Power grid heterogeneous wireless network matching game access method and system based on approximate ideal solution sorting

Abstract

The invention belongs to the technical field of power grids, and discloses a power grid heterogeneous wireless network matching game access method based on ideal solution ordering approximation, in a heterogeneous network environment, the invention provides a matching game model, realizes optimal resource allocation between equipment and a network, comprehensively considers equipment priority, network performance and energy consumption constraints, and realizes optimal resource allocation between the equipment and the network. And meanwhile, real-time environment change is adapted through a dynamic adjustment strategy. Aiming at the requirements of communication reliability and time delay, a priority matching mechanism of high-priority equipment is designed, and a standby network is quickly switched through a fault-tolerant mechanism under the condition of link interruption, so that the communication stability is guaranteed. Through a distributed lightweight algorithm, the calculation complexity is reduced, the method is suitable for remote area equipment with limited resources, the reliability, efficiency and adaptability of a power grid communication system are integrally improved, and an innovative solution is provided for stable operation of an intelligent power grid.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grids, and in particular relates to a method and system for accessing heterogeneous wireless networks of power grids based on matching game of approximate ideal solution sorting. Background Art

[0002] With the continuous development of wireless communication technology, the emergence and integration of various wireless communication technologies have led to the formation of heterogeneous networks. Faced with the unique characteristics of these wireless technologies, improving network resource utilization while ensuring user network access quality of service has become a key concern. Among the current mainstream network selection technologies, handover algorithms based on received signal strength (RSSI) and intelligent selection algorithms based on Q-learning are widely used. For example, the RSSI algorithm triggers handover by monitoring signal strength thresholds in real time. However, it only considers a single physical layer metric. In complex scenarios such as power grid inspections, it can easily ignore factors such as network load and service demand, leading to frequent erroneous handovers. While the Q-learning algorithm can optimize long-term benefits through reinforcement learning, its training phase has slow convergence and high computational overhead, making it difficult to meet the stringent real-time requirements of inspection equipment. Users in areas covered by multiple networks expect to maintain the best connection anytime, anywhere, achieving seamless roaming to achieve higher system performance and user satisfaction. However, this process is not static. As user service needs change or their location moves, the performance of the network they are connected to may fluctuate. In such cases, it is necessary to reassess whether the current access network is still the best choice. Once a network with better performance becomes available, users will need to switch networks. Existing technologies, such as multi-attribute decision-making methods based on fuzzy logic, attempt to improve decision-making quality by comprehensively evaluating metrics such as bandwidth and latency. However, their static weight allocation mechanism struggles to adapt to the dynamically changing service priorities during power grid inspections, resulting in degraded service quality in emergencies. Furthermore, network selection must comprehensively consider the preferences of both users and the network. From the user's perspective, they want to select the optimal network that meets their service needs. From the network's perspective, network resource allocation must both meet user needs as much as possible while also properly controlling network load to avoid performance degradation or excessive maintenance pressure caused by excessive user access. This two-way selection problem can be addressed through the application of game theory. By establishing a network access selection game model, we can systematically analyze the preferences of both users and networks, thereby achieving optimal allocation and rational planning of network resources. In power grid inspection scenarios, complex geographical environments place higher demands on network coverage, often requiring the coordinated operation of multiple networks to achieve full regional coverage. Furthermore, user preferences for network performance and the user capacity constraints of each network further exacerbate the complexity of network switching selection. Dynamic user demands and the collaborative operation of multiple networks pose significant challenges to network selection. However, current research has yet to integrate game-theoretic network selection methods with power grid inspection scenarios. This is primarily due to the following reasons: 1. The geographical environment of power grid inspection is diverse and complex, and network requirements vary significantly across regions, making a unified network selection strategy difficult to apply.2. Power grid inspection tasks are usually highly dynamic, and network performance needs to be evaluated and switched in real time according to the movement of inspection equipment and environmental changes, while existing game models are mostly focused on static or semi-dynamic scenarios. 3. Power grid inspection involves a variety of heterogeneous networks (such as 5G, WiFi, satellite communications, etc.). How to effectively integrate these network resources and introduce game theory models for optimization is a technical challenge. 4. Current research on heterogeneous network selection mainly focuses on general communication scenarios, while there is less research on the specific needs of power grid inspection, and there is a lack of mature theoretical and practical foundations. Based on this, a solution for heterogeneous network selection based on TOPSIS matching game in power grid inspection scenarios is proposed. While ensuring user needs, the load capacity of the network end is considered to achieve the optimal overall performance of the system.

[0003] Through the above analysis, the problems and defects of the existing technology are as follows:

[0004] (1) Existing technologies only consider local unilateral selection. In fact, the capacity of the network also affects the overall performance. Therefore, it is very important to consider both the user's preference for the network and the network's preference for the user. It is difficult to achieve the optimal overall performance by only considering local unilateral selection.

[0005] (2) Existing network selection techniques do not consider the impact of multiple network attributes. They usually focus on a single indicator and cannot comprehensively measure network performance, resulting in the selected network not fully meeting the requirements of complex inspection tasks. Considering only the impact of a single network attribute is quite limited. Using TOPSIS can provide a more comprehensive consideration of multiple attributes.

