Iot sensing data security management method and system for transmission and distribution

Abstract

The application discloses a kind of thing networking transmission and distribution use perception data security management method and system, it is related to transmission and distribution use field, including: establishing edge node, transmission and distribution use perception data are received and analyzed;Each edge node perception data is optimized and handled, obtains single edge node positive-inverse optimal layout;Establishes multi-island genetic algorithm positive optimization model, obtains the positive optimal layout of whole transmission and distribution use perception data;Establishes multi-island genetic algorithm reverse optimization model, obtains the reverse optimal layout of whole transmission and distribution use perception data;Establish transmission and distribution use perception data security management model, and filter out the layout of optimal transmission and distribution use perception data;Based on thing networking technology, each transmission and distribution use perception data information and optimal layout are received, stored and displayed.Through transmission and distribution use perception data security management model, using positive-inverse optimal layout mode, effectively improve the efficient distribution of transmission and distribution use, guarantee the operation safety of transmission and distribution use.

Description

Technical Field

[0001] This invention relates to the field of power transmission, transformation and distribution, and more specifically to a method and system for managing the security of sensing data in Internet of Things (IoT) power transmission, transformation and distribution. Background Technology

[0002] "Transmission, transformation, distribution, and consumption" is an abbreviation for the four key links of a power system, mainly including: power transmission, voltage transformation, distribution network, and end-user electricity consumption. The "transmission, transformation, distribution, and consumption" of a power system are physically closely coupled, but in terms of management, they belong to different levels and professional departments. Therefore, these four links are both independent entities in management and data processing, but also interconnected. Especially with the rapid development of new energy technologies, when the power generation of new energy exceeds the local load, power backflow and "stranded power" phenomena are prone to occur, leading to power supply imbalance in the power system and even causing power system paralysis. Therefore, ensuring the stability and dynamic balance of "transmission, transformation, distribution, and consumption" is of great significance to maintaining the stability of the power system.

[0003] Existing technologies are mainly laid out in a sequential manner, involving power transmission, voltage transformation, power distribution networks, and power allocation to end users. While this approach can achieve physical uniformity and stability, with the development of new energy technologies, the traditional forward layout method combined with genetic algorithms is difficult to adapt to current needs and is easily affected by new energy technologies, leading to power backflow and "stranded power," which is detrimental to the overall balance of the power system and poses certain safety hazards. Summary of the Invention

[0004] To address the aforementioned technical issues, this paper provides a method and system for the security management of IoT transmission, transformation, distribution, and sensing data. This technical solution addresses the challenges posed by the development of new energy technologies mentioned in the background section. The traditional forward layout method combined with genetic algorithms is ill-suited to current needs and is easily affected by new energy technologies, leading to backfeeding and "stranded power," which is detrimental to the overall balance of the power system and poses certain safety hazards.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for secure management of IoT transmission, transformation, distribution, and sensing data includes:

[0007] Based on each link of transmission, transformation, distribution and utilization, edge nodes are established respectively, and the sensing data of transmission, transformation, distribution and utilization are received and analyzed through each edge node;

[0008] Based on the genetic algorithm, the perception data of each edge node is optimized to obtain the optimal forward-inverse layout of a single edge node;

[0009] Based on the forward optimal layout of a single edge node, a multi-island genetic algorithm forward optimization model is established to obtain the forward optimal layout of the overall transmission, transformation, distribution and sensing data;

[0010] Based on the reverse optimal layout of a single edge node, a multi-island genetic algorithm reverse optimization model is established to obtain the reverse optimal layout of the overall transmission, transformation, distribution and sensing data.

[0011] Establish a data security management model for transmission, transformation, distribution and utilization sensing data. By acquiring changes in sensing data at each stage of transmission, transformation, distribution and utilization, the optimal layout of sensing data for transmission, transformation, distribution and utilization can be selected.

[0012] Based on IoT technology, a data security management platform for transmission, transformation, distribution and utilization sensing data is established to receive, store and display the sensing data information and optimal layout of each transmission, transformation and distribution system.

[0013] Preferably, the step of establishing edge nodes according to each link of transmission, transformation, distribution and utilization, and receiving and analyzing the sensing data of transmission, transformation, distribution and utilization through each edge node specifically includes:

[0014] Based on the various links of power transmission, transformation, distribution and consumption, each link is set as an edge node for sensing data. The links of power transmission, transformation, distribution and consumption include: power transmission, voltage transformation, power distribution network and end-user power consumption.

[0015] Based on each edge node, a corresponding data acquisition device for transmission, transformation, distribution and utilization sensing is set up to acquire and save the data collected by each edge node.

[0016] Preferably, the step of optimizing the perception data of each edge node based on a genetic algorithm to obtain the optimal forward-inverse layout of a single edge node specifically includes:

[0017] Based on the transmission, transformation, distribution and utilization sensing data collected by each edge node, establish a transmission, transformation, distribution and utilization sensing dataset for each edge node;

[0018] Based on the Kalman filter algorithm, the data in the sensing dataset of each edge node's transmission, transformation, distribution and utilization are filtered and denoised.

