Demand matching method for electric power 5G-Advanced communication and inductance integration and intelligent substation business

Abstract

The invention discloses a demand matching method for 5G-Advanced communication and inductance integration and intelligent substation business, and relates to the technical field of electric power system communication, the method comprises the following steps: acquiring business demands of an intelligent substation, combining an analytic hierarchy process to generate a demand vector with a weight, and synchronously constructing a 5G-Advanced communication and inductance integration technical feature vector; the method comprises the following steps: establishing a technology-demand incidence matrix based on an adaptability principle, carrying out dual-path dimension reduction, realizing multi-attribute decision matching by utilizing a TOPSIS-VIKOR fusion evaluation model, forming closed-loop optimization through resource monitoring and a dynamic redistribution mechanism, and finally achieving Pareto optimization of a matching degree and resource consumption. According to the method, the matching precision and the system adaptive capacity are improved, and the high-reliability, low-time-delay and multi-service fusion communication sensing requirements of the intelligent substation are effectively met.

Description

Technical Field

[0001] This invention relates to the field of power system communication, and in particular to a method for matching the needs of power 5G-Advanced integrated sensing and communication with smart substation services. Background Technology

[0002] With the development of 5G-Advanced technology, the integration of communication and sensing functions has become a key feature of the next generation of wireless networks, widely applied in fields such as the Industrial Internet, intelligent transportation, and energy and power. In smart substation scenarios, business requirements exhibit characteristics of high reliability, ultra-low latency, multi-source heterogeneous data fusion, and wide-area precise sensing, which traditional communication networks struggle to meet. Existing technologies include 5G-based power communication solutions that support various service transmissions within substations through network slicing and edge computing. Some systems introduce independent sensing modules for equipment status monitoring. However, communication and sensing resources remain in a state of separate scheduling, lacking a unified modeling and collaborative optimization mechanism. This fails to effectively address the precise adaptation between 5G-Advanced integrated communication and sensing technology parameters and the diverse, non-steady-state business requirements of smart substations. Especially in environments with multi-objective conflicts, resource constraints, and dynamic changes, achieving a balance between security, efficiency, and feasibility is difficult, resulting in low matching accuracy, slow response speed, and low resource utilization, ultimately failing to guarantee the stable operation of critical power grid services. Summary of the Invention

[0003] To address the above problems, this invention provides a method for matching the needs of power 5G-Advanced sensing integration with smart substation services, the method comprising: S1. Collect business demand data of smart substations. Based on the business demand data, quantify business demand indicators through a multi-attribute utility theory model. Determine the weight of each business demand indicator through the analytic hierarchy process combined with expert scoring, and generate a business demand vector with weight coefficients. S2. Collect 5G-Advanced integrated sensing technology parameters, including quantitative indicators of communication performance, sensing accuracy, fusion capability and reliability, and generate technical feature vectors; S3. Based on business demand vectors and technical feature vectors, construct a technology-demand correlation matrix through a preset adaptability principle, and perform dual-path dimensionality reduction on the technology-demand correlation matrix; S4. Based on the reduced-dimensional technology-demand correlation matrix, a hybrid multi-attribute decision model is constructed through the TOPSIS-VIKOR fusion evaluation mechanism to match the sensor integration technology parameters with business requirements. S5. Input the matching result into the resource-efficiency joint optimizer. By deploying a resource monitoring platform in the substation to monitor the consumption of communication resources in real time, when the consumption of communication resources exceeds the preset threshold, the resource reallocation mechanism is triggered, and the matching process of steps S3-S4 is repeated until the Pareto optimal matching result that satisfies the "matching degree-resource consumption" is obtained.

[0004] Furthermore, in step S1, the weights of each business requirement indicator are determined by combining the analytic hierarchy process (AHP) with expert scoring. The specific method is as follows: S1.1 Construct a hierarchical structure model of smart substation business requirements, dividing the smart substation business requirements into an objective layer, a criterion layer, and an indicator layer, wherein: Target layer: Optimal business requirement vector; Criterion Layer: Four primary criteria are set: system security, power control reliability, equipment sensing accuracy, and service carrying flexibility; Indicator layer: This includes specific requirement indicators corresponding to each primary criterion. These requirements indicators include security encryption level, communication latency, transmission success rate, positioning accuracy, identification accuracy, connection density, and adjustable bandwidth range. S1.2 Based on a pre-configured expert knowledge base, the 1-9 scale method is used to compare the importance of each indicator element under the same criterion layer pairwise and establish a judgment matrix. S1.3 Calculate the largest eigenvalue and the corresponding eigenvector of the judgment matrix, and after normalizing the eigenvector, obtain the preliminary weight coefficients of each indicator element. S1.4 Calculate the consistency index CI and the random consistency ratio CR. When CR≤0.1, the weight allocation is deemed reasonable, and the final weight coefficient is output. When CR>0.1, the judgment matrix is ​​reconstructed, and steps S1.3-S1.4 are repeated until the consistency requirements are met.