[0006] (3) Existing network selection methods are difficult to adapt to complex power grid inspection scenarios, have high computational complexity, are difficult to quickly obtain the optimal selection in a large-scale heterogeneous network environment, and are costly, and cannot solve the proposed problem of efficient, fast, and low-cost network selection. Summary of the Invention

[0007] In view of the problems existing in the prior art, the present invention provides a matching game access method for heterogeneous wireless networks in power grids based on sorting of approximate ideal solutions.

[0008] The present invention is implemented as follows: a method for accessing heterogeneous wireless networks in a power grid based on a matching game approach to an ideal solution sorting method includes:

[0009] Step 1: Rank the user terminals' preferences for heterogeneous networks, perform quantitative analysis and integrated scoring of multiple performance indicators of heterogeneous networks using the TOPSIS method, and calculate the comprehensive performance score of each network.

[0010] Step 2: The network side ranks the user's preferences. The network side models the user's needs. For each network, it analyzes its resource constraints and service quality requirements and determines the preference ranking for user devices.

[0011] Step 3: Combining the network's service priorities for users and the user's rating of network performance, a multi-objective utility function is designed to ensure that the match between user needs and network resources adheres to the principle of bilateral optimization. Using a matching game approach, the preferences of both parties are adjusted through multiple rounds of iterations, gradually optimizing the matching solution and ultimately achieving a stable match between the device and the network.

[0012] Dynamically monitor performance changes in heterogeneous networks and changes in user device requirements, and promptly update utility functions. Based on real-time performance monitoring results, adjust matching strategies to ensure devices are always connected to the optimal network, achieving dynamic adaptation.

[0013] Furthermore, the TOPSIS method constructs an evaluation matrix, standardizes data, determines indicator weights, calculates ideal solutions and negative ideal solutions, and calculates the relative proximity of each network in combination with equipment requirements to derive a comprehensive performance score for each network and form a priority ranking for equipment selection.

[0014] Furthermore, the resource limitations include bandwidth and load capacity, and the quality of service requirements include maximizing the number of users or balancing the network load.

[0015] Further, the process of step 1 is as follows:

[0016] Define users and the network as bilateral participants in the game, and users belong to the set User={u1,u2,…,u k}, each mobile communication network belongs to the set Net={n1,n2,…,n m}; collect the performance parameters of heterogeneous networks; the standardized formula is:

[0017]

[0018] where x ij Represents the value of the i-th network on the j-th index, r ij is the standardized value; then assign weight w to each performance indicator according to actual needs ij ;

[0019] Compute ideal and negative ideal solutions:

[0020] Ideal solution: the maximum value set of all performance indicators;

[0021] A + ={max(r ij )|j∈J +},{min(r ij)|j∈J -}

[0022] Negative ideal solution: the minimum value set of all performance indicators;

[0023] A - ={min(r ij )|j∈J +},{max(r ij )|j∈J -}

[0024] Among them J + is a benefit-type indicator, J - It is a cost indicator.

[0025] Compute the Euclidean distance of each network from the ideal solution and the negative ideal solution:

[0026]

[0027] Calculate relative proximity:

[0028]

[0029] Using C i The values ​​are used to sort all networks and form the user's preference ranking for the networks.

[0030] Further, the process of step 2 is as follows:

[0031] Model the resources of each network and define its maximum number of users or resource allocation capacity. Each network ranks user devices according to their service needs. Use the utility function to evaluate user priorities:

[0032] U ij =α·Q i +β·W j

[0033] Among them U ij represents the utility value of network j to user i, Q i represents the user's demand weight, W j represents the weighted score of network performance, and α and β represent weight coefficients.

[0034] Further, the process of step 3 is as follows:

[0035] (1) Matching starts based on the utility functions of both parties. First, a preference list is constructed: the user terminal constructs a priority list p(i) based on the benefit value of the network utility function, and the network constructs a priority list p(m) based on the benefit value of the user terminal utility function;

[0036] (2) The user terminal sends a connection request to the first network access point in p(i);

[0037] (3) The network access point sorts the requesting terminals according to p(m) and enters the top-ranked users into the list according to the quota.

[0038] (4) Each access point reserves a number of terminal users not greater than the quota according to the preference list, and the associated terminals will be deleted from the priority list;

[0039] (5) The priority list p(i) is updated. The rejected user terminal performs the next round of matching. The terminal user deletes the access point that has rejected it from the preference list and sends a request to the first access point in the preference list again.

[0040] (6) The access point rejects or accepts the terminal user again according to the remaining quota and preference list; if the quota is full, only the terminal with a priority greater than the lowest priority in the list will be accepted, and the lowest priority terminal will be rejected.

[0041] (7) Repeat the above iterative process for K rounds until all users are matched or the unmatched end users are rejected by all nodes, and the output result is a stable match;

[0042] Another object of the present invention is to provide a power grid heterogeneous wireless network matching game access system based on approximate ideal solution sorting, comprising:

[0043] The ranking module is used to rank the user terminal's preferences for heterogeneous networks, form a quantitative analysis and integrated scoring of multiple performance indicators of heterogeneous networks using the TOPSIS method, and calculate the comprehensive performance score of each network;

[0044] An analysis module is used to rank the network's preferences for users. The network models user requirements for each network, analyzes its resource constraints and service quality requirements, and determines the preference ranking for user devices.