[0019] The data in the sensor dataset of each edge node after filtering are normalized to eliminate the influence of data units;

[0020] Based on the application scenarios of transmission, transformation, distribution and utilization, the forward adaptive function and the reverse adaptive function of the sensing data of each edge node transmission, transformation, distribution and utilization are set respectively;

[0021] Based on the genetic algorithm, the perception data of each edge node is optimized to obtain the forward-inverse optimal layout of a single edge node.

[0022] Preferably, the step of establishing a multi-island genetic algorithm forward optimization model based on the forward optimal layout of a single edge node to obtain the forward optimal layout of the overall transmission, distribution, and sensing data specifically includes:

[0023] The power transmission, distribution and consumption sensing data are optimized by adopting a method of sequential allocation of power transmission, voltage transformation, power distribution network and end user power consumption, and this allocation method is marked as positive allocation;

[0024] Based on the positive optimal layout of a single edge node, and combined with the actual transmission and distribution of each edge node, the positive fitness value is set to determine the deviation of the gene population in the positive optimal layout of a single edge node.

[0025] Based on the deviation of the gene population in the positive optimal layout of a single edge node, and using big data, a boundary threshold for population migration is set.

[0026] The migration rate of the population in the forward optimal layout of a single edge node is determined based on the deviation of the gene population and the boundary threshold of population migration.

[0027] Based on the migration rate of the population in the forward optimal layout of a single edge node, a multi-island genetic algorithm forward optimization model is established to obtain the forward optimal layout of the overall transmission, distribution and sensing data.

[0028] The expression for the deviation of the gene population in the optimal layout of a single edge node is:

[0029]

[0030] In the formula, This represents the deviation of an individual in the population within a single edge node's positive optimal layout. For the equilibrium constant term, For the first Each edge node transmits and modifies the fitness value of an individual in the population. A positive fitness value is set for the actual transmission and distribution of each edge node. This represents the number of gene populations in the optimal forward layout of a single edge node.

[0031] The migration rate expression for the population in the positive optimal layout of a single edge node is:

[0032]

[0033] In the formula, For the first The migration rate of each individual population at each edge node is used for transmission and matching. The boundary threshold for population migration is denoted as , where The meaning is that when the deviation of the gene population in the positive optimal layout of a single edge node is less than the boundary threshold of population migration, all fitness values ​​of the individuals in the population that are used for transmission and matching at each edge node are obtained. By judging the probability of the fitness of a single individual in the whole, the migration rate of a single individual is determined. Otherwise, no migration is performed.

[0034] The expression for the forward optimization model of the multi-island genetic algorithm is as follows:

[0035]

[0036] In the formula, For the first The edge node transmission and distribution population is in the first edge nodes and the edge nodes The optimized population under the combined effect For the first The edge node about the first The function relating the migration rate of individuals in the population to the migration rate of each edge node is used. For the first Each edge node transmits a set of indicator functions representing the fitness values ​​of individuals in the population. The symbol for convolution. For the first The functional relationship between the migration rate of individuals in the population transmitted by each edge node and the first... The convolution value of the set of fitness values ​​of individuals in the population is used to transmit the variants of each edge node. For the first The edge node about the first The function relating the migration rate of individuals in the population to the migration rate of each edge node is used. For the first Each edge node transmits an indicator function that uses the set of fitness values ​​of individuals in the population.

[0037] Preferably, the step of establishing a multi-island genetic algorithm reverse optimization model based on the reverse optimal layout of a single edge node to obtain the reverse optimal layout of the overall transmission, distribution, and sensing data specifically includes:

[0038] The power consumption of end users, power distribution network, voltage transformation and power transmission are allocated sequentially to optimize the sensing data of power transmission, distribution and consumption, and this allocation method is marked as reverse allocation.

[0039] Based on the reverse optimal layout of a single edge node and the reverse adaptive function of the transmission and distribution sensing data of each edge node, an adaptive genetic operator is set to constrain the newly added user population individuals;

[0040] Based on the adaptive genetic operator, the migration rate of the population in the reverse optimal layout of a single edge node is dynamically adjusted to ensure that the newborn individuals meet the overall constraints of the transmission, transformation, allocation and utilization.

[0041] Based on the migration rate of the population in the reverse optimal layout of a single edge node, a multi-island genetic algorithm reverse optimization model is established to obtain the reverse optimal layout of the overall transmission, distribution and sensing data.

