Route optimization method and device for source network load storage power system

Abstract

The invention provides a routing optimization method and device for a source network load storage power system. The method comprises the following steps: acquiring candidate routing paths of a wireless sensor network; updating and iterating the cloud droplet position based on a cloud model optimization algorithm until the number of iterations meets a preset condition, and calculating the fitness value of the cloud droplet position based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position meeting the preset condition; and obtaining an optimal routing path based on the fitness value of the cloud droplet position. According to the method provided by the invention, the routing path is reflected through the cloud droplet position, the cloud droplet position is optimized through the cloud model optimization algorithm, the fitness value of the cloud droplet position is calculated based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position meeting the preset condition, and the routing path is determined based on the fitness value of the cloud droplet position. And the optimal cloud drop position is determined from the cloud drop positions to obtain the optimal routing path, so that efficient and high-quality dynamic routing path optimization is realized.

Description

Technical Field

[0001] The present invention relates to the field of information and communication technology, and in particular to a method and device for optimizing routing of a source-grid-load-storage power system. Background Art

[0002] With the rapid development of renewable energy and the continuous growth of electricity demand, traditional power systems are facing many challenges, such as imbalance in power supply and demand, energy waste, and reduced system reliability and stability. In order to solve these problems, the power system has begun to adopt the concept of source-grid-load-storage integration, that is, to achieve efficient operation of the power system by coordinating the power generation source (source), power grid (grid), load (load) and energy storage system (storage). The dynamic routing optimization method in the source-grid-load-storage power system aims to adjust the operating status of each link in the power system in real time through advanced algorithms and technologies, thereby optimizing the performance of the overall system.

[0003] In order to further cope with the complex challenges of modern power grids, it is urgent to solve the problem of how to improve the optimization accuracy and optimization rate of routing of source-grid-load-storage power systems, thereby improving routing quality. Summary of the invention

[0004] The present invention provides a route optimization method and device for a source-grid-load-storage power system, which are used to solve the defects in the prior art that urgently need to be solved, that is, how to improve the optimization accuracy and optimization rate of the route of the source-grid-load-storage power system, thereby improving the route quality.

[0005] The present invention provides a route optimization method for a source-grid-load-storage power system, comprising: Obtaining a candidate routing path of the wireless sensor network, and determining the number of cloud droplets matching the candidate routing path; Based on the cloud model optimization algorithm, the cloud droplet position of the cloud droplet is updated and iterated until the number of iterations meets the preset conditions, and the fitness value of the cloud droplet position is calculated based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position that meets the preset conditions; Based on the fitness values ​​of the cloud drop positions, an optimal cloud drop position is determined from the cloud drop positions to obtain an optimal routing path.

[0006] According to a routing optimization method for a source-grid-load-storage power system provided by the present invention, the fitness value of the cloud droplet position is calculated based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position that meets the preset conditions, including: Calculating based on the node monitoring efficiency and node weight of the wireless sensor node, obtaining the overall monitoring efficiency of the cloud droplet position; Based on the node energy consumption of the wireless sensor node and the overall energy consumption value corresponding to the cloud droplet position, obtaining the node utilization rate of the cloud droplet position; Based on the overall monitoring effectiveness and the node utilization rate, the fitness value of the cloud droplet position is calculated.

[0007] According to a routing optimization method for a source-grid-load-storage power system provided by the present invention, the node monitoring efficiency and node weight of the wireless sensor node are calculated to obtain the overall monitoring efficiency of the cloud droplet position, including: The node monitoring efficiency is calculated based on the basic monitoring capability of the wireless sensor node and the node energy consumption; the basic monitoring capability is determined based on at least one of the sensor sampling accuracy, sensor measurement range and sensor response time of the node; The overall monitoring efficiency of the cloud droplet position is obtained by performing calculations based on the node monitoring efficiency and the node weights of the wireless sensor nodes.

[0008] According to a routing optimization method for a source-grid-load-storage power system provided by the present invention, the cloud model-based optimization algorithm updates and iterates the cloud droplet positions of the cloud droplets until the number of iterations meets a preset condition, including: determining an initial cloud droplet position of the cloud droplet; Based on the cloud model optimization algorithm, the initial cloud droplet position of the cloud droplet is updated and iterated to obtain the current cloud droplet position of the cloud droplet, and based on the current fitness value of the cloud droplet position, the elite cloud droplet is determined; Based on the current cloud droplet position of the elite cloud droplet, the model parameters of the cloud model optimization algorithm are updated, so that the current cloud droplet position of the cloud droplet is updated and iterated for the next round based on the updated cloud model optimization algorithm until the number of iterations meets the preset conditions.

[0009] According to a routing optimization method for a source-grid-load-storage power system provided by the present invention, the elite cloud droplet is determined based on the current fitness value of the cloud droplet position, including: Based on the routing optimization objective function, the current fitness value of the cloud droplet position is calculated; the routing optimization objective function includes at least one of minimizing the total energy consumption of the path, minimizing the path length, and maximizing the network life; The current fitness values ​​are sorted in descending order, and cloud droplets corresponding to the preset current fitness values ​​are selected as the elite cloud droplets.