[0045] The matching module is used to design a multi-objective utility function by combining the network's service priority for users and the user's performance score for the network. This ensures that the match between user needs and network resources complies with the principle of bilateral optimization. The matching game method is used to adjust the preferences of both parties through multiple rounds of iterations, gradually optimizing the matching solution and ultimately achieving a stable matching state between the device and the network.

[0046] Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the power grid heterogeneous wireless network matching game access method based on approximate ideal solution sorting.

[0047] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the power grid heterogeneous wireless network matching game access method based on approximate ideal solution sorting.

[0048] Another object of the present invention is to provide an information data processing terminal, which is used to implement the power grid heterogeneous wireless network matching game access system based on approximate ideal solution sorting.

[0049] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0050] First, the present invention aims to address multiple technical challenges facing communications in remote power grid scenarios, including efficient selection of heterogeneous networks, optimization under resource-constrained conditions, communication reliability and latency assurance, adaptability in dynamic environments, algorithm complexity and implementation efficiency, and the design of communication redundancy and fault-tolerance mechanisms. Traditional solutions, such as RSSI-based threshold switching mechanisms and static weighted multi-attribute decision-making methods, suffer from limitations such as a single metric dimension and difficulty adapting to dynamic service demands. Furthermore, existing technologies typically focus only on local, unilateral selection, ignoring the impact of network capacity on overall performance. In reality, user preferences for network requirements and the network's access policy for users should be optimized in a coordinated manner; considering only one aspect of selection will not achieve optimal overall performance. In complex power grid inspection scenarios, existing network selection methods suffer from high computational complexity and difficulty in rapidly deriving optimal solutions in large-scale, heterogeneous network environments. Furthermore, high deployment costs make them inadequate for efficient, fast, and low-cost network selection. Therefore, an intelligent decision-making mechanism that comprehensively considers user preferences and network resource status is urgently needed to improve the adaptability and efficiency of network selection. In a heterogeneous network environment, this invention proposes a matching game model to achieve optimal resource allocation between devices and networks, comprehensively considering device priority, network performance, and energy consumption constraints, while adapting to real-time environmental changes through dynamic adjustment strategies. In response to communication reliability and latency requirements, this invention designs a priority matching mechanism for high-priority devices, and uses a fault-tolerant mechanism to quickly switch to a backup network in the event of a link interruption to ensure communication stability. Through a distributed lightweight algorithm, this invention reduces computational complexity, making it suitable for devices in remote areas with limited resources, improving the reliability, efficiency, and adaptability of the power grid communication system as a whole, and providing an innovative solution for the stable operation of smart grids.

[0051] The present invention is implemented as follows: a fusion network selection method based on the TOPSIS method and matching game, which aims to solve the key problems of high-quality matching and resource optimization between equipment and heterogeneous networks in remote power grid communications. For satellite communications, WIFI networks and 4G5G public networks commonly found in remote areas, the present invention first performs parameterized modeling of their performance characteristics (such as bandwidth, latency, reliability, coverage and energy consumption), and constructs a matching game model based on the diverse needs of power grid equipment (including real-time performance, power consumption limit and communication reliability). By designing a multi-objective utility function, the matching problem between power grid equipment and heterogeneous networks is abstracted into a bilateral game, comprehensively considering network performance and equipment priority to achieve optimal resource allocation and dynamic adaptation. The matching process ensures that the selection between equipment and network reaches a stable matching state through iterative optimization and dynamic adjustment of the utility function. On this basis, the present invention effectively solves the problems of scarce communication resources in remote areas, significant differences in heterogeneous network performance and diversified equipment requirements, significantly improves the efficiency and reliability of network selection, ensures the stable operation of smart grid communication systems in remote areas, and provides an innovative solution for power grid communications under resource-limited conditions.

[0052] The idea of ​​using TOPSIS to rank the preference of multi-attribute indicators in heterogeneous networks is presented in Part 4. The idea of ​​using bilateral selection based on matching game between the user end and the network receiving end in heterogeneous networks is presented in Part 4.

[0053] The TOPSIS-based matching game method introduces the idea of ​​game theory on this basis, which can dynamically adjust the network selection strategy to take into account the needs of users and both ends of the network. Combining the comprehensive evaluation ability of multi-attribute decision-making and the dynamic optimization advantages of game theory, it can not only realize comprehensive analysis of multiple indicators, but also effectively allocate network resources. In the existing power grid heterogeneous network communication solutions, most systems use a single network selection strategy to cope with different communication needs: such as a switching algorithm based on signal strength to ensure basic connection; a static weight allocation method based on QoS to handle high-priority services; an independent fault-tolerant mechanism to deal with network interruptions, etc. If you want to achieve comprehensive communication guarantee, you must deploy multiple independent decision modules, which not only increases the complexity of the system, but also leads to a sharp increase in computing resource consumption, significantly reducing the real-time response capability of power grid inspection equipment. Compared with the existing technology, the present invention innovatively adopts a method that combines TOPSIS and matching game to integrate functions such as multi-dimensional network evaluation, dynamic resource allocation and fault-tolerant switching into a unified decision-making framework. By building a bilateral device-network utility function, we achieve intelligent matching of communication needs with network characteristics. This involves precisely quantifying overall network performance using a dynamic TOPSIS algorithm, while also utilizing a lightweight game engine to optimize resource allocation in real time. This integrated intelligent decision-making system not only significantly improves system efficiency but also overcomes the technical bottleneck of collaborative optimization of heterogeneous networks in complex environments in remote areas.