[0042] The expression for the adaptive genetic operator is:

[0043]

[0044] In the formula, The migration rate of the population in the reverse optimal layout of a single edge node after the adaptive genetic operator is constrained. This represents the minimum migration rate of the population in the reverse optimal layout of a single edge node. This represents the maximum migration rate of the population in the reverse optimal layout of a single edge node. The maximum value of the reverse fitness of the sensing data is used to power the transmission and transformation of each edge node. The sensor data is used to provide inverse adaptive adaptation for each edge node's power transmission and transformation. The minimum value of the reverse fitness of the sensing data used by each edge node for transmission, transformation, and distribution;

[0045] The expression for the multi-island genetic algorithm inverse optimization model is as follows:

[0046]

[0047] In the formula, For the first The edge node transmission and distribution population is in the first edge nodes and the edge nodes The optimized population under the combined effect For the first The edge node about the first The function relating the migration rate of individuals in the population to the migration rate of each edge node is used. For the first Each edge node transmits a set of indicator functions representing the fitness values ​​of individuals in the population. The symbol for convolution. For the first The functional relationship between the migration rate of individuals in the population transmitted by each edge node and the first... The convolution value of the set of fitness values ​​of individuals in the population is used to transmit the variants of each edge node. For the first The edge node about the first The function relating the migration rate of individuals in the population to the migration rate of each edge node is used. For the first Each edge node transmits an indicator function that uses the set of fitness values ​​of individuals in the population.

[0048] Preferably, the establishment of the transmission, transformation, distribution, and utilization sensing data security management model, which involves acquiring changes in sensing data at each stage of transmission, transformation, distribution, and utilization, and selecting the optimal layout of the sensing data, specifically includes:

[0049] Based on the changes in the sensing data of power transmission and end-user electricity consumption, determine the degree of change in the sensing data at both ends of the transmission, transformation, distribution and consumption.

[0050] Based on the Pearson correlation coefficient, the weights of the impact of changes in power transmission and end-user electricity consumption on the entire transmission, transformation, distribution and utilization operation are obtained.

[0051] Based on the forward optimization model and the backward optimization model of the multi-island genetic algorithm, and combined with the degree of change and influence weight of the sensing data at both ends of the transmission, transformation, distribution and utilization, a safety management model for the sensing data of the transmission, transformation, distribution and utilization is established.

[0052] Based on the data security management model for transmission, transformation, distribution and utilization sensing data, the changes in sensing data in each link of transmission, transformation, distribution and utilization are obtained, and the optimal layout of sensing data for transmission, transformation, distribution and utilization is selected.

[0053] The expression for the degree of change in the sensing data at both ends of the transmission and distribution system is:

[0054]

[0055] In the formula, To measure the degree of change in sensing data at both ends of the transmission, distribution, and utilization system. The total amount detected by sensing data at both ends of the transmission and distribution system. The original amount of sensing data at both ends of the transmission and distribution system;

[0056] The expression for the data security management model of transmission, transformation, distribution and utilization sensing is:

[0057]

[0058] In the formula, To sense the degree of data change at the power transmission end, The weighting of the impact of changes in sensing data at the power transmission end on the overall operation of power transmission, transformation, distribution, and consumption. Weight the impact of changes in end-user perceived data on the overall operation of transmission, transformation, distribution, and utilization. To enable end users to perceive the degree of data change.

[0059] Preferably, the establishment of a data security management platform for transmission, transformation, distribution and utilization sensing based on Internet of Things (IoT) technology, used to receive, store and display various transmission, transformation, distribution and utilization sensing data information and optimal layout, specifically includes:

[0060] Based on IoT technology, it receives and collects sensing data from various edge nodes for power transmission, distribution, and use.

[0061] Establish a data security management platform for transmission, transformation, distribution and utilization sensing data to build the operating environment for the data security management model for transmission, transformation, distribution and utilization sensing data and ensure the normal operation of the program;

[0062] Based on the data security management platform for transmission, transformation, distribution and utilization sensing, it receives, stores and displays the sensing data information and optimal layout of each transmission, transformation and utilization system.

[0063] Furthermore, this solution proposes an IoT transmission, transformation, distribution, and sensing data security management system to implement the aforementioned IoT transmission, transformation, distribution, and sensing data security management method, including:

[0064] The edge node module is used to establish edge nodes according to each link of transmission, transformation, distribution and utilization, and to receive and analyze the sensing data of transmission, transformation, distribution and utilization through each edge node;

[0065] The single-point optimization module is used to optimize the perception data of each edge node based on a genetic algorithm to obtain the forward-inverse optimal layout of a single edge node.

[0066] The overall optimal module is used to establish a multi-island genetic algorithm forward optimization model based on the forward optimal layout of a single edge node to obtain the forward optimal layout of the overall transmission, transformation, distribution and utilization sensing data; to establish a multi-island genetic algorithm reverse optimization model based on the reverse optimal layout of a single edge node to obtain the reverse optimal layout of the overall transmission, transformation, distribution and utilization sensing data; and to establish a transmission, transformation, distribution and utilization sensing data security management model, which selects the optimal layout of the transmission, transformation, distribution and utilization sensing data by acquiring the changes in sensing data in each link of transmission, transformation, distribution and utilization.

[0067] The management platform module is used to establish a data security management platform for transmission, transformation, distribution and utilization sensing based on Internet of Things (IoT) technology. It is used to receive, store and display the sensing data information and optimal layout of each transmission, transformation and distribution unit.

[0068] Preferably, the overall optimal module includes:

[0069] A forward optimization unit is used to establish a multi-island genetic algorithm forward optimization model based on the forward optimal layout of a single edge node, and obtain the forward optimal layout of the overall transmission, transformation, distribution and sensing data.