[0010] According to a routing optimization method for a source-grid-load-storage power system provided by the present invention, the updating of the model parameters of the cloud model optimization algorithm based on the current cloud droplet position of the elite cloud droplet includes: Based on the current cloud droplet position of the elite cloud droplet, an updated expected value of the cloud model optimization algorithm is calculated; Based on the current cloud droplet position of the elite cloud droplet and the updated expected value, an updated entropy value is calculated; Based on the current cloud droplet position of the elite cloud droplet and the updated entropy value, an updated super entropy value is calculated; Based on the updated expected value, the updated entropy value and the updated super entropy value, the model parameters of the cloud model optimization algorithm are updated.

[0011] The present invention also provides a route optimization device for a source-grid-load-storage power system, comprising: An acquisition unit, which acquires a candidate routing path of the wireless sensor network and determines the number of cloud droplets matching the candidate routing path; The optimization unit updates and iterates the cloud droplet position of the cloud droplet based on the cloud model optimization algorithm until the number of iterations meets the preset conditions, and calculates the fitness value of the cloud droplet position based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position that meets the preset conditions; The path determination unit determines an optimal cloud droplet position from the cloud droplet positions based on the fitness values ​​of the cloud droplet positions to obtain an optimal routing path.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a routing optimization method for a source-grid-load-storage power system as described above is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a route optimization method for a source-grid-load-storage power system as described in any one of the above.

[0014] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-mentioned methods for optimizing the routing of the source-grid-load-storage power system.

[0015] The routing optimization method and device of the source-grid-load-storage power system provided by the present invention reflect the routing path through the cloud droplet position, optimize the cloud droplet position through a cloud model optimization algorithm, and calculate the fitness value of the cloud droplet position based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position under preset conditions. Based on the fitness value of the cloud droplet position, the optimal cloud droplet position is determined from the cloud droplet positions to obtain the optimal routing path, thereby realizing efficient and high-quality dynamic routing path optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of a method for optimizing the routing of a source-grid-load-storage power system provided by the present invention; Figure 2 is a schematic diagram of a method for transmitting fault data based on a routing path provided by the present invention; Figure 3 It is a structural schematic diagram of a route optimization device for a source-grid-load-storage power system provided by the present invention; Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The dynamic routing optimization method in the source-grid-load-storage power system aims to adjust the operating status of each link in the power system in real time through advanced algorithms and technologies, thereby optimizing the performance of the overall system. This method not only needs to consider the dynamic changes of power generation sources, such as fluctuations in wind and solar energy, but also needs to deal with real-time fluctuations in power load and the charging and discharging status of the energy storage system. Through dynamic routing optimization, the utilization rate of renewable energy can be maximized, energy waste can be reduced, and operating costs can be reduced while ensuring the stable operation of the power system. Therefore, dynamic routing optimization of the source-grid-load-storage power system has become a key factor in improving the stability and reliability of the source-grid-load-storage wireless sensor network.

[0020] The dynamic routing of the source-grid-load-storage power system has significant characteristics such as real-time, intelligence, coordination, flexibility, robustness and efficiency optimization. Through real-time monitoring and data analysis, dynamic routing can quickly respond to the state changes of each link in the system, use technologies such as artificial intelligence and machine learning to autonomously learn and adapt to the operating laws of the power system, and make intelligent decisions. It coordinates the comprehensive dispatch of power sources, power grids, loads and energy storage systems to achieve overall optimization and ensure efficient collaborative work. In the face of external environmental fluctuations and emergencies, dynamic routing has strong robustness, can quickly adjust strategies, maintain stable operation of the system, and maximize the use of renewable energy by optimizing power transmission paths and load distribution, improving the economy and operation efficiency of the system. Due to the volatility of renewable energy such as wind and solar energy, as well as the real-time changes in power load, dynamic routing also needs to take into account the stability and security of the system. Therefore, the development and implementation of patented dynamic routing optimization technology is crucial to improving the flexibility, efficiency and reliability of the power system, and can promote the widespread application and popularization of sustainable energy.

[0021] Based on the above reasons, in order to further cope with the complex challenges of modern power networks, how to improve the optimization accuracy and optimization rate of the routing of the source-grid-load-storage power system, and thus improve the routing quality, is solved. The present invention provides a routing optimization method for the source-grid-load-storage power system to achieve efficient and high-quality routing path optimization. Figure 1 Schematic diagram of the flow of the routing optimization method of the source-grid-load-storage power system provided by the present invention, such as Figure 1 As shown, the method includes: Step 110, obtaining a candidate routing path of the wireless sensor network, and determining the number of cloud droplets matching the candidate routing path.

[0022] Here, the wireless sensor network refers to a collection of sensors used to detect faults in the source-grid-load-storage collaboratively optimized distribution network. Each sensor node can communicate with the wireless sensor nodes around it. It should be noted that the wireless sensor network can be used to monitor the physical area corresponding to the source-grid-load-storage power system, such as a vast agricultural area. The wireless sensor network can be composed of N sensor nodes, and the nodes are arranged to monitor environmental parameters such as soil moisture and temperature in a specific area. For each node in the wireless sensor network, a unique integer code ranging from 1 to N can be assigned to it. The code represents the physical location or installation location of each sensor, and the code is assigned according to the actual geographical location of the node in the area. Therefore, the candidate routing path here refers to all feasible node communication schemes from the initial node to the target node.