[0054] The core innovation of this invention lies in the construction of a three-in-one technical system of "dynamic evaluation, two-way game, and flexible adaptation," which for the first time enables precise network selection and resource optimization in power grid inspection scenarios. By deeply integrating TOPSIS with game theory, the technical barriers of traditional methods in terms of dynamism, complexity, and real-time performance are overcome, providing a verifiable and reusable innovative solution for smart grid construction. Currently, no research has combined heterogeneous network selection based on game theory with power grid inspection scenarios. The TOPSIS-based matching game has higher computational efficiency and is suitable for scenarios such as power grid inspection, which require high real-time performance.

[0055] In summary, this invention effectively addresses the problems of traditional power grid inspections, such as a single network selection strategy, poor dynamic adaptability, and inefficient resource allocation, through multi-dimensional parameter modeling, game theory algorithm design, and distributed architecture optimization. This intelligent selection method, which integrates multi-attribute decision-making with game theory, improves communication reliability and provides a breakthrough technical solution for the stable operation of smart grids in remote areas.

[0056] Second, current heterogeneous network selection solutions for power grid inspections include single-metric switching based on signal strength or other attributes, static game theory resource allocation, and centralized network load control. However, these approaches still suffer from issues such as a disconnect between user and network preferences, a lack of a comprehensive multi-attribute evaluation system, and delayed responses in dynamic scenarios. No proposal has yet applied the fusion of dynamic game theory and TOPSIS multi-attribute decision-making technology to the collaborative optimization of heterogeneous power grid inspection networks, nor has a solution for a two-way matching game-based joint optimization of user service quality requirements and network load capacity constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of a method for accessing heterogeneous wireless networks in a power grid based on matching game and sorting of near-ideal solutions provided by an embodiment of the present invention.

[0058] Figure 2 This is a structural block diagram of a power grid heterogeneous wireless network matching game access system based on approximate ideal solution sorting provided by an embodiment of the present invention.

[0059] Figure 3 This is a schematic diagram of a multi-user heterogeneous network selection scenario in a power grid inspection scenario provided by an embodiment of the present invention.

[0060] Figure 4 It is a flow chart of a design method for heterogeneous network selection based on TOPSIS matching game in a power grid inspection scenario provided by an embodiment of the present invention.

[0061] Figure 5 3 is a schematic diagram comparing the utility values ​​of the entire system for each user under the proposed selection algorithm and the random matching algorithm provided by an embodiment of the present invention.

[0062] Figure 6 This is a schematic diagram of the utility value ratio of a user for each network provided by an embodiment of the present invention, that is, the larger the TOPSIS_best value, the closer the utility function value is to TOPSIS_best. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0064] like Figure 1 As shown, an embodiment of the present invention provides a method for accessing heterogeneous wireless networks in a power grid based on matching game and approximating ideal solution sorting, comprising the following steps:

[0065] S101: rank the user terminals' preferences for heterogeneous networks, perform quantitative analysis and integrated scoring of multiple performance indicators of the heterogeneous networks using the TOPSIS method, and calculate a comprehensive performance score for each network;

[0066] Multiple performance indicators such as latency, bandwidth, energy consumption, and reliability;

[0067] Specifically, the TOPSIS method constructs an evaluation matrix, standardizes data, determines indicator weights, calculates ideal and negative ideal solutions, and calculates the relative proximity of each network based on equipment requirements. This method derives a comprehensive performance score for each network, forms a priority ranking for equipment selection, and ensures that network performance evaluation is scientific and comprehensive.

[0068] S102, ranking the user preferences of the network end, the network end models the user requirements for each network, analyzes its resource constraints and service quality requirements, and determines the preference ranking for the user equipment;

[0069] Among them, resource constraints such as bandwidth and load capacity; quality of service requirements such as maximizing the number of users or balancing the network load;

[0070] S103: Combining the network's service priorities for users and the user's rating of network performance, a multi-objective utility function is designed to ensure that the match between user needs and network resources complies with the principle of bilateral optimization. A matching game approach is then used to iterate through multiple rounds of adjustments to the preferences of both parties, gradually optimizing the matching solution and ultimately achieving a stable match between the device and the network.

[0071] Dynamically monitor performance changes in heterogeneous networks (such as channel quality or load conditions) and changes in user device requirements (such as real-time or reliability requirements), and promptly update the utility function; based on real-time performance monitoring results, adjust the matching strategy to ensure that the device is always connected to the optimal network and achieve dynamic adaptation.

[0072] The process of S101 provided in the embodiment of the present invention is as follows:

[0073] Define users and the network as bilateral participants in the game, and users belong to the set User={u1,u2,…,u k}, each mobile communication network belongs to the set Net={n1,n2,...,n m Collect performance parameters of heterogeneous networks (such as latency, bandwidth, reliability, coverage, and energy consumption) and express the performance indicators of each network in numerical form. Construct an evaluation matrix, where each row represents multiple performance indicators of a network and each column represents the value corresponding to a certain indicator. Standardize the evaluation matrix to eliminate the impact of different indicators due to different dimensions. The standardization formula is:

[0074]

[0075] where x ij Represents the value of the i-th network on the j-th index, r ij is the standardized value; then each performance indicator is given a weight w according to actual requirements (such as real-time requirements take precedence over low power requirements) ij ;

[0076] Compute ideal and negative ideal solutions:

[0077] Ideal solution: the maximum value set of all performance indicators;

[0078] A + ={max(r ij )|j∈J +},{min(r ij )|j∈J -}

[0079] Negative ideal solution: the minimum value set of all performance indicators;

[0080] A - ={min(r ij )|j∈J +},{max(r ij )|j∈J -}

[0081] Among them J + is a benefit-type indicator, J - It is a cost indicator.