[0070] The reverse optimization unit is used to establish a multi-island genetic algorithm reverse optimization model based on the reverse optimal layout of a single edge node, and obtain the reverse optimal layout of the overall transmission, transformation, distribution and sensing data.

[0071] The integrated optimization unit is used to establish a data security management model for transmission, transformation, distribution and utilization sensing data. By acquiring the changes in sensing data in each link of transmission, transformation, distribution and utilization, the optimal layout of transmission, transformation, distribution and utilization sensing data is selected.

[0072] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0073] By setting each link in the power transmission, distribution, and utilization process as edge nodes, and utilizing these edge nodes to collect sensing data, a genetic algorithm is used to set the forward and reverse fitness functions for the sensing data of each edge node, thereby obtaining the optimal forward-reverse layout of a single edge node. Secondly, based on the sequential allocation of power transmission, voltage transformation, distribution network, and end-user electricity consumption, a multi-island genetic algorithm forward optimization model is established by analyzing the migration rate of gene population fusion between different edge nodes, thereby obtaining the optimal forward layout of the overall power transmission, distribution, and utilization sensing data. Furthermore, through the reverse allocation of end-user electricity consumption, distribution network, voltage transformation, and power transmission, a genetic algorithm forward optimization model is established. An adaptive genetic operator is introduced to dynamically adjust the migration rate of the population in the reverse optimal layout of a single edge node, constraining newborn individuals to meet the overall constraints of the transmission, distribution, and allocation system. This establishes a multi-island genetic algorithm reverse optimization model to obtain the reverse optimal layout of the overall transmission, distribution, and allocation sensing data. Finally, by combining the multi-island genetic algorithm forward optimization model and the multi-island genetic algorithm reverse optimization model with the degree of change and influence weight of sensing data at both ends of the transmission, distribution, and allocation system, a transmission, distribution, and allocation sensing data security management model is established. Based on the model, the changes in sensing data at each stage of the transmission, distribution, and allocation system are obtained, and the optimal layout of the transmission, distribution, and allocation sensing data is selected. This effectively improves the efficiency of the transmission, distribution, and allocation system and ensures its operational safety. Attached Figure Description

[0074] Figure 1 This is a flowchart of a method for managing the security of IoT transmission, transformation, and distribution sensing data according to the present invention.

[0075] Figure 2 The present invention uses a genetic algorithm to optimize the perception data of each edge node and obtains a flowchart of the forward-inverse optimal layout of a single edge node.

[0076] Figure 3 The flowchart of the present invention establishes a multi-island genetic algorithm forward optimization model based on the forward optimal layout of a single edge node to obtain the forward optimal layout of the overall transmission, transformation, distribution and sensing data;

[0077] Figure 4 The flowchart of the reverse optimal layout of the overall transmission, transformation, distribution and sensing data is as follows: Based on the reverse optimal layout of a single edge node, a multi-island genetic algorithm reverse optimization model is established to obtain the reverse optimal layout of the overall transmission, transformation and distribution sensing data.

[0078] Figure 5 To establish a data security management model for transmission, transformation, distribution and utilization sensing data in this invention, the optimal layout flowchart for transmission, transformation, distribution and utilization sensing data is selected by acquiring the changes in sensing data at each stage of transmission, transformation, distribution and utilization. Detailed Implementation

[0079] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0080] Reference Figure 1 As shown, a method for secure management of IoT transmission, transformation, distribution, and sensing data includes:

[0081] Based on each link of transmission, transformation, distribution and utilization, edge nodes are established respectively, and the sensing data of transmission, transformation, distribution and utilization are received and analyzed through each edge node;

[0082] Based on the genetic algorithm, the perception data of each edge node is optimized to obtain the optimal forward-inverse layout of a single edge node;

[0083] Based on the forward optimal layout of a single edge node, a multi-island genetic algorithm forward optimization model is established to obtain the forward optimal layout of the overall transmission, transformation, distribution and sensing data;

[0084] Based on the reverse optimal layout of a single edge node, a multi-island genetic algorithm reverse optimization model is established to obtain the reverse optimal layout of the overall transmission, transformation, distribution and sensing data.

[0085] Establish a data security management model for transmission, transformation, distribution and utilization sensing data. By acquiring changes in sensing data at each stage of transmission, transformation, distribution and utilization, the optimal layout of sensing data for transmission, transformation, distribution and utilization can be selected.

[0086] Based on IoT technology, a data security management platform for transmission, transformation, distribution and utilization sensing data is established to receive, store and display the sensing data information and optimal layout of each transmission, transformation and distribution system.