[0005] Furthermore, in step S3, the technology-demand correlation matrix is ​​based on a preset adaptability principle. It calculates the correlation degree between each demand indicator in the business demand vector and each technical parameter in the technology feature vector. The correlation degree is quantified according to the degree to which the technical parameters satisfy the demand indicators.

[0006] Furthermore, the pre-defined adaptability principles include the principle of prioritizing business security, the principle of perception-communication coupling, the principle of multi-dimensional dynamic scalability, and the principle of resource constraint compatibility.

[0007] Furthermore, in step S3, a two-path dimensionality reduction is performed on the technology-demand correlation matrix, including: Technology-side dimensionality reduction: For the technology feature vector in the technology-demand correlation matrix, the principal component analysis algorithm is used to calculate the variance contribution rate of each technology parameter in the technology feature vector. By retaining the principal components with a cumulative contribution rate of ≥95%, the core technology factors are extracted. Business-side dimensionality reduction: For the demand feature vectors in the technology-demand correlation matrix, a local linear embedding algorithm is used to maintain the topological correlation between typical business scenarios and extract the intrinsic dimensions of business requirements. The demand indicators of the demand feature vectors are merged into core demand clusters including security, control, perception and management categories.

[0008] Furthermore, in step S4, a hybrid multi-attribute decision model is constructed using the TOPSIS-VIKOR fusion evaluation mechanism to match business requirements with the parameters of the integrated sensor technology. The specific process is as follows: S4.1 Based on the reduced-dimensional technology-demand correlation matrix, define the positive ideal solution and negative ideal solution corresponding to each integrated sensing technology parameter scheme. The positive ideal solution is the set of optimal values ​​of each technology parameter scheme to adapt to the business demand index, and the negative ideal solution is the set of worst values ​​of each technology parameter scheme to adapt to the business demand index. S4.2 Calculate the relative closeness between each technical parameter scheme and the positive and negative ideal solutions using the TOPSIS model; S4.3 When there are conflicts among business requirement indicators, the VIKOR model should be used to evaluate the various technical parameter solutions, including: Calculate the group utility value of each technical parameter scheme. The group utility value is obtained by weighted summation of the differences between each technical parameter scheme and the positive ideal solution in each business demand indicator. Calculate the individual regret value for each technical solution. The individual regret value is the maximum difference between each technical parameter solution and the ideal solution among all business requirement indicators. Based on the group utility value, individual regret value, minimum group utility value, and maximum individual regret value, calculate the compromise evaluation value of each technical parameter scheme; S4.4 Sort the technical parameter schemes according to the compromise evaluation value, and select the technical parameter scheme with the smallest compromise evaluation value as the final matching scheme; when there are multiple technical parameter schemes with the smallest compromise evaluation value, select the technical parameter scheme with the largest relative closeness as the final matching scheme.

[0009] Furthermore, in step S5, the resource reallocation mechanism specifically includes: S5.1. The resource monitoring platform deployed in the substation monitors the consumption of communication resources in real time. The evaluation indicators of communication resource consumption include communication channel occupancy rate, computing resource utilization rate and perceived service load indicators. S5.2 When the evaluation indicators exceed the corresponding preset thresholds, a resource reallocation process is triggered to dynamically adjust beamforming parameters and edge computing resource scheduling, including: Adjust beamforming parameters: Based on channel occupancy and signal interference intensity, increase the signal gain of beamforming; Adjust edge computing resource scheduling: Based on computing resource utilization and perceived service load indicators, allocate additional computing power to high-priority services, while temporarily downgrading low-priority services. S5.3 Based on the adjusted beamforming gain and edge computing power parameters, reconstruct the technical feature vector and business requirement vector, and re-execute the matching process; S5.4. Verify the matching results using a non-dominated sorting genetic algorithm, and construct a bi-objective optimization model with matching degree as the maximization objective and resource consumption as the minimization objective. S5.5. Through non-dominated hierarchical processing, reference point guidance, and constraint handling using a non-dominated sorting genetic algorithm, a Pareto optimal solution set is generated. S5.6 If the result of the rematch is at the Pareto optimal frontier, it is determined as the final matching result; otherwise, return to step S5.2 to readjust the parameters until the Pareto optimal requirement is met.

[0010] The beneficial effects of this invention are: This invention provides a demand matching method for power 5G-Advanced integrated sensing and communication systems and smart substation services. It quantifies service demands using multi-attribute utility theory and the analytic hierarchy process (AHP), and constructs a high-dimensional correlation matrix using technical feature vectors, achieving a structured expression of the correlation between service demands and sensing capabilities. Furthermore, it reduces computational complexity through dual-path dimensionality reduction, improving processing efficiency while ensuring the integrity of key information. The TOPSIS-VIKOR fusion decision mechanism effectively solves the matching problem under multi-objective conflicts, enhancing decision robustness. Finally, it continuously optimizes resource allocation through resource monitoring and closed-loop reallocation mechanisms, approaching the Pareto optimality of matching degree and resource consumption. This solves the problems of low matching accuracy, poor adaptability, and uneconomical resource utilization in existing technologies, significantly improving the intelligence level and operational stability of the smart substation communication and sensing system. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the overall process of the power 5G-Advanced integrated sensing and intelligent substation service demand matching method provided by the present invention. Detailed Implementation