[0082] Compute the Euclidean distance of each network from the ideal solution and the negative ideal solution:

[0083]

[0084] Calculate relative proximity:

[0085]

[0086] Using C i The values ​​are used to sort all networks and form the user's preference ranking for the networks.

[0087] The S102 process provided by the embodiment of the present invention is as follows:

[0088] Model each network's resources (such as bandwidth capacity and user load capacity) and define its maximum number of users or resource allocation capacity. Each network ranks user devices based on their service needs. Utility functions are used to evaluate user priorities:

[0089] U ij =α·Q i+β·W j

[0090] Among them U ij represents the utility value of network j to user i, Q i represents the user's demand weight, W j represents the weighted score of network performance, and α and β represent weight coefficients.

[0091] The process of S103 provided in the embodiment of the present invention is as follows:

[0092] (1) Matching starts based on the utility functions of both parties. First, a preference list is constructed: the user terminal constructs a priority list p(i) based on the benefit value of the network utility function, and the network constructs a priority list p(m) based on the benefit value of the user terminal utility function;

[0093] (2) The user terminal sends a connection request to the first network access point in p(i);

[0094] (3) The network access point sorts the requesting terminals according to p(m) and enters the top-ranked users into the list according to the quota.

[0095] (4) Each access point reserves a number of terminal users not greater than the quota according to the preference list, and the associated terminals will be deleted from the priority list;

[0096] (5) The priority list p(i) is updated. The rejected user terminal performs the next round of matching. The terminal user deletes the access point that has rejected it from the preference list and sends a request to the first access point in the preference list again.

[0097] (6) The access point rejects or accepts the terminal user again according to the remaining quota and preference list; if the quota is full, only the terminal with a priority greater than the lowest priority in the list will be accepted, and the lowest priority terminal will be rejected.

[0098] (7) Repeat the above iterative process for K rounds until all users are matched or the unmatched end users are rejected by all nodes, and the output result is a stable match;

[0099] like Figure 2 As shown, an embodiment of the present invention provides a power grid heterogeneous wireless network matching game access system based on approximate ideal solution sorting, including:

[0100] The ranking module is used to rank the user terminal's preferences for heterogeneous networks, form a quantitative analysis and integrated scoring of multiple performance indicators of heterogeneous networks using the TOPSIS method, and calculate the comprehensive performance score of each network;

[0101] An analysis module is used to rank the network's preferences for users. The network models user requirements for each network, analyzes its resource constraints and service quality requirements, and determines the preference ranking for user devices.

[0102] The matching module is used to design a multi-objective utility function by combining the network's service priority for users and the user's performance score for the network. This ensures that the match between user needs and network resources complies with the principle of bilateral optimization. The matching game method is used to adjust the preferences of both parties through multiple rounds of iterations, gradually optimizing the matching solution and ultimately achieving a stable matching state between the device and the network.

[0103] Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the power grid heterogeneous wireless network matching game access method based on approximate ideal solution sorting.

[0104] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the power grid heterogeneous wireless network matching game access method based on approximate ideal solution sorting.

[0105] Another object of the present invention is to provide an information data processing terminal, which is used to implement the power grid heterogeneous wireless network matching game access system based on approximate ideal solution sorting.

[0106] like Figure 3 , a schematic diagram of a multi-user heterogeneous network selection scenario in a power grid inspection scenario provided by an embodiment of the present invention.

[0107] like Figure 4 , a flow chart of a design method for heterogeneous network selection based on TOPSIS matching game in a power grid inspection scenario provided by an embodiment of the present invention.

[0108] like Figure 5 , a schematic diagram comparing the utility values ​​of the entire system for each user under the proposed selection algorithm and the random matching algorithm in an embodiment of the present invention.

[0109] like Figure 6 , a schematic diagram of the utility value ratio of a user for each network in an embodiment of the present invention, that is, the larger the TOPSIS_best value, the closer the utility function value is to TOPSIS_best.

[0110] 1. Specific application fields or related products of the present invention.

[0111] Example 1: Application of TOPSIS-based heterogeneous wireless network matching game access method in smart grid inspection

[0112] 1) Background and Requirements:

[0113] Smart grid inspection requires efficient and reliable heterogeneous network collaboration capabilities to achieve real-time dynamic adaptation of inspection equipment communication quality and network resources.

[0114] Traditional network selection solutions rely on a unilateral decision-making model that disconnects user and network preferences, resulting in low resource utilization and high service quality fluctuations. Existing technologies rely on static metric evaluation and one-way selection mechanisms, making them incapable of addressing the complex demands of dynamic inspection scenarios, where multiple networks experience performance fluctuations, user demands shift, and network load are intertwined. This leads to high network switching latency and limited overall system performance.

[0115] 2) Technical implementation:

[0116] A user-network two-way matching game model is constructed, and a multi-attribute comprehensive evaluation system based on improved TOPSIS is designed to achieve heterogeneous network optimization in dynamic scenarios.