[0087] This can be explained by the following: This scheme sets each link in the transmission, transformation, distribution, and utilization process as edge nodes. These edge nodes collect sensing data from the transmission, transformation, distribution, and utilization processes. A genetic algorithm is used to set the forward and backward fitness functions for the sensing data of each edge node, thereby obtaining the optimal forward-backward layout for a single edge node. Secondly, based on the sequential allocation of power transmission, voltage transformation, distribution network, and end-user electricity consumption, a multi-island genetic algorithm forward optimization model is established by analyzing the migration rate of gene populations merging between different edge nodes, thus obtaining the optimal forward layout of the overall transmission, transformation, distribution, and utilization sensing data. Furthermore, the backward fitness function is used to allocate power consumption, distribution network, voltage transformation, and power transmission in sequence. This approach introduces an adaptive genetic operator to dynamically adjust the migration rate of the population in the reverse optimal layout of a single edge node, constraining newborn individuals to meet the overall constraints of the transmission, distribution, and allocation system. This establishes a multi-island genetic algorithm reverse optimization model to obtain the reverse optimal layout of the overall transmission, distribution, and allocation sensing data. Finally, by combining the multi-island genetic algorithm forward optimization model and the multi-island genetic algorithm reverse optimization model with the degree of change and influence weight of sensing data at both ends of the transmission, distribution, and allocation system, a transmission, distribution, and allocation sensing data security management model is established. Based on the model, the changes in sensing data at each stage of the transmission, distribution, and allocation system are obtained, and the optimal layout of the transmission, distribution, and allocation sensing data is selected. This effectively improves the efficiency of transmission, distribution, and allocation and ensures the safe operation of the system.

[0088] Reference Figure 2 As shown, the optimization of the perception data of each edge node based on the genetic algorithm to obtain the optimal forward-inverse layout of a single edge node specifically includes:

[0089] Based on the transmission, transformation, distribution and utilization sensing data collected by each edge node, establish a transmission, transformation, distribution and utilization sensing dataset for each edge node;

[0090] Based on the Kalman filter algorithm, the data in the sensing dataset of each edge node's transmission, transformation, distribution and utilization are filtered and denoised.

[0091] The data in the sensor dataset of each edge node after filtering are normalized to eliminate the influence of data units;

[0092] Based on the application scenarios of transmission, transformation, distribution and utilization, the forward adaptive function and the reverse adaptive function of the sensing data of each edge node transmission, transformation, distribution and utilization are set respectively;

[0093] Based on the genetic algorithm, the perception data of each edge node is optimized to obtain the forward-inverse optimal layout of a single edge node.

[0094] This can be explained by the fact that the forward allocation method of transmission, transformation, distribution, and utilization, and the reverse allocation method of utilization, transmission, and utilization correspond to different fitness functions in the genetic algorithm. The forward allocation method of transmission, transformation, distribution, and utilization mainly focuses on minimizing power generation costs and satisfying power supply constraints at each level. On the other hand, the reverse allocation method of utilization, transmission, and utilization mainly focuses on maximizing user utility and adjusting the upper-level power grid in the reverse direction. Therefore, when setting the fitness function of the transmission, transformation, distribution, and utilization sensing data of each edge node, it is necessary to consider both forward and reverse aspects. For example, in the forward allocation process of transmission, transformation, distribution, and utilization, the fitness function for power transmission can be set as follows: In the formula, As a weight of investment costs, For investment costs, For power loss, As a weight for power loss, To meet the reliability criterion of N-1, To satisfy the weighting of the N-1 criterion for reliability measurement, and in the reverse distribution process of power transmission via distribution transformers, the fitness function for power transmission can be set as follows: In the formula, This represents the total number of power transmission lines for transmitting electrical energy. For the first The current useful power, For the first Maximum allowable power of the line This represents the penalty coefficient for voltage violations. The number of nodes exceeding voltage limits is given. A comparison of these two adaptive functions shows that the first focuses on economy and losses, while the second focuses on user safety. Therefore, different adaptive functions can be used to establish a distribution method for both forward and reverse power transmission and distribution. The above method is only a functional example for easy understanding and is not a limited function. The specific adaptive function can be set according to the actual power demand.

[0095] Reference Figure 3 As shown, the step of establishing a multi-island genetic algorithm forward optimization model based on the forward optimal layout of a single edge node to obtain the forward optimal layout of the overall transmission, distribution, and sensing data specifically includes:

[0096] The power transmission, distribution and consumption sensing data are optimized by adopting a method of sequential allocation of power transmission, voltage transformation, power distribution network and end user power consumption, and this allocation method is marked as positive allocation;

[0097] Based on the positive optimal layout of a single edge node, and combined with the actual transmission and distribution of each edge node, the positive fitness value is set to determine the deviation of the gene population in the positive optimal layout of a single edge node.

[0098] Based on the deviation of the gene population in the positive optimal layout of a single edge node, and using big data, a boundary threshold for population migration is set.

[0099] The migration rate of the population in the forward optimal layout of a single edge node is determined based on the deviation degree of the gene population and the boundary threshold of population migration.

[0100] Based on the migration rate of the population in the forward optimal layout of a single edge node, a multi-island genetic algorithm forward optimization model is established to obtain the forward optimal layout of the overall transmission, distribution and sensing data.

[0101] The expression for the deviation of the gene population in the optimal layout of a single edge node is:

[0102]

[0103] In the formula, This represents the deviation of an individual in the population within a single edge node's positive optimal layout. For the equilibrium constant term, For the first Each edge node transmits and modifies the fitness value of an individual in the population. A positive fitness value is set for the actual transmission and distribution of each edge node. This represents the number of gene populations in the optimal forward layout of a single edge node.