[0023] In one embodiment, a specific encoding scheme of the routing path is as follows: Assume that there are 4 wireless sensor nodes, numbered A, B, C, and D, and let the data start from node A and eventually reach node D. Therefore, binary encoding can be used to represent the connection status between each node. It can be assumed that each node can be directly connected to any other node, and the encoding length is the possible connection mode of each node to other nodes, that is, (n(n-1)), where n is the number of nodes, here is (4x3 = 12). Assume that the routing encoding is as follows: 110001001011. Among them, 110: indicates that starting from node A, data can be sent directly to node B and node C, but cannot be sent to node D. 001: indicates that starting from node B, data can only be sent to node C, and cannot be sent to node A and node D. 001: indicates that starting from node C, data can only be sent to node D, and cannot be sent to node A and node B. 011: indicates that starting from node D, data can be sent to node B and node C, but cannot be sent to node A. In this encoding scheme, node A sends data to node D via node C, and the routing path is A to C to D.

[0024] It is understandable that binary-based coding schemes are very effective in describing data transmission paths in complex sensor networks, especially when multi-hop transmission, multi-path selection, and network redundancy need to be considered. Through this coding method, the flow of data in the network can be precisely controlled, transmission efficiency can be optimized, and the robustness of the network can be enhanced.

[0025] Specifically, by obtaining the initial node and the target node, a series of possible routing paths can be generated based on topology discovery algorithms, heuristic search algorithms, etc., to obtain candidate routing paths. In addition, the number of cloud droplets matching the candidate routing paths can be determined based on the number of candidate routing paths.

[0026] Step 120, based on the cloud model optimization algorithm, the cloud droplet position of the cloud droplet is updated and iterated until the number of iterations meets the preset conditions, and the fitness value of the cloud droplet position is calculated based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position that meets the preset conditions; Here, the cloud model optimization algorithm is an optimization algorithm that simulates the movement law of cloud droplets in nature. It combines the ideas of fuzzy logic and probability theory and can be used to efficiently and high-quality solve the optimization problems of complex problems, which contain multiple cloud droplets. The update rules include random movement of cloud droplets, movement towards high fitness areas, etc. In detail, through the generation, fusion and splitting operations of cloud droplets, global and local searches can be performed in the search space to find the optimal solution to the problem, that is, to find the optimal routing path. In addition, the cloud droplet position here can represent the candidate routing path.

[0027] Specifically, the cloud droplet position can be updated and iterated through the cloud model optimization algorithm until the number of iterations meets the preset conditions. In each iteration, the cloud droplet position will be updated according to certain rules, such as random walk, gradient descent, etc., to approach the global optimal solution.

[0028] It should be noted that the preset condition here can be that the number of iterations reaches the preset number of iterations, or that the cloud droplet tends to converge, which can indicate that the optimal solution has been found, that is, the optimal routing path has been obtained. Then, the fitness value of the cloud droplet position can be calculated by the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position that meets the preset conditions.

[0029] Here, the calculated fitness value of each cloud droplet position can be used to reflect the quality of the candidate routing path represented by each cloud droplet position under the current monitoring sensor node state. The quality here can be specifically reflected in indicators such as monitoring efficiency and sensor utilization rate.

[0030] It should be noted that the method provided in the embodiment of the present invention adopts a cloud model optimization algorithm to ensure the efficient transmission of electricity from the power generation point to the consumption point, while being able to quickly respond to load changes and system failures, optimize the distribution and utilization of power resources, reduce environmental impact, support the reliable operation and green development of the power grid, and effectively solve the problems of low accuracy and poor safety performance of the algorithms in existing routing solutions.

[0031] Step 130 , based on the fitness values ​​of the cloud droplet positions, determine an optimal cloud droplet position from the cloud droplet positions to obtain an optimal routing path.

[0032] Specifically, the fitness values ​​of the cloud droplet positions can be sorted, and the cloud droplet position with the highest fitness value can be selected as the optimal cloud droplet position, and the routing path reflected by the optimal cloud droplet position can be used as the optimal routing path. Therefore, according to the determined optimal routing path, the transmission of fault information is executed to ensure the reliability and timeliness of information transmission, and the fault information is transmitted to the relevant processing unit in a timely manner. Considering factors such as energy consumption and communication quality, the routing path is dynamically adjusted and optimized to ensure the selection and execution of the optimal routing path to cope with changes in network conditions and the needs of fault information transmission.

[0033] The method provided by the embodiment of the present invention reflects the routing path through the cloud droplet position, optimizes the cloud droplet position through a cloud model optimization algorithm, and calculates the fitness value of the cloud droplet position based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position that meets the preset conditions. Based on the fitness value of the cloud droplet position, the optimal cloud droplet position is determined from the cloud droplet positions to obtain the optimal routing path, thereby realizing efficient and high-quality dynamic routing path optimization.