[0117] By establishing a game theory framework to quantify the relationship between user service quality requirements (latency, bandwidth) and network load capacity constraints, a two-way matching algorithm is used to find the optimal solution set. An entropy weighting method is introduced to dynamically adjust the weights of network evaluation indicators, and a TOPSIS decision matrix is ​​used for joint evaluation. A lightweight distributed decision engine is developed.

[0118] 3) Application effect:

[0119] The communication reliability and resource utilization efficiency of smart grid inspection are improved. Through dynamic game decision-making and TOPSIS multi-attribute evaluation, the optimal solution for user-network two-way matching is achieved, thereby improving the overall performance of the system.

[0120] It reduces the cost of collaborative operation and maintenance of heterogeneous networks, relies on lightweight distributed decision-making to reduce network switching overhead and redundant resource usage, and reduces equipment communication energy consumption in complex scenarios such as mountainous areas and substations.

[0121] It enhances the continuity and security of power grid inspection services and reduces the interruption rate of inspection data transmission through real-time load sensing and multi-network dynamic switching mechanisms.

[0122] like Figure 1 As shown, an embodiment of the present invention provides a method for accessing heterogeneous wireless networks in a power grid based on matching game and approximating ideal solution sorting, comprising the following steps:

[0123] S101: rank the user terminals' preferences for heterogeneous networks, perform quantitative analysis and integrated scoring of multiple performance indicators of the heterogeneous networks using the TOPSIS method, and calculate a comprehensive performance score for each network;

[0124] Multiple performance indicators such as latency, bandwidth, energy consumption, and reliability;

[0125] Specifically, the TOPSIS method constructs an evaluation matrix, standardizes data, determines indicator weights, calculates ideal and negative ideal solutions, and calculates the relative proximity of each network based on equipment requirements. This method derives a comprehensive performance score for each network, forms a priority ranking for equipment selection, and ensures that network performance evaluation is scientific and comprehensive.

[0126] S102, ranking the user preferences of the network end, the network end models the user requirements for each network, analyzes its resource constraints and service quality requirements, and determines the preference ranking for the user equipment;

[0127] Among them, resource constraints such as bandwidth and load capacity; quality of service requirements such as maximizing the number of users or balancing the network load;

[0128] S103: Combining the network's service priorities for users and the user's rating of network performance, a multi-objective utility function is designed to ensure that the match between user needs and network resources complies with the principle of bilateral optimization. A matching game approach is then used to iterate through multiple rounds of adjustments to the preferences of both parties, gradually optimizing the matching solution and ultimately achieving a stable match between the device and the network.

[0129] Dynamically monitor performance changes in heterogeneous networks (such as channel quality or load conditions) and changes in user device requirements (such as real-time or reliability requirements), and promptly update the utility function; based on real-time performance monitoring results, adjust the matching strategy to ensure that the device is always connected to the optimal network and achieve dynamic adaptation.

[0130] The process of S101 provided in the embodiment of the present invention is as follows:

[0131] Define users and the network as bilateral participants in the game, and users belong to the set User={u1,u2,…,u k}, each mobile communication network belongs to the set Net={n1,n2,...,n m Collect performance parameters of heterogeneous networks (such as latency, bandwidth, reliability, coverage, and energy consumption) and express the performance indicators of each network in numerical form. Construct an evaluation matrix, where each row represents multiple performance indicators of a network and each column represents the value corresponding to a certain indicator. Standardize the evaluation matrix to eliminate the impact of different indicators due to different dimensions. The standardization formula is:

[0132]

[0133] where x ij Represents the value of the i-th network on the j-th index, r ij is the standardized value; then each performance indicator is given a weight w according to actual requirements (such as real-time requirements take precedence over low power requirements) ij ;

[0134] Compute ideal and negative ideal solutions:

[0135] Ideal solution: the maximum value set of all performance indicators;

[0136] A + ={max(r ij )|j∈J +},{min(r ij )|j∈J -}

[0137] Negative ideal solution: the minimum value set of all performance indicators;

[0138] A - ={min(r ij )|j∈J +},{max(r ij )|j∈J -}

[0139] Among them J + is a benefit-type indicator, J - It is a cost indicator.

[0140] Compute the Euclidean distance of each network from the ideal solution and the negative ideal solution:

[0141]

[0142] Calculate relative proximity:

[0143]

[0144] Using C i The values ​​are used to sort all networks and form the user's preference ranking for the networks.

[0145] The S102 process provided by the embodiment of the present invention is as follows:

[0146] Model each network's resources (such as bandwidth capacity and user load capacity) and define its maximum number of users or resource allocation capacity. Each network ranks user devices based on their service needs. Utility functions are used to evaluate user priorities:

[0147] U ij =α·Q i+β·W j

[0148] Among them U ij represents the utility value of network j to user i, Q i represents the user's demand weight, W j represents the weighted score of network performance, and α and β represent weight coefficients.

[0149] The process of S103 provided in the embodiment of the present invention is as follows:

[0150] (1) Matching starts based on the utility functions of both parties. First, a preference list is constructed: the user terminal constructs a priority list p(i) based on the benefit value of the network utility function, and the network constructs a priority list p(m) based on the benefit value of the user terminal utility function;

[0151] (2) The user terminal sends a connection request to the first network access point in p(i);

[0152] (3) The network access point sorts the requesting terminals according to p(m) and enters the top-ranked users into the list according to the quota.