[0012] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0013] Example 1 Please see Figure 1 A method for matching the needs of 5G-Advanced power sensing integration with smart substation services, the method comprising: S1. Collect business demand data of smart substations. Based on the business demand data, quantify business demand indicators through a multi-attribute utility theory model. Determine the weight of each business demand indicator through the analytic hierarchy process combined with expert scoring, and generate a business demand vector with weight coefficients. S2. Collect 5G-Advanced integrated sensing technology parameters, including quantitative indicators of communication performance, sensing accuracy, fusion capability and reliability, and generate a technical feature vector; S3. Based on the business demand vector and the technical feature vector, construct a technology-demand correlation matrix through a preset adaptability principle, and perform dual-path dimensionality reduction on the technology-demand correlation matrix; S4. Based on the reduced-dimensional technology-demand correlation matrix, a hybrid multi-attribute decision model is constructed through the TOPSIS-VIKOR fusion evaluation mechanism to match the sensor integration technology parameters with business requirements. S5. Input the matching result into the resource-efficiency joint optimizer. By deploying a resource monitoring platform in the substation to monitor the consumption of communication resources in real time, when the consumption of communication resources exceeds the preset threshold, the resource reallocation mechanism is triggered, and the matching process of steps S3-S4 is repeated until the Pareto optimal matching result that satisfies the "matching degree-resource consumption" is obtained.

[0014] Specifically, this invention achieves multi-dimensional dynamic adaptation of integrated sensory technology resources to business scenarios by constructing a closed-loop matching system from demand modeling, technology characterization, correlation analysis to dynamic optimization.

[0015] On the business side, data on various business requirements of smart substations is collected, and multi-attribute utility theory is used to quantify business indicators of different dimensions (such as communication latency, positioning accuracy, and connection density) to ensure that business requirements are measurable and comparable. The Analytic Hierarchy Process (AHP) is adopted, and a domain expert scoring mechanism is introduced to scientifically determine the relative importance weights of each business requirement indicator, thereby forming a weighted business requirement vector that reflects priority differences, providing an accurate requirement benchmark for subsequent matching.

[0016] On the technical side, key parameters of the 5G-Advanced integrated sensing system were comprehensively collected, including communication performance (such as peak rate and bit error rate), sensing accuracy (such as distance resolution and angle estimation error), fusion capabilities (such as sensing collaboration efficiency and spectrum reuse rate), and system reliability (such as lifetime and anti-interference capability). Data sources covered actual measurements, simulations, and algorithm complexity analysis. After standardizing the technical parameters, technical feature vectors were constructed to accurately characterize the service capabilities of currently available integrated sensing technologies. The constructed technical feature vectors highlight the comprehensive performance under the integrated sensing architecture and reflect the technical advantages of 5G-Advanced in natively supporting sensing functions.

[0017] As a preferred embodiment, in step S1, the weights of each business requirement indicator are determined by combining the analytic hierarchy process (AHP) with expert scoring. The specific method is as follows: S1.1 Construct a hierarchical structure model of smart substation business requirements, dividing the smart substation business requirements into an objective layer, a criterion layer, and an indicator layer, wherein: Target layer: Optimal business requirement vector; Criterion Layer: Four primary criteria are set: system security, power control reliability, equipment sensing accuracy, and service carrying flexibility; Indicator layer: This includes specific requirement indicators corresponding to each primary criterion. These requirements indicators include security encryption level, communication latency, transmission success rate, positioning accuracy, identification accuracy, connection density, and adjustable bandwidth range. S1.2 Based on a pre-configured expert knowledge base, the 1-9 scale method is used to compare the importance of each indicator element under the same criterion layer pairwise and establish a judgment matrix. S1.3 Calculate the largest eigenvalue and the corresponding eigenvector of the judgment matrix, and after normalizing the eigenvector, obtain the preliminary weight coefficients of each indicator element. S1.4 Calculate the consistency index CI and the random consistency ratio CR. When CR≤0.1, the weight allocation is deemed reasonable, and the final weight coefficient is output. When CR>0.1, the judgment matrix is ​​reconstructed, and steps S1.3-S1.4 are repeated until the consistency requirements are met.

[0018] Specifically, a three-tiered hierarchical structure model is constructed to organize complex business requirement systems, wherein: Target layer: Optimal business requirement vector, representing the final desired comprehensive requirement expression.

[0019] Criterion Layer: Four primary criteria are set: system security, power control reliability, equipment sensing accuracy, and service carrying flexibility. These correspond to the core guarantee capabilities of power system operation, the stability of key control links, the technical performance of sensing services, and the system's adaptability to dynamic load changes.