[0034] Based on any of the above embodiments, in step 120, the fitness value of the cloud droplet position is calculated based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position that meets the preset conditions, including: Calculating based on the node monitoring efficiency and node weight of the wireless sensor node, obtaining the overall monitoring efficiency of the cloud droplet position; Based on the node energy consumption of the wireless sensor node and the overall energy consumption value corresponding to the cloud droplet position, obtaining the node utilization rate of the cloud droplet position; Based on the overall monitoring effectiveness and the node utilization rate, the fitness value of the cloud droplet position is calculated.

[0035] Here, the node monitoring efficiency can be used to reflect the monitoring capability of the sensor node, which can be determined based on the node's energy consumption. Generally speaking, the higher the node's energy consumption, the higher the node's monitoring efficiency, but it may also lead to a shorter network life. Therefore, the overall monitoring efficiency here can be used to reflect the monitoring capability of all sensor nodes in the routing path corresponding to the cloud droplet position. In addition, the node weight can be used to reflect the importance of the node in the entire wireless sensor network, which can be determined by the location of the sensor node, the importance of the monitoring area, and other factors.

[0036] Specifically, the overall monitoring efficiency of the cloud droplet position can be calculated by the node energy consumption of the wireless sensor node corresponding to the cloud droplet position and the node weight of each wireless sensor node. Then, the overall detection efficiency of all cloud droplet positions can be calculated by this method. The overall monitoring efficiency here can be calculated by the following formula, as shown below: In the formula, Indicates the overall monitoring effectiveness of cloud droplet positions; represents the total number of sensor nodes in the routing path corresponding to the cloud droplet position, i Indicates i sensor nodes; Indicates i The node weight of each sensor node; Indicates i Node monitoring effectiveness of sensor nodes; Indicates i The sensor node sends j The energy consumption required for data transmission by each sensor node.

[0037] In addition, the node utilization rate of the cloud droplet position can be obtained by the node energy consumption of the wireless sensor node and the overall energy consumption value corresponding to the cloud droplet position. The node utilization rate here can be calculated by the following formula: In the formula, The node utilization rate representing the cloud droplet location; It represents the total energy consumption of data transmission by all sensor nodes corresponding to the cloud droplet position.

[0038] Then, the fitness value of the cloud droplet position can be calculated by the overall monitoring efficiency and node utilization rate of the cloud droplet position. For example, the overall monitoring efficiency of each cloud droplet position can be sorted from high to low, and the rank of the cloud droplet position with the highest overall monitoring efficiency is 1, and the rank of the overall monitoring efficiency of other cloud droplet positions can be obtained by analogy. Similarly, the node utilization rate of each cloud droplet position can be sorted from high to low, and the rank of the cloud droplet position with the highest node utilization rate is 1, and the rank of the node utilization rate of other cloud droplet positions can be obtained by analogy. Then, the rank of the overall monitoring efficiency and the rank of the node utilization rate of each cloud droplet position can be weighted, and the calculated value can be used as the fitness value of the cloud droplet position.

[0039] It should be noted that the overall monitoring efficiency of the cloud droplet position is obtained by calculating the node monitoring efficiency and node weight of the wireless sensor node; the node utilization rate of the cloud droplet position is obtained based on the node energy consumption of the wireless sensor node and the overall energy consumption value corresponding to the cloud droplet position; the fitness value of the cloud droplet position is calculated based on the overall monitoring efficiency and the node utilization rate, and the accurate fitness value is obtained, thereby improving the quality of the optimal routing path finally selected, so as to execute the transmission of fault information according to the determined optimal routing path, ensure the reliability and timeliness of information transmission, and promptly transmit the fault information to the relevant processing units, so as to achieve rapid response and processing of distribution network faults.

[0040] Based on any of the above embodiments, the calculation based on the node monitoring efficiency and the node weight of the wireless sensor node to obtain the overall monitoring efficiency of the cloud droplet position includes: The node monitoring efficiency is calculated based on the basic monitoring capability of the wireless sensor node and the node energy consumption; the basic monitoring capability is determined based on at least one of the sensor type, sensor sampling accuracy, sensor measurement range and sensor response time of the node; The overall monitoring efficiency of the cloud droplet position is obtained by performing calculations based on the node monitoring efficiency and the node weights of the wireless sensor nodes.

[0041] Specifically, the basic monitoring capability can be determined by at least one of the sensor sampling accuracy, sensor measurement range, and sensor response time of the node. The basic monitoring capability of the sensor node can be calculated based on the following formula, as shown in the following formula: In the formula, It indicates the basic monitoring capability of the sensor node; Sencitivity indicates sensitivity, which can be determined based on the sensor response time; Resolution indicates sensor resolution, which can be determined based on sensor accuracy; MeaRange indicates the sensor measurement range. It can be understood that the sensitivity of the sensor can reflect the sensor's ability to respond to the target signal. The higher the sensitivity, the stronger the basic monitoring capability of the node; the higher the resolution of the sensor, the stronger the basic monitoring capability of the node; the wider the range of physical quantities that the sensor can monitor, the stronger the basic monitoring capability of the node may be.