[0153] (4) Each access point reserves a number of terminal users not greater than the quota according to the preference list, and the associated terminals will be deleted from the priority list;

[0154] (5) The priority list p(i) is updated. The rejected user terminal performs the next round of matching. The terminal user deletes the access point that has rejected it from the preference list and sends a request to the first access point in the preference list again.

[0155] (6) The access point rejects or accepts the terminal user again according to the remaining quota and preference list; if the quota is full, only the terminal with a priority greater than the lowest priority in the list will be accepted, and the lowest priority terminal will be rejected.

[0156] (7) Repeat the above iterative process for K rounds until all users are matched or the unmatched end users are rejected by all nodes, and the output result is a stable match;

[0157] 2. Relevant evidence of the technical effects obtained by the embodiments of the present invention.

[0158] The present invention relates to the field of power grid inspection communication technology, and in particular to a heterogeneous network selection method based on the fusion of the TOPSIS method and matching game and its application in the smart grid inspection system in remote areas. The present invention proposes a network selection optimization framework for complex geographical environments and dynamic task requirements. By establishing a two-way matching game model between equipment and heterogeneous networks, it solves the problem of dynamic resource allocation in multi-standard network collaboration scenarios such as satellite communications, WiFi networks and 4G / 5G public networks. This method is based on the multi-dimensional communication requirements (real-time, reliability) of power grid inspection equipment and the multi-attribute performance indicators (bandwidth, latency) of the network end, and combines game theory with multi-attribute decision-making theory to achieve optimal adaptation of communication resources and system stability assurance. The effectiveness of the scheme of the present invention has been verified through simulation experiments. Although it does not involve the implementation of specific hardware products, it has significant theoretical innovation and engineering application value in the field of smart grid communication optimization in remote areas.

[0159] In an embodiment of the present invention, the heterogeneous network selection process in the remote area power grid inspection scenario is simulated, and a hybrid networking environment of three types of heterogeneous networks including satellite communication, WiFi network and 5G public network is set up, and the dynamic task requirements of multiple user inspection devices are simulated. During the movement, the inspection equipment needs to dynamically switch networks based on real-time network performance (such as bandwidth, latency, reliability) and its own business requirements (such as real-time performance, power consumption limit and communication reliability). This solution takes the TOPSIS multi-attribute decision-making method as the starting point, quantifies the matching degree between network performance and equipment requirements, constructs a bilateral game model, and comprehensively considers network load capacity and equipment priority to achieve optimal resource allocation and dynamic adaptation. First, based on the TOPSIS method, the performance indicators of the heterogeneous network are normalized, and the comprehensive closeness of each network to the equipment requirements is calculated; then, the stable matching theory in game theory is introduced, and a multi-objective utility function is designed. The matching problem between equipment and network is abstracted into a bilateral selection game; finally, through iterative optimization and dynamic adjustment of the utility function, it is ensured that the selection between equipment and network reaches a balanced state and forms a stable matching result. Simulation results show that this solution significantly reduces network switching delay and computing overhead while ensuring the service quality of inspection tasks, providing an innovative solution for the stable operation of smart grids in remote areas.

[0160] The comparison diagram of the utility value of the system as a whole under the proposed selection algorithm and the random matching algorithm provided by the embodiment of the present invention is as follows: Figure 5As shown in the figure, the simulation results show the impact of the matching game algorithm and the random matching strategy on the overall system performance. As the number of users increases from 0 to 19, the utility value of the matching game algorithm exhibits a stable distribution (mean 0.621), significantly outperforming the random matching algorithm (mean 0.525). The high utility value of the matching game algorithm is mainly due to its two-way selection mechanism that comprehensively considers user preferences and network load status, achieving optimal resource allocation through dynamic adjustment strategies. However, due to the lack of systematic evaluation of user needs and network performance, the random matching algorithm has difficulty in achieving efficient resource matching, resulting in a low overall utility value. This shows that the network selection strategy based on game theory can effectively coordinate the supply and demand relationship between users and the network, improving resource allocation efficiency.

[0161] A schematic diagram of the utility value ratio of a user to each candidate network in a power grid inspection scenario provided by an embodiment of the present invention is shown in FIG. Figure 6 As shown in the figure, the larger the TOPSIS_best value, the closer the utility function value is to TOPSIS_best. The simulation results allow us to analyze the performance differences between different networks (including LTE 1, WiFi 1, WiFi 2, Satellite 1, and Satellite 2) in meeting user needs and their impact on the utility function. These results demonstrate that the proposed TOPSIS_best-based utility function optimization method can effectively balance user needs and network performance, providing theoretical support and practical verification for real-time communication assurance in power grid inspection scenarios.

[0162] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware portion can be implemented using dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will appreciate that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0163] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A matching game access method for heterogeneous wireless networks in power grids based on approximate ideal solution sorting, characterized by: The following steps are involved: Step 1: Rank the user terminals' preferences for heterogeneous networks, perform quantitative analysis and integrated scoring of multiple performance indicators of heterogeneous networks using the TOPSIS method, and calculate the comprehensive performance score of each network. Step 2: The network side ranks the user's preferences. The network side models the user's needs. For each network, it analyzes its resource constraints and service quality requirements and determines the preference ranking for user devices. Step 3: Combining the network's service priorities for users and the user's rating of network performance, a multi-objective utility function is designed to ensure that the match between user needs and network resources adheres to the principle of bilateral optimization. Using a matching game approach, the preferences of both parties are adjusted through multiple rounds of iterations, gradually optimizing the matching solution and ultimately achieving a stable match between the device and the network. Dynamically monitor performance changes in heterogeneous networks and changes in user device requirements, and promptly update utility functions. Based on real-time performance monitoring results, adjust matching strategies to ensure devices are always connected to the optimal network, achieving dynamic adaptation.