[0020] The indicator layer includes specific requirements for each primary criterion. These requirements include security encryption level, communication latency, transmission success rate, positioning accuracy, identification accuracy, connection density, and adjustable bandwidth, covering the main performance requirements for communication and sensing fusion services in typical smart substation scenarios.

[0021] Based on a hierarchical model of smart substation business requirements, a 1-9 scale method is introduced to compare each indicator at the same criterion level pairwise, forming a judgment matrix. The relative importance of different indicators is numerically expressed according to experts' perceptions. For example, if "communication latency" is considered significantly more important than "connection density," it is assigned a score of 5 or 7, thus achieving a quantitative transformation from qualitative judgment. The construction of the judgment matrix relies on a pre-configured expert knowledge base, which integrates professional opinions from multiple fields such as power grid dispatching, automation control, and communication engineering, ensuring the professionalism and representativeness of the evaluation process.

[0022] By solving for the largest eigenvalue and its corresponding eigenvector in the judgment matrix, preliminary weight coefficients for each indicator are obtained. These weight coefficients reflect the relative importance of each indicator under its respective criterion. After normalization, an initial weight set is formed. Since human judgments may be inconsistent (e.g., A is superior to B, B is superior to C, but C is superior to A), the logical rationality of the weight results needs to be verified. By calculating the consistency index CI (ConsistencyIndex) and the random consistency ratio CR (ConsistencyRatio), when CR ≤ 0.1, it indicates that the judgment matrix has an acceptable level of consistency and the weight allocation is reliable; otherwise, it indicates significant contradictions in the expert judgments, requiring readjustment of the judgment matrix and iterative execution of the weight calculation process until the consistency requirement is met.

[0023] This embodiment addresses the multi-dimensional and heterogeneous business needs in smart substations. In the process of generating weighted business requirement vectors, it proposes a weight determination mechanism based on the analytic hierarchy process (AHP) combined with expert scoring and mathematical consistency testing. By combining structured modeling and quantitative evaluation, it improves the scientificity and objectivity of the weight allocation of business requirement indicators, avoiding the subjective arbitrariness and logical contradictions that exist in traditional experience-based weighting.

[0024] As a preferred embodiment: In step S3, the technology-demand correlation matrix is ​​based on a preset adaptability principle. The correlation degree is calculated by comparing each demand indicator in the business demand vector with each technical parameter in the technology feature vector. The correlation degree is quantified according to the degree to which the technical parameters satisfy the demand indicators.

[0025] Specifically, the technological capabilities of 5G-Advanced integrated sensing are structurally mapped to the diverse business needs of smart substations, constructing a technology-demand correlation matrix. This matrix uses business demand indicators as columns and technical parameters as rows. Each matrix element represents the degree to which a specific technical parameter supports a particular business demand indicator. The values ​​are quantified through segmented threshold mapping, reflecting the extent to which the technical parameter meets the corresponding business demand. This quantification process relies on pre-defined adaptability principles as the basis for judgment, ensuring that the matching logic conforms to the actual constraints and priority requirements of power system operation.

[0026] As a preferred embodiment, the preset adaptability principles include the business security priority principle, the perception-communication coupling principle, the multi-dimensional dynamic scalability principle, and the resource constraint compatibility principle.

[0027] Specifically, the pre-defined adaptability principles are based on four dimensions: security assurance, resource coordination efficiency, system elastic response, and realistic constraints, forming a customized matching mechanism for power scenarios, which includes: Prioritizing Business Security: This principle is the core guideline for correlation mapping. In the power production sector, any solution linking technology and demand must strictly comply with mandatory security standards. Specifically, critical control services must meet three 100% hard targets: communication latency ≤10ms, transmission reliability ≥99.999%, and information security encryption strength ≥128-bit AES. When allocating sensing resources, dedicated protection slices must be pre-configured and resource isolation zones set up to ensure that even if sensing service congestion occurs, it will not affect the transmission of protection commands. When the system detects an anomaly in the sensing channel, it immediately triggers the "communication-sensing decoupling" emergency mechanism, prioritizing the release of sensing resources to ensure basic power control functions, and simultaneously activating Plan B communication links.

[0028] The Sensing-Communication Coupling Principle: This principle focuses on the essential characteristics of integrated sensing technology, addressing the issues of spectrum resource reuse and hardware resource coordination, including: Joint optimization model: A Pareto boundary equation for communication efficiency and sensing accuracy is established. By adjusting the OFDM signal cyclic prefix length and subcarrier spacing parameters, the same signal can achieve centimeter-level positioning accuracy for substation inspection robots and support 4K high-definition video transmission, achieving a peak rate of ≥100Mbps.

[0029] Spatiotemporal resource integration: By utilizing the flexible air interface of the 5G-Advanced frame structure, the sensing pulse sequence is embedded into the GP protection interval of the communication time slot, and the equipment temperature monitoring and status data back transmission are completed synchronously within a 1ms period, thereby improving spectrum utilization.

