirement function μ is constructed. p ;

[0132]

[0133] The endorsement strength requirement function μp The minimum number of endorsements required for subsequent jumps of path p; is a rounded-up function; the minimum value of 2 in the formula is 2, which also represents double endorsement. If the sensitivity score is high, it will gradually be upgraded to triple and quadruple endorsement mechanisms. This prevents the path from being easily authenticated and jumped in a highly sensitive state, thereby enhancing anti-spoofing strength.

[0134] Construct authentication tolerance adjustment function δ p ; Define the time and frequency tolerance window adjustment factor of the subsequent jump behavior of the gray path in the authentication system;

[0135]

[0136] Where α is the slope adjustment parameter of the sensitivity score; β is the system sensitivity median, which is used to control the inflection point of the tolerance strategy; p →1 means no tolerance at all, that is, high risk, the authentication frequency and time window will be extremely compressed; if δ p →0 indicates the default strategy, which allows normal jumps;

[0137] For the overall description of the above formula: (1) Each path freezing and recovery process forms a structural behavior record (2) Construct a sensitivity score S based on freezing frequency, risk volatility, and recovery frequency gray (3) Sensitivity score controls the subsequent endorsement threshold μ p , to prevent the path from being easily reused; (4), and at the same time use the tolerance function δ p Adjust the system's ability to accept its authentication jump behavior.

[0138] An energy storage configuration optimization system combined with grid adaptive regulation includes a snapshot generation module, a trajectory mapping module, an endorsement verification module, a path determination module, an abnormality isolation module, and a path expansion module;

[0139] The snapshot generation module generates an authentication behavior snapshot based on the authentication behavior of the energy storage node, wherein the authentication behavior snapshot includes an authentication initiator identifier, a target identifier, a role switching flag, an authentication timestamp, and an authentication type tag; the snapshot generation module is used to describe the complete behavior information of the authentication status;

[0140] The trajectory mapping module constructs an authentication trajectory graph structure according to the authentication behavior snapshot, wherein the trajectory graph includes multiple authentication nodes and their authentication jump paths; the trajectory mapping module is used to represent the authentication behavior association relationship between energy storage devices;

[0141] The endorsement verification module is used to obtain the first endorsement signature from the previous authentication node and the second endorsement signature from at least one auxiliary node during each authentication jump process; the endorsement verification module is used to confirm the trusted source of the authentication jump path;

[0142] The path determination module is used to determine whether the authentication jump path meets the role switching authorization path set preset in the boundary model, and output the path matching result through the path determination module;

[0143] The abnormal isolation module includes: if the jump path does not meet the authorization structure requirements of the boundary model, freezing the corresponding trajectory path, moving the energy storage device corresponding to the authentication node into the verification pool, and performing isolation control;

[0144] The path extension module includes: if the authentication behavior path is interpreted as a new type of path, it is included in the authorization authentication path set through the boundary model extension mechanism.

[0145] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing energy storage configuration combined with grid adaptive control, characterized by: The method is used to prevent unauthorized role takeover in a distributed energy storage network with dynamic master-slave role switching, and the method includes: Generate an authentication behavior snapshot based on the authentication behavior of the energy storage node, wherein the authentication behavior snapshot includes an authentication initiator identifier, a target identity identifier, a role switching flag, an authentication timestamp, and an authentication type tag; Constructing an authentication trajectory graph structure according to the authentication behavior snapshot, wherein the trajectory graph includes multiple authentication nodes and their authentication jump paths; During each authentication jump, the first endorsement signature is obtained from the previous authentication node, and the second endorsement signature is obtained from at least one auxiliary node; Determining whether the authentication jump path satisfies a preset role switching authorization path set in the boundary model; If the jump path does not meet the authorization structure requirements of the boundary model, the corresponding trajectory path is frozen, and the energy storage device corresponding to the authentication node is moved into the verification pool to perform isolation control; If the authentication behavior path is interpreted as a new type of path, it is included in the authorization authentication path set through the boundary model extension mechanism.