2. The method for accessing heterogeneous wireless networks in power grids based on matching game based on approximate ideal solution sorting according to claim 1, characterized in that: The TOPSIS method constructs an evaluation matrix, standardizes data, determines indicator weights, calculates ideal solutions and negative ideal solutions, and calculates the relative proximity of each network based on equipment requirements to derive a comprehensive performance score for each network and form a priority ranking for equipment selection.

3. The method for accessing heterogeneous wireless networks in power grids based on matching game based on near-ideal solution sorting as claimed in claim 1, characterized in that: The resource limitations are: bandwidth, load capacity; the quality of service requirements are: maximizing the number of users or balancing the network load.

4. The method for accessing heterogeneous wireless networks in a power grid based on matching game based on approximate ideal solution sorting according to claim 1, characterized in that: The process of step 1 is as follows: Define users and the network as bilateral participants in the game, and users belong to the set User={u1,u2,…,u k }, each mobile communication network belongs to the set Net={n1,n2,…,n l }; collect the performance parameters of heterogeneous networks; the standardized formula is: where x ij Represents the value of the i-th network on the j-th index, r ij is the standardized value; then assign weight w to each performance indicator according to actual needs ij ; Compute ideal and negative ideal solutions: Ideal solution: the maximum value set of all performance indicators; A + ={max(r ij )|j∈J + },{min(r ij )|j∈J - } Negative ideal solution: the minimum value set of all performance indicators; A - ={min(r ij )|j∈J + },{max(r ij )|j∈J - } Among them J + is a benefit-type indicator, J - It is a cost indicator. Compute the Euclidean distance of each network from the ideal solution and the negative ideal solution: Calculate relative proximity: Using C i The values ​​are used to sort all networks and form the user's preference ranking for the networks.

5. The method for accessing heterogeneous wireless networks in power grids based on matching game based on near-ideal solution sorting as claimed in claim 1, characterized in that: The process of step 2 is as follows: Model the resources of each network and define its maximum number of users or resource allocation capacity. Each network ranks user devices according to their service needs. Use the utility function to evaluate user priorities: U ij =α·Q i +β·W j Among them U ij represents the utility value of network j to user i, Q i represents the user's demand weight, W j represents the weighted score of network performance, and α and β represent weight coefficients.

6. The method for accessing heterogeneous wireless networks in power grids based on matching game based on near-ideal solution sorting as claimed in claim 1, characterized in that: The process of step 3 is as follows: (1) Matching starts based on the utility functions of both parties. First, a preference list is constructed: the user terminal constructs a priority list p(i) based on the benefit value of the network utility function, and the network constructs a priority list p(m) based on the benefit value of the user terminal utility function; (2) The user terminal sends a connection request to the first network access point in p(i); (3) The network access point sorts the requesting terminals according to p(m) and enters the top-ranked users into the list according to the quota. (4) Each access point reserves a number of terminal users not greater than the quota according to the preference list, and the associated terminals will be deleted from the priority list; (5) The priority list p(i) is updated. The rejected user terminal performs the next round of matching. The terminal user deletes the access point that has rejected it from the preference list and sends a request to the first access point in the preference list again. (6) The access point again rejects or accepts the terminal user according to the remaining quota and preference list; If the quota is full, only terminals with a priority greater than the lowest priority in the list will be accepted, and the lowest priority terminal will be rejected. (7) Repeat the above iterative process for K rounds until all users are matched or the unmatched end users are rejected by all nodes, and the output result is a stable match; 7. A system for accessing heterogeneous wireless networks in a power grid based on matching game of near-ideal solution sorting, which implements the method for accessing heterogeneous wireless networks in a power grid based on matching game of near-ideal solution sorting as described in any one of claims 1 to 6, characterized in that: The power grid heterogeneous wireless network matching game access system based on approximate ideal solution sorting includes: The ranking module is used to rank the user terminal's preferences for heterogeneous networks, form a quantitative analysis and integrated scoring of multiple performance indicators of heterogeneous networks using the TOPSIS method, and calculate the comprehensive performance score of each network; An analysis module is used to rank the network's preferences for users. The network models user requirements for each network, analyzes its resource constraints and service quality requirements, and determines the preference ranking for user devices. The matching module is used to design a multi-objective utility function by combining the network's service priority for users and the user's performance score for the network. This ensures that the match between user needs and network resources complies with the principle of bilateral optimization. The matching game method is used to adjust the preferences of both parties through multiple rounds of iterations, gradually optimizing the matching solution and ultimately achieving a stable matching state between the device and the network.

8. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the power grid heterogeneous wireless network matching game access method based on approximate ideal solution sorting as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the method for matching and accessing heterogeneous wireless networks in power grids based on sorting of approximate ideal solutions as described in any one of claims 1 to 6.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the power grid heterogeneous wireless network matching game access system based on approximate ideal solution sorting as described in claim 7.

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