[0030] Multidimensional Dynamic Scalability Principle: This principle addresses the non-steady-state characteristics of smart substation business requirements, supporting flexible technology-demand mapping. When the system detects equipment anomalies, it automatically increases the refresh rate of partial discharge monitoring and simultaneously expands the bandwidth of associated communication slices. This process achieves decision optimization through online reinforcement learning algorithms, with response latency controlled within 300ms. Simultaneously, the model pre-defines four-dimensional expansion interfaces—business dimension, spatial dimension, temporal dimension, and resource dimension—and achieves system capacity doubling through tensor expansion algorithms.

[0031] Resource Constraint Compatibility Principle: This principle ensures the feasibility of implementing the technology-demand correlation mapping scheme and optimizes multiple constraint boundaries. It limits the construction cost of a single-site sensing system, employs a cost-aware scheduling algorithm to prioritize the use of common frequency bands while meeting performance thresholds, and activates cell merging energy-saving strategies when equipment density is high. Simultaneously, based on the computing power limit of the substation edge computing platform, it sets preprocessing rules for sensing data to ensure that the system load rate remains controllable.

[0032] This embodiment, through the synergistic effect of the above four principles, can construct a technology-demand correlation mapping logic system that combines security, efficiency, flexibility, and feasibility in complex and ever-changing power operation environments. In the process of constructing the technology-demand correlation matrix, customized adaptation rules for the power industry are introduced, solving the problems of traditional matching methods lacking domain specificity and easily deviating from the fundamental requirements of power grid safe operation. By clarifying the four principles of security priority, coupling optimization, dynamic expansion, and resource compatibility, the technical orientation of the matching process is standardized, improving the rationality and practicality of resource allocation, and enhancing the applicability and implementation capability of sensor-integrated technology in smart substation scenarios.

[0033] As a preferred embodiment, in step S3, a two-path dimensionality reduction is performed on the technology-demand correlation matrix, including: Technology-side dimensionality reduction: For the technology feature vector in the technology-demand correlation matrix, the principal component analysis algorithm is used to calculate the variance contribution rate of each technology parameter in the technology feature vector. By retaining the principal components with a cumulative contribution rate of ≥95%, the core technology factors are extracted. Business-side dimensionality reduction: For the demand feature vectors in the technology-demand correlation matrix, a local linear embedding algorithm is used to maintain the topological correlation between typical business scenarios and extract the intrinsic dimensions of business requirements. The demand indicators of the demand feature vectors are merged into core demand clusters including security, control, perception and management categories.

[0034] Specifically, the dimensionality reduction on the technical side adopts the principal component analysis algorithm. By calculating the covariance matrix between the original technical parameters, the eigenvalues ​​and corresponding eigenvectors are solved. Based on the variance contribution rate of each principal component, the top k principal components that can cumulatively explain ≥95% of the variability of the original data are selected as core technical factors, effectively eliminating multicollinearity and redundant information among technical indicators such as communication performance and sensing accuracy.

[0035] For business-side dimensionality reduction, a local linear embedding algorithm is employed. This algorithm constructs a k-nearest neighbor graph for each business scenario in the high-dimensional demand space, calculates the linear weight coefficients of each point within its neighborhood, and then searches for a new coordinate representation in the low-dimensional space that optimally preserves these locally reconstructed relationships. This approach retains the topological proximity relationships between different business types. Cluster analysis is used to divide the dimensionality-reduced business requirements into four core demand clusters: security (covering security encryption levels, transmission success rates, etc.), control (corresponding to communication latency, power control reliability, etc.), perception (including positioning accuracy, recognition accuracy, etc.), and management (involving adjustable bandwidth range, connection density, etc.). This effectively reduces the input dimensionality and enhances the model's understanding of business semantics, facilitating the subsequent implementation of differentiated resource scheduling strategies based on categories.

[0036] In this embodiment, the technical and business-side dimensionality reduction operations are performed in parallel, acting on the row and column directions of the technology-demand correlation matrix, respectively, forming a complementary dual-path processing architecture. The technical side focuses on removing statistical redundancy between quantitative indicators to improve the numerical stability of the model; the business side focuses on faithfully restoring the business logic structure to enhance the interpretability of the results. Together, they complete the mapping transformation from the original high-dimensional heterogeneous data to the low-dimensional core factor space, providing a structurally clear and semantically explicit input foundation for the subsequent TOPSIS-VIKOR hybrid evaluation.