2. The energy storage configuration optimization method combined with grid adaptive control according to claim 1 is characterized by: The authentication behavior snapshot is used to represent an information set of the complete status of the authentication process. The authentication behavior snapshot includes authentication behavior elements, which include identity identification elements, behavior attribute elements, and role instruction elements. The identity identification element includes the authentication initiator identifier and the target identifier, the behavior attribute element includes the authentication timestamp and the authentication type tag, and the role instruction element includes the role switching flag; The authentication behavior snapshot is generated by the authentication behavior collection mechanism by performing a structured combination of various elements, and is embedded into the authentication trajectory graph structure in the form of authentication records, forming the basis for expressing the status of each authentication node; During the execution of the authentication path, the authentication behavior snapshot is called as the structural input for judging the legitimacy of the trajectory path, and the integrity of the node behavior chain is judged through the continuity and combination of snapshots.

3. The energy storage configuration optimization method combined with grid adaptive control according to claim 2, characterized in that: The authentication trajectory graph structure is used to represent the authentication node behavior jump relationship. Each authentication node in the authentication trajectory graph structure is composed of an authentication behavior snapshot, and its jump edge is generated by authentication jump data. The authentication jump data includes authentication behavior snapshot index information, previous jump node identification information and endorsement signature identification, and the authentication jump data is used to establish the directionality and credibility of the jump edge; The authentication trajectory graph structure continuously expands the combination of nodes and edges during the continuous update of authentication behavior, and embeds the endorsement signature identifier in each jump into the jump edge information to identify its trust source; Based on the authentication trajectory graph structure, structural mapping processing is performed on all authentication paths, and boundary model matching judgment is performed using the behavior path sequence formed by the nodes and jump edges in the authentication trajectory graph structure as input to support the subsequent risk assessment process.

4. The energy storage configuration optimization method combined with grid adaptive control according to claim 3 is characterized by: Each authentication jump satisfies the dual endorsement condition, which includes the endorsement of the source node and the endorsement of the auxiliary node; The source node endorsement is signed by the previous authentication node in the jump path based on the current authentication behavior snapshot and is bound to the corresponding authentication jump data; The auxiliary node endorsement is generated by an energy storage node with the same role level. The energy storage node generates a response signature after performing a consistency comparison on the authentication behavior snapshot based on the historical status data collected in its behavior observation window; Perform consistency checks on the source node endorsement and the auxiliary node endorsement. If any endorsement chain is found to have inconsistent direction, missing signature, or snapshot matching failure, the authentication jump behavior will be marked as a potential risk path; The potential risk path is suspended, and the jump edges formed by it are frozen in the authentication trajectory graph structure and recorded in the risk assessment result set.

5. The energy storage configuration optimization method combined with grid adaptive control according to claim 4 is characterized in that: The boundary model is used to determine a set of rules for determining the validity of the authentication jump path structure. The rule set is constructed based on the authentication path structure constraint logic, which includes: role level transfer rules, authentication jump depth limit, node continuity requirements and path time window matching rules; Receive path information from the authentication trajectory graph structure, and combine it with the role switching flag and timestamp data in the corresponding authentication behavior snapshot to perform matching analysis based on the authentication path structure constraint logic; when the path does not meet the structure constraint logic and its structural combination is in the risk-high-risk interval in the path sample, calculate its risk score through risk assessment; If the risk score is higher than the preset threshold, the authentication jump path will be marked as a structural abnormal path, and the relevant jump edges will be frozen, and behavioral isolation will be performed on the nodes.