[0104] The migration rate expression for the population in the positive optimal layout of a single edge node is:

[0105]

[0106] In the formula, For the first The migration rate of each individual population at each edge node is used for transmission and matching. The threshold value for population migration is denoted as , where The meaning is that when the deviation of the gene population in the positive optimal layout of a single edge node is less than the boundary threshold of population migration, all fitness values ​​of the individuals in the population that are used for transmission and matching at each edge node are obtained. By judging the probability of the fitness of a single individual in the whole, the migration rate of a single individual is determined. Otherwise, no migration is performed.

[0107] The expression for the forward optimization model of the multi-island genetic algorithm is as follows:

[0108]

[0109] In the formula, For the first The edge node transmission and distribution population is in the first edge nodes and the edge nodes The optimized population under the combined effect For the first The edge node about the first The function relating the migration rate of individuals in the population to the migration rate of each edge node is used. For the first Each edge node transmits a set of indicator functions representing the fitness values ​​of individuals in the population. The symbol for convolution. For the first The functional relationship between the migration rate of individuals in the population transmitted by each edge node and the first... The convolution value of the set of fitness values ​​of individuals in the population is used to transmit the variants of each edge node. For the first The edge node about the first The function relating the migration rate of individuals in the population to the migration rate of each edge node is used. For the first Each edge node transmits an indicator function that uses the set of fitness values ​​of individuals in the population.

[0110] It can be explained that, based on the forward optimal layout of a single edge node, the forward optimal layout of each edge node in the forward allocation method of transmission and distribution can be obtained. This is an independent selection process. However, the transmission and distribution sensing data of each edge node are interconnected. Therefore, based on obtaining the forward optimal layout of a single edge node, it is necessary to perform data fusion on the gene populations in the forward optimal layouts of different edge nodes, and re-select the new generation of subpopulations that meet the common state. This scheme evaluates the deviation of the gene populations in the forward optimal layout of a single edge node, selects the boundary threshold that meets the population migration, and determines the migration rate of the populations in the forward optimal layout of a single edge node that meets the boundary threshold of population migration. Thus, by fusing the data of each gene population, a multi-island genetic algorithm forward optimization model is established to obtain the forward optimal layout of the overall transmission and distribution sensing data.

[0111] Reference Figure 4 As shown, the step of establishing a multi-island genetic algorithm reverse optimization model based on the reverse optimal layout of a single edge node to obtain the reverse optimal layout of the overall transmission, distribution, and sensing data specifically includes:

[0112] The power consumption of end users, power distribution network, voltage transformation and power transmission are allocated sequentially to optimize the sensing data of power transmission, distribution and consumption, and this allocation method is marked as reverse allocation.

[0113] Based on the reverse optimal layout of a single edge node and the reverse adaptive function of the transmission and distribution sensing data of each edge node, an adaptive genetic operator is set to constrain the newly added user population individuals;

[0114] Based on the adaptive genetic operator, the migration rate of the population in the reverse optimal layout of a single edge node is dynamically adjusted to ensure that the newborn individuals meet the overall constraints of the transmission, transformation, allocation and utilization.

[0115] Based on the migration rate of the population in the reverse optimal layout of a single edge node, a multi-island genetic algorithm reverse optimization model is established to obtain the reverse optimal layout of the overall transmission, distribution and sensing data.

[0116] The expression for the adaptive genetic operator is:

[0117]

[0118] In the formula, The migration rate of the population in the reverse optimal layout of a single edge node after the adaptive genetic operator is constrained. This represents the minimum migration rate of the population in the reverse optimal layout of a single edge node. This represents the maximum migration rate of the population in the reverse optimal layout of a single edge node. The maximum value of the reverse fitness of the sensing data is used to power the transmission and transformation of each edge node. The sensor data is used to provide inverse adaptive adaptation for each edge node's power transmission and transformation. The minimum value of the reverse fitness of the sensing data used by each edge node for transmission, transformation, and distribution;

[0119] The expression for the multi-island genetic algorithm inverse optimization model is as follows:

[0120]

[0121] In the formula, For the first The edge node transmission and distribution population is in the first edge nodes and the edge nodes The optimized population under the combined effect For the first The edge node about the first The function relating the migration rate of individuals in the population to the migration rate of each edge node is used. For the first Each edge node transmits a set of indicator functions representing the fitness values ​​of individuals in the population. The symbol for convolution. For the first The functional relationship between the migration rate of individuals in the population transmitted by each edge node and the first... The convolution value of the set of fitness values ​​of individuals in the population is used to transmit the variants of each edge node. For the first The edge node about the first The function relating the migration rate of individuals in the population to the migration rate of each edge node is used. For the first Each edge node transmits an indicator function that uses the set of fitness values ​​of individuals in the population.