[0037] Example 2 As a preferred embodiment: In step S4, a hybrid multi-attribute decision model is constructed using the TOPSIS-VIKOR fusion evaluation mechanism to match business requirements with the parameters of the integrated sensing technology. The specific process is as follows: S4.1 Based on the reduced-dimensional technology-demand correlation matrix, define the positive ideal solution and negative ideal solution corresponding to each integrated sensing technology parameter scheme. The positive ideal solution is the set of optimal values ​​of each technology parameter scheme to adapt to the business demand index, and the negative ideal solution is the set of worst values ​​of each technology parameter scheme to adapt to the business demand index. S4.2 Calculate the relative closeness between each technical parameter scheme and the positive and negative ideal solutions using the TOPSIS model; S4.3 When there are conflicts among business requirement indicators, the VIKOR model should be used to evaluate the various technical parameter solutions, including: Calculate the group utility value of each technical parameter scheme. The group utility value is obtained by weighted summation of the differences between each technical parameter scheme and the positive ideal solution in each business demand indicator. Calculate the individual regret value for each technical solution. The individual regret value is the maximum difference between each technical parameter solution and the ideal solution among all business requirement indicators. Based on the group utility value, individual regret value, minimum group utility value, and maximum individual regret value, calculate the compromise evaluation value of each technical parameter scheme; S4.4 Sort the technical parameter schemes according to the compromise evaluation value, and select the technical parameter scheme with the smallest compromise evaluation value as the final matching scheme; when there are multiple technical parameter schemes with the smallest compromise evaluation value, select the technical parameter scheme with the largest relative closeness as the final matching scheme.

[0038] Specifically, based on the dimensionality-reduced technology-demand correlation matrix, positive and negative ideal solutions are constructed, where: The positive ideal solution represents a vector of optimal performance values ​​among all available technical parameter schemes for each business requirement indicator, such as the lowest communication latency and the highest sensing accuracy. Negative ideal solutions correspond to the set of worst-case performance values ​​for each indicator. These two ideal solutions together constitute the reference benchmark in the evaluation space, providing geometric anchors for subsequent distance calculations.

[0039] The Euclidean distance between each technical parameter scheme and the positive and negative ideal solutions is calculated using the TOPSIS model, and then normalized to a relative proximity. The relative proximity reflects how close a technical scheme is to the "optimal" state in terms of overall performance. The value ranges from 0 to 1, with the closer to 1 indicating better overall performance.

[0040] When a conflict is detected between business requirement metrics—for example, a certain type of control service requires extremely low latency (≤10ms), while a sensing service requires high bandwidth to ensure recognition accuracy (≥98%)—the two compete for resource allocation, triggering the VIKOR evaluation mechanism. The VIKOR algorithm model incorporates a dual consideration mechanism of "group utility" and "individual regret." The group utility value is obtained by weighting and summing the standardized differences across various indicators, reflecting the average satisfaction of the technical solution with overall demand. The individual regret value is the largest deviation from the positive ideal solution among all indicators, which characterizes the risk level brought about by the worst single performance.

[0041] The compromise evaluation value is a linear combination of the group utility value and the individual regret value. All technical parameter schemes are ranked according to the compromise evaluation value, and the scheme with the smallest compromise evaluation value is selected as the recommendation. If multiple schemes have the same minimum compromise evaluation value, the relative similarity provided by TOPSIS is used as a secondary criterion, prioritizing the scheme with better overall performance.

[0042] This embodiment adopts a collaborative evaluation architecture of TOPSIS and VIKOR. When faced with typical contradictory requirements in smart substations, such as the coexistence of low-latency control and high-precision sensing, it can generate a compromise solution that takes into account both fairness and efficiency. This avoids the biased selection problem that may occur in traditional single models, improves the rationality and engineering applicability of the technology-demand matching results, and enhances the decision robustness of the entire integrated sensing resource allocation system.

[0043] As a preferred embodiment, in step S5, the resource reallocation mechanism specifically includes: S5.1. The resource monitoring platform deployed in the substation monitors the consumption of communication resources in real time. The evaluation indicators of communication resource consumption include communication channel occupancy rate, computing resource utilization rate and perceived service load indicators. S5.2 When the evaluation indicators exceed the corresponding preset thresholds, a resource reallocation process is triggered to dynamically adjust beamforming parameters and edge computing resource scheduling, including: Adjust beamforming parameters: Based on channel occupancy and signal interference intensity, increase the signal gain of beamforming; Adjust edge computing resource scheduling: Based on computing resource utilization and perceived service load indicators, allocate additional computing power to high-priority services, while temporarily downgrading low-priority services. S5.3 Based on the adjusted beamforming gain and edge computing power parameters, reconstruct the technical feature vector and business requirement vector, and re-execute the matching process; S5.4. Verify the matching results using a non-dominated sorting genetic algorithm, and construct a bi-objective optimization model with matching degree as the maximization objective and resource consumption as the minimization objective. S5.5. Through non-dominated hierarchical processing, reference point guidance, and constraint handling using a non-dominated sorting genetic algorithm, a Pareto optimal solution set is generated. S5.6 If the result of the rematch is at the Pareto optimal frontier, it is determined as the final matching result; otherwise, return to step S5.2 to readjust the parameters until the Pareto optimal requirement is met.

[0044] Specifically, a resource monitoring platform is deployed within the substation to continuously collect and analyze key communication resource consumption indicators.

[0045] The resource monitoring platform is integrated into edge computing nodes or base station control units, possessing multi-dimensional data collection capabilities. It can acquire core evaluation indicators such as communication channel occupancy, computing resource utilization, and perceived service load in real time. These indicators collectively constitute a resource health assessment system, providing input for subsequent decision-making.