6. The energy storage configuration optimization method combined with grid adaptive control according to claim 5, characterized in that: The energy storage device corresponding to the authentication node whose trajectory is frozen is isolated, which includes disconnecting the authentication chain contact relationship, temporarily freezing the control authority, and limiting the ability to transfer the authentication role; At the beginning of the isolation process, the authentication behavior snapshot chain associated with the authentication node is saved and entered into the verification pool, and its behavior response process is recorded as a state trajectory archive. Based on the state trajectory, a behavior replay process is constructed to verify the state deviation performance of the energy storage device under different control paths, and the deviation results are used as the input basis for credibility determination. The risk assessment generates a credibility score based on the state deviation performance. If the credibility score is lower than the set credibility threshold, the energy storage device will be put into permanent isolation state. If the score is within the pre-accepted deviation range, the energy storage device is subject to the observation mechanism and operates within the limited authentication frequency and response time window.

7. The energy storage configuration optimization method combined with grid adaptive control according to claim 6, characterized in that: The boundary model expansion is based on the joint judgment and execution of new path identification, behavior evolution judgment and risk assessment; the new path identification extracts candidate paths based on the authentication trajectory graph structure with a frequency lower than a preset threshold and having offset characteristics, and records the associated authentication behavior snapshot sequence; Perform consistency comparison on the authentication behavior snapshot sequence through behavior evolution judgment, match it to the historical authentication trajectory evolution curve and calculate the path credibility score; Based on the path credibility score and the node historical offset record, a joint risk assessment is conducted to determine whether the candidate path is scalable. If a candidate path exhibits a continuous behavior pattern in its evolutionary trend, and its state deviation fluctuates within the path historical deviation range standard, and its path credibility score is compared with the extended credibility score threshold and is determined to be lower than the extended credibility score threshold, then the candidate path is included in the boundary model authorized path set and an extended label is set for it to track the consistency of its subsequent evolutionary path in structural trends.

8. The energy storage configuration optimization method combined with grid adaptive control according to claim 7, characterized in that: Construct a trajectory evolution record during the authentication behavior execution cycle and continuously track all path changes in the authentication trajectory graph structure; establish a trajectory freezing record table, organize the freezing reasons, recovery conditions and evolution results of all frozen paths, and construct a behavior label set; The trajectory evolution record provides an input data source for future path credibility assessment. The path prediction model is trained based on the trajectory evolution record. The path prediction model is used to predict the future evolution trend of the authentication behavior path. If a certification path is identified as a trusted path after multiple rounds of freezing, it will be marked as a gray behavior path. Future jumps must meet the triple endorsement requirements and be accompanied by risk score information for reference; Dynamically update the sensitivity level of the gray behavior path and adjust the tolerance strategy of the authentication jump behavior accordingly to reduce the risk of interference of path deception behavior on the authentication system.

9. A system for optimizing energy storage configuration combined with grid adaptive control, comprising a snapshot generation module, a trajectory mapping module, an endorsement verification module, a path determination module, an anomaly isolation module, and a path expansion module, characterized in that: The snapshot generation module generates an authentication behavior snapshot based on the authentication behavior of the energy storage node, wherein the authentication behavior snapshot includes an authentication initiator identifier, a target identifier, a role switching flag, an authentication timestamp, and an authentication type tag; the snapshot generation module is used to describe the complete behavior information of the authentication status; The trajectory mapping module constructs an authentication trajectory graph structure according to the authentication behavior snapshot, wherein the trajectory graph includes multiple authentication nodes and their authentication jump paths; the trajectory mapping module is used to represent the authentication behavior association relationship between energy storage devices; The endorsement verification module is used to obtain the first endorsement signature from the previous authentication node and the second endorsement signature from at least one auxiliary node during each authentication jump process; the endorsement verification module is used to confirm the trusted source of the authentication jump path; The path determination module is used to determine whether the authentication jump path meets the role switching authorization path set preset in the boundary model, and output the path matching result through the path determination module; The abnormal isolation module includes: if the jump path does not meet the authorization structure requirements of the boundary model, freezing the corresponding trajectory path, moving the energy storage device corresponding to the authentication node into the verification pool, and performing isolation control; The path extension module includes: if the authentication behavior path is interpreted as a new type of path, it is included in the authorization authentication path set through the boundary model extension mechanism.

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