[0042] Then, the node monitoring efficiency can be calculated by the basic monitoring capability of the sensor node and the node energy consumption. The node monitoring efficiency here can be calculated by the following formula, as shown below: In the formula, The coefficient that represents the effect of energy consumption on node monitoring performance is usually positive; it is understandable that the greater the energy consumption required for node transmission, the worse the node monitoring performance of the node; Indicates i The sensor node sends j The energy consumption required for data transmission by each sensor node.

[0043] Finally, the overall monitoring efficiency of the cloud droplet position is obtained by calculating the node monitoring efficiency and node weight of the wireless sensor nodes.

[0044] Based on any of the above embodiments, in step 120, based on the cloud model optimization algorithm, the cloud droplet position of the cloud droplet is updated and iterated until the number of iterations meets the preset condition, including: determining an initial cloud droplet position of the cloud droplet; Based on the cloud model optimization algorithm, the initial cloud droplet position of the cloud droplet is updated and iterated to obtain the current cloud droplet position of the cloud droplet, and based on the current fitness value of the cloud droplet position, the elite cloud droplet is determined; Based on the current cloud droplet position of the elite cloud droplet, the model parameters of the cloud model optimization algorithm are updated, so that the current cloud droplet position of the cloud droplet is updated and iterated for the next round based on the updated cloud model optimization algorithm until the number of iterations meets the preset conditions.

[0045] Specifically, first, in the initialization stage, the initial cloud droplet position of the cloud droplet can be randomly obtained by generating uniformly distributed random numbers. The initial cloud droplet position here can be achieved by the following formula, as shown in the following formula: In the formula, Indicates i The initial cloud droplet position of each cloud droplet; Representation interval A uniformly distributed random number generated within.

[0046] Then, the initial cloud droplet position of the cloud droplet can be updated and iterated through the cloud model optimization algorithm to obtain the current cloud droplet position of the cloud droplet. Here, the position of the cloud droplet can be obtained by the following formula, as shown in the following formula: In the formula, Indicates i The current cloud droplet position obtained by the update iteration; Indicates i The cloud droplet position input during the update iteration can be considered as i - The current cloud droplet position obtained after 1 update iteration. If it is the first iterative update, it is the initial cloud droplet position; represents the entropy value; represents the super entropy value; Represents a normal distribution from 0 to 1.

[0047] In detail, the cloud model optimization algorithm is used to update the cloud droplet position iteration. Two other cloud droplets can be randomly selected to perform fusion and split operations to achieve droplet position update iteration. The fusion operation involves generating new attribute values ​​based on the attributes of the two cloud droplets. Assume that the values ​​of the two cloud droplets in the (k)th attribute are ( ) and( ), the new value of this attribute can be calculated as follows ( ): Through the normal cloud generator, new cloud droplet properties are generated based on these three digital features, namely the cloud droplet position ( ). The normal cloud model can be expressed as { = N( , , )}, where (N) represents a normal distribution. Assume that there are two cloud droplets, denoted as cloud droplet 1 (P1) and cloud droplet 2 (P2). P1 = 101010, P2 = 011001, and the fusion ratio that can be selected is α = 0.5. That is, Ex = 0.5x101010 + 0.5x011001 = 011010. Therefore, the code of the generated new cloud droplet is 011010.

[0048] In addition, the split operation is to mutate the new cloud droplets after fusion to increase the diversity of the population. This is usually achieved by randomly adjusting certain attributes of the new individuals: select an attribute ( ), apply mutation to it, such as ( = + Δ ), where (Δ ) is a small perturbation determined by the characteristics of the problem. After generating a new cloud droplet, it is necessary to re-evaluate the fitness of the new cloud droplet and decide whether to replace a cloud droplet in the original population or directly add it to the population based on the evaluation results. Assuming that individual P = 101010, the split point that can be selected is the third position (i.e., split after the third position). The two new cloud droplets generated are: P1=101, P2=010, and the split operation can divide the original cloud droplet into two new cloud droplets at the selected split point.

[0049] It should be noted that the fusion and split operations are important genetic operations in the cloud model optimization algorithm, which are used to generate new cloud droplets to explore the search space. The fusion operation generates a new cloud droplet by combining the characteristics of two cloud droplets, and the split operation generates two new cloud droplets by cutting a cloud droplet. These operations use the expected value, entropy, and super entropy of the cloud model to adjust the crossover and mutation process in the genetic algorithm, thereby increasing the diversity and search ability of the algorithm.

[0050] It should be noted that in each update iteration process, the elite cloud droplet can be determined by the current fitness value of the cloud droplet position. Then, the model parameters of the cloud model optimization algorithm can be updated by the current cloud droplet position of the elite cloud droplet, so that the current cloud droplet position of the cloud droplet can be updated and iterated for the next round based on the updated cloud model optimization algorithm until the number of iterations meets the preset conditions.

[0051] Based on any of the above embodiments, determining elite cloud droplets based on the current fitness value of the cloud droplet position includes: Based on the routing optimization objective function, the current fitness value of the cloud droplet position is calculated; the routing optimization objective function includes at least one of minimizing the total energy consumption of the path, minimizing the path length, and maximizing the network life; The current fitness values ​​are sorted in descending order, and cloud droplets corresponding to the preset current fitness values ​​are selected as the elite cloud droplets.