[0046] When any evaluation metric exceeds its preset threshold, the system automatically triggers a resource reallocation process: On the communication side, signal quality and coverage efficiency are improved by adjusting beamforming parameters. Specifically, the signal gain of beamforming is dynamically increased based on joint feedback information of current channel occupancy and signal interference intensity.

[0047] On the computing side, a differentiated edge computing resource scheduling strategy is implemented, which can classify and manage various tasks according to business priority tags, prioritizing the supply of computing power to high-priority businesses such as security control and fault alarm. When computing resource shortages are detected, additional computing power resources are dynamically allocated to these businesses, while temporary downsizing measures are implemented for low-priority businesses such as video inspection and environmental monitoring.

[0048] After completing the above parameter adjustments, the system enters the reconstruction and rematching stage. Based on the updated beamforming gain and edge computing power configuration, the technical feature vector is reconstructed, covering the latest quantitative values ​​of communication performance, sensing accuracy, fusion capability and reliability. The weight coefficients in the business requirement vector are fine-tuned according to the current business operation status, and the aforementioned matching process (S3-S4) is called to re-execute the technology-requirement adaptation analysis to generate new candidate matching schemes.

[0049] To verify the global optimality of the new matching result, a non-dominated sorting genetic algorithm is introduced for multi-objective optimization evaluation. By constructing a dual-objective optimization model with the objectives of "maximizing matching degree" and "minimizing resource consumption," a set of non-dominated solutions, i.e., the Pareto optimal solution set, can be searched and retained in the high-dimensional solution space. The system determines whether the rematched solution is located at the current Pareto optimal front. If so, the solution is confirmed as a stable and feasible final matching result and is distributed to each execution unit for implementation; otherwise, it returns to the resource adjustment stage (S5.2) and continues iterative optimization until it converges to the Pareto front.

[0050] This invention achieves dynamic adjustment and continuous optimization of resource usage in a 5G-Advanced integrated sensing environment for power systems. By monitoring the status of communication and computing resources in real time, it promptly identifies resource bottlenecks and triggers an adaptive adjustment mechanism. Combined with beamforming gain optimization and dynamic edge computing power scheduling, it improves the overall resource utilization efficiency of the system. By reconstructing feature vectors and repeatedly executing the matching process, it ensures continuous alignment between technical parameters and business requirements. By using the NSGA-III algorithm to generate a Pareto optimal solution set and using whether it falls into the optimal frontier as the termination criterion, it ensures that the final matching result achieves the best balance between matching degree and resource consumption, enhancing the system's resilience and robustness in the face of complex operating conditions. It is suitable for practical application scenarios of long-term stable operation of smart substations.

[0051] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0052] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0053] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0054] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0055] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0056] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for matching the demand of power 5G-Advanced sensing integration and smart substation business, characterized in that, The method comprises: S1, collecting the business demand data of the smart substation, quantifying the business demand indexes based on the business demand data through a multi-attribute utility theory model, determining the weight of each business demand index through an analytic hierarchy process combined with expert scoring, and generating a business demand vector with a weight coefficient; S2, collecting 5G-Advanced sensing integrated technology parameters, the technology parameters including quantitative indexes of communication performance, sensing accuracy, fusion capability and reliability, and generating a technology feature vector; S3, based on the business demand vector and the technology feature vector, constructing a technology-demand correlation matrix through a preset adaptability principle, and performing double-path dimension reduction on the technology-demand correlation matrix; S4, based on the dimension-reduced technology-demand correlation matrix, constructing a hybrid multi-attribute decision model through a TOPSIS-VIKOR fusion evaluation mechanism to match the sensing integrated technology parameters and the business demand; S5, inputting the matching result into a resource-efficiency joint optimizer, monitoring the communication resource consumption in real time through a resource monitoring platform deployed in the substation, triggering a resource reallocation mechanism when the communication resource consumption exceeds a preset threshold, and repeating the matching process of steps S3-S4 until a matching result satisfying the Pareto optimality of "matching degree-resource consumption" is obtained.

2. The method of claim 1, wherein the method is a method of matching the demand of power 5G-Advanced sensing-integrated and smart substation services. In step S1, the weight of each business demand index is determined through an analytic hierarchy process combined with expert scoring, and the specific method is: S1.1, a hierarchical structure model of the business demand of the smart substation is constructed, and the business demand of the smart substation is divided into a target layer, a criterion layer and an index layer, wherein: the target layer: an optimal business demand vector; the criterion layer: four first-level criteria of system security, power control reliability, device sensing accuracy and business load flexibility are set; the index layer: includes specific demand indexes corresponding to each first-level criterion, and the demand indexes include security encryption level, communication delay, transmission success rate, positioning accuracy, identification accuracy, connection density and bandwidth adjustable range; S1.2, based on the preconfigured expert knowledge base, the 1-9 scale method is used to compare the importance of each index element under the same criterion layer, and a judgment matrix is established; S1.3, the maximum eigenvalue of the judgment matrix and the corresponding eigenvector are calculated, and after normalization processing of the eigenvector, the preliminary weight coefficients of each index element are obtained; S1.4, the consistency index CI and the random consistency ratio CR are calculated, when CR≤0.1, it is determined that the weight distribution is reasonable, and the final weight coefficient is output; when CR>0.1, the judgment matrix is reconstructed, and steps S1.3-S1.4 are repeated until the consistency requirement is met.