[0122] This can be explained by the fact that, since the method of sequentially allocating electricity to end users, distribution networks, voltage transformation, and power transmission is centered on maximizing user utility and adjusting the upper-level power grid in reverse, it is necessary to constrain the newly added user population when establishing the multi-island genetic algorithm reverse optimization model. Unlike the forward allocation method of transmission, transformation, distribution, and consumption, it is not possible to directly fuse and screen the gene populations of different edge nodes. It is necessary to consider the constraints of the newly added population, such as voltage and capacity limitations. Therefore, this scheme introduces an adaptive genetic operator to dynamically adjust the migration rate of the population in the reverse optimal layout of a single edge node, and constrains the new individuals to meet the overall constraints of transmission, transformation, distribution, and consumption, thereby establishing a multi-island genetic algorithm reverse optimization model and obtaining the reverse optimal layout of the overall transmission, transformation, distribution, and consumption sensing data.

[0123] Reference Figure 5 As shown, the establishment of the transmission, transformation, distribution, and utilization sensing data security management model, which involves acquiring changes in sensing data at each stage of transmission, transformation, distribution, and utilization, and selecting the optimal layout of sensing data, specifically includes:

[0124] Based on the changes in the sensing data of power transmission and end-user electricity consumption, determine the degree of change in the sensing data at both ends of the transmission, transformation, distribution and consumption.

[0125] Based on the Pearson correlation coefficient, the weights of the impact of changes in power transmission and end-user electricity consumption on the entire transmission, transformation, distribution and utilization operation are obtained.

[0126] Based on the forward optimization model and the backward optimization model of the multi-island genetic algorithm, and combined with the degree of change and influence weight of the sensing data at both ends of the transmission, transformation, distribution and utilization, a safety management model for the sensing data of the transmission, transformation, distribution and utilization is established.

[0127] Based on the data security management model for transmission, transformation, distribution and utilization sensing data, the changes in sensing data in each link of transmission, transformation, distribution and utilization are obtained, and the optimal layout of sensing data for transmission, transformation, distribution and utilization is selected.

[0128] The expression for the degree of change in the sensing data at both ends of the transmission and distribution system is:

[0129]

[0130] In the formula, To measure the degree of change in sensing data at both ends of the transmission, distribution, and utilization system. The total amount detected by sensing data at both ends of the transmission and distribution system. The original amount of sensing data at both ends of the transmission and distribution system;

[0131] The expression for the data security management model of transmission, transformation, distribution and utilization sensing is:

[0132]

[0133] In the formula, To sense the degree of data change at the power transmission end, The weighting of the impact of changes in sensing data at the power transmission end on the overall operation of power transmission, transformation, distribution, and consumption. Weight the impact of changes in end-user perceived data on the overall operation of transmission, transformation, distribution, and utilization. To enable end users to perceive the degree of data change.

[0134] It can be explained that the forward optimization model and the backward optimization model of the multi-island genetic algorithm can optimize the allocation of transmission, transformation, distribution and utilization sensing data from two aspects. However, a decision still needs to be made on which one to choose. This solution establishes a security management model for transmission, transformation, distribution and utilization sensing data. By analyzing the degree of change in sensing data of power transmission and end-user electricity consumption and the weight of its impact on the overall operation of transmission, transformation, distribution and utilization, the most suitable unidirectional optimal allocation is automatically selected through comparison. This reduces human intervention, intelligently selects the optimal allocation mode, and further improves the efficiency of transmission, transformation, distribution and utilization allocation.

[0135] Furthermore, based on the same inventive concept as the aforementioned IoT transmission, transformation, distribution, and sensing data security management method, this solution proposes an IoT transmission, transformation, distribution, and sensing data security management system, comprising:

[0136] The edge node module is used to establish edge nodes according to each link of transmission, transformation, distribution and utilization, and to receive and analyze the sensing data of transmission, transformation, distribution and utilization through each edge node;

[0137] The single-point optimization module is used to optimize the perception data of each edge node based on a genetic algorithm to obtain the forward-inverse optimal layout of a single edge node.

[0138] The overall optimal module is used to establish a multi-island genetic algorithm forward optimization model based on the forward optimal layout of a single edge node to obtain the forward optimal layout of the overall transmission, transformation, distribution and utilization sensing data; to establish a multi-island genetic algorithm reverse optimization model based on the reverse optimal layout of a single edge node to obtain the reverse optimal layout of the overall transmission, transformation, distribution and utilization sensing data; and to establish a transmission, transformation, distribution and utilization sensing data security management model, which selects the optimal layout of the transmission, transformation, distribution and utilization sensing data by acquiring the changes in sensing data in each link of transmission, transformation, distribution and utilization.

[0139] The management platform module is used to establish a data security management platform for transmission, transformation, distribution and utilization sensing based on Internet of Things technology. It is used to receive, store and display the sensing data information and optimal layout of each transmission, transformation and distribution unit.

[0140] The overall optimal modules include:

[0141] A forward optimization unit is used to establish a multi-island genetic algorithm forward optimization model based on the forward optimal layout of a single edge node, and obtain the forward optimal layout of the overall transmission, transformation, distribution and sensing data.

[0142] The reverse optimization unit is used to establish a multi-island genetic algorithm reverse optimization model based on the reverse optimal layout of a single edge node, and obtain the reverse optimal layout of the overall transmission, transformation, distribution and sensing data.