[0052] Specifically, at least one of minimizing the total energy consumption of the path, minimizing the path length, and maximizing the network lifetime can be used as the routing optimization objective function, so as to obtain the real-time status of each sensor node and calculate the current fitness value of the cloud droplet position through the routing optimization objective function. Among them, the objective function of minimizing the total energy consumption of the path can be expressed as ,in, represents the energy consumption of node i sending data to node j; E represents the set of all edges of each sensor node under the routing path corresponding to the cloud droplet position; the objective function of minimizing the path length can be expressed as , Indicates and The Euclidean distance between sensor nodes, and the routing path contains k sensor nodes. It should be noted that the minimum path delay can be expressed as ,in, Represents a collection of sensor nodes in the routing path. When selecting a routing path, , indicating that the routing path P contains k sensor nodes; Representation Node i With Node j The Euclidean distance between It can be calculated by the following formula, as shown below: In the formula, Representation Node i With Node j The Euclidean distance between , Respectively represent nodes i The horizontal and vertical coordinates of , Respectively represent nodes j The horizontal and vertical coordinates of .

[0053] In addition, the objective function of maximizing the network lifetime can be expressed as , where Representation Node i The residual energy of the routing path is represented by N, and N represents all sensor nodes in the routing path. It can be understood that the sensor node consumes the least energy, but the larger the residual energy, the longer the network life of the corresponding routing path.

[0054] Finally, the current fitness values ​​can be sorted in descending order, and the cloud droplets corresponding to the preset current fitness values ​​can be selected as elite cloud droplets. It should be noted that when the cloud model optimization algorithm is updated to meet the preset conditions, after the current fitness value of each position is calculated, each cloud droplet can be hierarchically divided based on the calculated current fitness value to obtain the most advantageous solution set, that is, the most advantageous cloud droplet position set.

[0055] Based on any of the above embodiments, based on the current cloud droplet position of the elite cloud droplet, the model parameters of the cloud model optimization algorithm are updated, including: Based on the current cloud droplet position of the elite cloud droplet, an updated expected value of the cloud model optimization algorithm is calculated; Based on the current cloud droplet position of the elite cloud droplet and the updated expected value, an updated entropy value is calculated; Based on the current cloud droplet position of the elite cloud droplet and the updated entropy value, an updated super entropy value is calculated; Based on the updated expected value, the updated entropy value and the updated super entropy value, the model parameters of the cloud model optimization algorithm are updated.

[0056] Specifically, after obtaining the current cloud droplet position of the elite cloud droplet, the updated expected value of the cloud model optimization algorithm can be calculated by the following formula, as shown in the following formula: In the formula, Indicates the update expectation value; Indicates the number of elite cloud droplets, i Indicates i Elite cloud drops; Indicates i The current droplet position of the elite droplets; The updated entropy value can also be calculated by the current cloud droplet position and the updated expected value of the elite cloud droplet. The updated entropy value here can be calculated by the following formula, as shown below: In the formula, Indicates the updated entropy value; Indicates the update expectation value; Indicates the number of elite cloud droplets, i Indicates i Elite cloud drops; Indicates i The current droplet position of the elite droplet.

[0057] In addition, the updated super entropy value can also be calculated based on the current cloud droplet position and the updated entropy value of the elite cloud droplet. The updated super entropy value here can be calculated by the following formula, as shown in the following formula: In the formula, Indicates the update of super entropy value; Elite Cloud Drop i The current entropy value of .

[0058] Finally, the model parameters of the cloud model optimization algorithm may be updated based on the updated expected value, the updated entropy value, and the updated super entropy value.

[0059] Based on any of the above embodiments, Figure 2 is a schematic diagram of a method for transmitting fault data based on a routing path provided by the present invention, such as Figure 2 As shown in the figure, multiple wireless sensor nodes are set up in the monitoring area to form a wireless sensor node network, in which each wireless sensor node transmits information through the base station signal. After obtaining the real-time monitoring data, the cloud model optimization algorithm is used to optimize the routing path, and different routing paths are explored by continuously updating the position of cloud droplets. The node sensor status is monitored in real time, and the power grid fault information is transmitted according to the determined optimal routing path to ensure the reliability and timeliness of information transmission. This method can reduce unnecessary communication and energy consumption, extend the life of sensor nodes, and thus improve the service life of the entire network, provide high-quality information transmission, and ensure feasibility.

[0060] It should be noted that this method brings benefits by improving energy efficiency, enhancing system reliability and stability, optimizing resource allocation, supporting the integration of renewable energy, improving economic benefits, strengthening the adaptive ability of the power grid, and improving the environmental friendliness of the system. The cloud model optimization algorithm can ensure the efficient transmission of electricity from the power generation point to the consumption point, while also quickly responding to load changes and system failures, optimizing the distribution and utilization of power resources, reducing environmental impact, supporting the reliable operation and green development of the power grid, and effectively solving the problems of low accuracy and poor safety performance of the algorithm in the existing routing solution.