3. The method of claim 1, wherein the method is a method of matching the demand of power 5G-Advanced integrated sensing and smart substation services. In step S3, the technology-demand correlation matrix is calculated based on the preset adaptability principle, the correlation degree of each demand index in the business demand vector and each technology parameter in the technology feature vector, and the value of the correlation degree is quantified according to the satisfaction degree of the technology parameter to the demand index.

4. The method of claim 3, wherein the power 5G-Advanced converged sensing and smart substation service demand matching method is characterized by: The preset adaptability principle includes the business safety priority principle, the sensing-communication coupling principle, the multi-dimensional dynamic expandability principle and the resource constraint compatibility principle.

5. The method of claim 1, wherein the power 5G-Advanced converged sensing and smart substation service demand matching method is characterized by: In step S3, double-path dimension reduction is performed on the technology-demand correlation matrix, including: Technology-side dimension reduction: for the technology feature vectors in the technology-demand correlation matrix, the variance contribution rate of each technology parameter in the technology feature vector is calculated using a principal component analysis algorithm, and the core technology factors are extracted by retaining the principal components with a cumulative contribution rate of ≥95%; Business-side dimension reduction: for the demand feature vectors in the technology-demand correlation matrix, the local linear embedding algorithm is used to maintain the topological correlation between typical business scenarios and extract the intrinsic dimension of business demand, and each demand index of the demand feature vector is merged into a core demand cluster including security, control, perception, and management.

6. The method of claim 1, wherein the method is a method of matching the demand of power 5G-Advanced sensing-integrated and smart substation services. In step S4, a hybrid multi-attribute decision model is constructed through a TOPSIS-VIKOR fusion evaluation mechanism to match the business demand and the integrated technology parameters, and the specific process is as follows: S4.1, based on the dimension-reduced technology-demand correlation matrix, define the positive ideal solution and the negative ideal solution corresponding to each integrated technology parameter scheme, the positive ideal solution is the optimal value set of each technology parameter scheme adapting to the business demand index, and the negative ideal solution is the worst value set of each technology parameter scheme adapting to the business demand index; S4.2, calculate the relative closeness between each technology parameter scheme and the positive ideal solution and the negative ideal solution through the TOPSIS model; S4.3, when there is a conflict between the business demand indexes, evaluate each technology parameter scheme using the VIKOR model, including: Calculate the group utility value of each technology parameter scheme, which is obtained by weighting and summing the differences between each technology parameter scheme and the positive ideal solution on each business demand index; Calculate the individual regret value of each technology scheme, which is the maximum difference between each technology parameter scheme and the positive ideal solution in all business demand indexes; Based on the group utility value, individual regret value, minimum value of the group utility value, and maximum value of the individual regret value, calculate the compromise evaluation value of each technology parameter scheme; S4.4, sort each technology parameter scheme according to the compromise evaluation value, and select the technology parameter scheme with the smallest compromise evaluation value as the final matching scheme; when there are multiple technology parameter schemes with the smallest compromise evaluation value, select the technology parameter scheme with the largest relative closeness as the final matching scheme.

7. The method of claim 1, wherein the method is a method of matching the demand of power 5G-Advanced sensing-integrated and smart substation services. In step S5, the resource reallocation mechanism specifically includes: S5.1, real-time monitoring of communication resource consumption through the resource monitoring platform deployed in the substation, the evaluation indexes of communication resource consumption include communication channel occupancy rate, computing resource utilization rate, and perception business load index; S5.2, when the evaluation indexes exceed the corresponding preset threshold, trigger the resource reallocation process, dynamically adjust the beamforming parameters and edge computing resource scheduling, including: Adjusting the beamforming parameters: based on the channel occupancy rate and signal interference intensity, increase the signal gain of beamforming; Adjusting the edge computing resource scheduling: based on the computing resource utilization rate and the perception business load index, allocate additional computing power to high-priority businesses, and temporarily reduce the allocation to low-priority businesses; S5.3, reconstruct the technical feature vector and service demand vector based on the adjusted beamforming gain and edge computing power parameters, and re-execute the matching process; S5.4, verify the matching result by the non-dominated sorting genetic algorithm, and construct a double-objective optimization model with the matching degree as the maximum target and the resource consumption as the minimum target; S5.5, generate a Pareto optimal solution set through the non-dominated sorting, reference point guidance and constraint processing of the non-dominated sorting genetic algorithm; S5.6, when the result of re-matching is on the Pareto optimal front, it is determined as the final matching result; otherwise, return to step S5.2 to adjust the parameters until the Pareto optimal requirement is met.

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