[0061] The method provided by the embodiment of the present invention has a clear process, can realize dynamic routing optimization, and has a significant effect in improving sensor utilization efficiency and device reliability. In addition, the method is also safe, energy-saving and robust, promotes the fault tolerance and overall reliability of sensor target routing, and promotes the efficient use of sensor networks.

[0062] Based on any of the above embodiments, Figure 3 Schematic diagram of the structure of the route optimization device of the source-grid-load-storage power system provided by the present invention. Figure 3 As shown, the device comprises: An acquisition unit 310 acquires a candidate routing path of the wireless sensor network and determines the number of cloud droplets matching the candidate routing path; The optimization unit 320 updates and iterates the cloud droplet position of the cloud droplet based on the cloud model optimization algorithm until the number of iterations meets the preset conditions, and calculates the fitness value of the cloud droplet position based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position that meets the preset conditions; The path determination unit 330 determines an optimal cloud droplet position from the cloud droplet positions based on the fitness values ​​of the cloud droplet positions to obtain an optimal routing path.

[0063] The device provided by the embodiment of the present invention reflects the routing path through the cloud droplet position, optimizes the cloud droplet position through a cloud model optimization algorithm, and calculates the fitness value of the cloud droplet position based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position under preset conditions. Based on the fitness value of the cloud droplet position, the optimal cloud droplet position is determined from the cloud droplet positions to obtain the optimal routing path, thereby realizing efficient and high-quality dynamic routing path optimization.

[0064] Based on any of the above embodiments, the optimization unit is specifically used for: Calculating based on the node monitoring efficiency and node weight of the wireless sensor node, obtaining the overall monitoring efficiency of the cloud droplet position; Based on the node energy consumption of the wireless sensor node and the overall energy consumption value corresponding to the cloud droplet position, obtaining the node utilization rate of the cloud droplet position; Based on the overall monitoring effectiveness and the node utilization rate, the fitness value of the cloud droplet position is calculated.

[0065] Based on any of the above embodiments, the optimization unit is further specifically used for: The node monitoring efficiency is calculated based on the basic monitoring capability of the wireless sensor node and the node energy consumption; the basic monitoring capability is determined based on at least one of the sensor sampling accuracy, sensor measurement range and sensor response time of the node; The overall monitoring efficiency of the cloud droplet position is obtained by performing calculations based on the node monitoring efficiency and the node weights of the wireless sensor nodes.

[0066] Based on any of the above embodiments, the optimization unit is further specifically used for: determining an initial cloud droplet position of the cloud droplet; Based on the cloud model optimization algorithm, the initial cloud droplet position of the cloud droplet is updated and iterated to obtain the current cloud droplet position of the cloud droplet, and based on the current fitness value of the cloud droplet position, the elite cloud droplet is determined; Based on the current cloud droplet position of the elite cloud droplet, the model parameters of the cloud model optimization algorithm are updated, so that the current cloud droplet position of the cloud droplet is updated and iterated for the next round based on the updated cloud model optimization algorithm until the number of iterations meets the preset conditions.

[0067] Based on any of the above embodiments, the optimization unit is further specifically used for: Based on the routing optimization objective function, the current fitness value of the cloud droplet position is calculated; the routing optimization objective function includes at least one of minimizing the total energy consumption of the path, minimizing the path length, and maximizing the network life; The current fitness values ​​are sorted in descending order, and cloud droplets corresponding to the preset current fitness values ​​are selected as the elite cloud droplets.

[0068] Based on any of the above embodiments, the optimization unit is further specifically used for: Based on the current cloud droplet position of the elite cloud droplet, an updated expected value of the cloud model optimization algorithm is calculated; Based on the current cloud droplet position of the elite cloud droplet and the updated expected value, an updated entropy value is calculated; Based on the current cloud droplet position of the elite cloud droplet and the updated entropy value, an updated super entropy value is calculated; Based on the updated expected value, the updated entropy value and the updated super entropy value, the model parameters of the cloud model optimization algorithm are updated.

[0069] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the routing optimization method of the source-grid-load-storage power system, the method comprising: obtaining the candidate routing path of the wireless sensor network, determining the number of cloud droplets matching the candidate routing path; updating and iterating the cloud droplet position of the cloud droplet based on the cloud model optimization algorithm until the number of iterations meets the preset conditions, and calculating the fitness value of the cloud droplet position based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position that meets the preset conditions; determining the optimal cloud droplet position from the cloud droplet position based on the fitness value of the cloud droplet position to obtain the optimal routing path.

[0070] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0071] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the routing optimization method of the source-grid-load-storage power system provided by the above methods, and the method includes: obtaining candidate routing paths of the wireless sensor network, and determining the number of cloud droplets matching the candidate routing paths; based on a cloud model optimization algorithm, updating and iterating the cloud droplet positions of the cloud droplets until the number of iterations meets a preset condition, and calculating the fitness value of the cloud droplet position based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position that meets the preset condition; based on the fitness value of the cloud droplet position, determining the optimal cloud droplet position from the cloud droplet positions to obtain the optimal routing path.

[0072] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the routing optimization method of the source-grid-load-storage power system provided by the above-mentioned methods, the method comprising: obtaining a candidate routing path of the wireless sensor network, and determining the number of cloud droplets matching the candidate routing path; based on a cloud model optimization algorithm, updating and iterating the cloud droplet position of the cloud droplet until the number of iterations meets a preset condition, and calculating the fitness value of the cloud droplet position based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position that meets the preset condition; based on the fitness value of the cloud droplet position, determining the optimal cloud droplet position from the cloud droplet positions to obtain the optimal routing path.

[0073] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0074] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A route optimization method for a source-grid-load-storage power system, characterized in that: include: Obtaining a candidate routing path of the wireless sensor network, and determining the number of cloud droplets matching the candidate routing path; Based on the cloud model optimization algorithm, the cloud droplet position of the cloud droplet is updated and iterated until the number of iterations meets the preset conditions, and the fitness value of the cloud droplet position is calculated based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position that meets the preset conditions; Based on the fitness values ​​of the cloud drop positions, an optimal cloud drop position is determined from the cloud drop positions to obtain an optimal routing path.

2. The routing optimization method of the source-grid-load-storage power system according to claim 1 is characterized in that: The step of calculating the fitness value of the cloud droplet position based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position that meets the preset conditions includes: Calculating based on the node monitoring efficiency and node weight of the wireless sensor node, obtaining the overall monitoring efficiency of the cloud droplet position; Based on the node energy consumption of the wireless sensor node and the overall energy consumption value corresponding to the cloud droplet position, obtaining the node utilization rate of the cloud droplet position; Based on the overall monitoring effectiveness and the node utilization rate, the fitness value of the cloud droplet position is calculated.

3. The routing optimization method of the source-grid-load-storage power system according to claim 2 is characterized in that: The calculation based on the node monitoring efficiency and the node weight of the wireless sensor node to obtain the overall monitoring efficiency of the cloud droplet position includes: The node monitoring efficiency is calculated based on the basic monitoring capability of the wireless sensor node and the node energy consumption; the basic monitoring capability is determined based on at least one of the sensor sampling accuracy, sensor measurement range and sensor response time of the node; The overall monitoring efficiency of the cloud droplet position is obtained by performing calculations based on the node monitoring efficiency and the node weights of the wireless sensor nodes.

4. The routing optimization method of the source-grid-load-storage power system according to any one of claims 1 to 3, characterized in that: The cloud droplet positions of the cloud droplets are updated and iterated based on the cloud model optimization algorithm until the number of iterations meets the preset conditions, including: determining an initial cloud droplet position of the cloud droplet; Based on the cloud model optimization algorithm, the initial cloud droplet position of the cloud droplet is updated and iterated to obtain the current cloud droplet position of the cloud droplet, and based on the current fitness value of the cloud droplet position, the elite cloud droplet is determined; Based on the current cloud droplet position of the elite cloud droplet, the model parameters of the cloud model optimization algorithm are updated, so that the current cloud droplet position of the cloud droplet is updated and iterated for the next round based on the updated cloud model optimization algorithm until the number of iterations meets the preset conditions.

5. The routing optimization method of the source-grid-load-storage power system according to claim 4 is characterized in that: The step of determining an elite cloud droplet based on the current fitness value of the cloud droplet position includes: Based on the routing optimization objective function, the current fitness value of the cloud droplet position is calculated; the routing optimization objective function includes at least one of minimizing the total energy consumption of the path, minimizing the path length, and maximizing the network life; The current fitness values ​​are sorted in descending order, and cloud droplets corresponding to the preset current fitness values ​​are selected as the elite cloud droplets.

6. The routing optimization method of the source-grid-load-storage power system according to claim 4 is characterized in that: The updating of the model parameters of the cloud model optimization algorithm based on the current cloud droplet position of the elite cloud droplet includes: Based on the current cloud droplet position of the elite cloud droplet, an updated expected value of the cloud model optimization algorithm is calculated; Based on the current cloud droplet position of the elite cloud droplet and the updated expected value, an updated entropy value is calculated; Based on the current cloud droplet position of the elite cloud droplet and the updated entropy value, an updated super entropy value is calculated; Based on the updated expected value, the updated entropy value and the updated super entropy value, the model parameters of the cloud model optimization algorithm are updated.

7. A route optimization device for a source-grid-load-storage power system, characterized in that: include: An acquisition unit, which acquires a candidate routing path of the wireless sensor network and determines the number of cloud droplets matching the candidate routing path; The optimization unit updates and iterates the cloud droplet position of the cloud droplet based on the cloud model optimization algorithm until the number of iterations meets the preset conditions, and calculates the fitness value of the cloud droplet position based on the node energy consumption value of the wireless sensor node corresponding to the cloud droplet position that meets the preset conditions; The path determination unit determines an optimal cloud droplet position from the cloud droplet positions based on the fitness values ​​of the cloud droplet positions to obtain an optimal routing path.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the routing optimization method of the source-grid-load-storage power system as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the routing optimization method of the source-grid-load-storage power system as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the routing optimization method of the source-grid-load-storage power system as claimed in any one of claims 1 to 6 is implemented.

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