[0143] The integrated optimization unit is used to establish a data security management model for transmission, transformation, distribution and utilization sensing data. By acquiring the changes in sensing data in each link of transmission, transformation, distribution and utilization, the optimal layout of transmission, transformation, distribution and utilization sensing data is selected.

[0144] In summary, the advantages of this invention are: to effectively improve the efficient allocation of power transmission and distribution, and to ensure the safe operation of power transmission and distribution.

[0145] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for secure management of IoT transmission, transformation, distribution, and sensing data, characterized in that, include: Based on each link of transmission, transformation, distribution and utilization, edge nodes are established respectively, and the sensing data of transmission, transformation, distribution and utilization are received and analyzed through each edge node; Based on the genetic algorithm, the perception data of each edge node is optimized to obtain the optimal forward-inverse layout of a single edge node; Based on the forward optimal layout of a single edge node, a multi-island genetic algorithm forward optimization model is established to obtain the forward optimal layout of the overall transmission, transformation, distribution and sensing data; Based on the reverse optimal layout of a single edge node, a multi-island genetic algorithm reverse optimization model is established to obtain the reverse optimal layout of the overall transmission, transformation, distribution and sensing data. Establish a data security management model for transmission, transformation, distribution and utilization sensing data. By acquiring changes in sensing data at each stage of transmission, transformation, distribution and utilization, the optimal layout of sensing data for transmission, transformation, distribution and utilization can be selected. Based on Internet of Things (IoT) technology, a data security management platform for transmission, transformation, distribution and utilization sensing data is established to receive, store and display the sensing data information and optimal layout of each transmission, transformation and distribution system. The optimal forward-reverse layout of a single edge node refers to setting forward and reverse adaptive functions for the sensing data of each edge node based on the application scenario of transmission, transformation, and distribution, and establishing a distribution method for transmission, transformation, and distribution in both forward and reverse aspects through different adaptive functions; The positive optimal layout of the overall transmission, transformation, distribution and sensing data refers to establishing a multi-island genetic algorithm positive optimization model based on the migration rate of the population in the positive optimal layout of a single edge node, and obtaining the positive optimal layout of the overall transmission, transformation, distribution and sensing data. The reverse optimal layout of the overall transmission, transformation, distribution, and sensing data refers to establishing a multi-island genetic algorithm reverse optimization model based on the migration rate of the population in the reverse optimal layout of a single edge node, and obtaining the reverse optimal layout of the overall transmission, transformation, distribution, and sensing data.

2. The IoT transmission, transformation, distribution, and sensing data security management method according to claim 1, characterized in that, The step of establishing edge nodes for each link of transmission, transformation, distribution and utilization, and receiving and analyzing the sensing data of transmission, transformation, distribution and utilization through each edge node specifically includes: Based on the various links of power transmission, transformation, distribution and consumption, each link is set as an edge node for sensing data. The links of power transmission, transformation, distribution and consumption include: power transmission, voltage transformation, power distribution network and end-user power consumption. Based on each edge node, a corresponding data acquisition device for transmission, transformation, distribution and utilization sensing is set up to acquire and save the data collected by each edge node.

3. The IoT transmission, transformation, distribution, and sensing data security management method according to claim 2, characterized in that, The process of optimizing the perception data of each edge node based on a genetic algorithm to obtain the optimal forward-inverse layout of a single edge node specifically includes: Based on the transmission, transformation, distribution and utilization sensing data collected by each edge node, establish a transmission, transformation, distribution and utilization sensing dataset for each edge node; Based on the Kalman filter algorithm, the data in the sensing dataset of each edge node's transmission, transformation, distribution and utilization are filtered and denoised. The data in the sensor dataset of each edge node after filtering are normalized to eliminate the influence of data units; Based on the application scenarios of transmission, transformation, distribution and utilization, the forward adaptive function and the reverse adaptive function of the sensing data of each edge node transmission, transformation, distribution and utilization are set respectively; Based on the genetic algorithm, the perception data of each edge node is optimized to obtain the forward-inverse optimal layout of a single edge node.

4. The IoT transmission, transformation, distribution, and sensing data security management method according to claim 3, characterized in that, The step of establishing a multi-island genetic algorithm forward optimization model based on the forward optimal layout of a single edge node to obtain the forward optimal layout of the overall transmission, distribution, and sensing data specifically includes: The power transmission, distribution and consumption sensing data are optimized by adopting a method of sequential allocation of power transmission, voltage transformation, power distribution network and end user power consumption, and this allocation method is marked as positive allocation; Based on the positive optimal layout of a single edge node, and combined with the actual transmission and distribution of each edge node, the positive fitness value is set to determine the deviation of the gene population in the positive optimal layout of a single edge node. Based on the deviation of the gene population in the positive optimal layout of a single edge node, and using big data, a boundary threshold for population migration is set. The migration rate of the population in the forward optimal layout of a single edge node is determined based on the deviation of the gene population and the boundary threshold of population migration. Based on the migration rate of the population in the forward optimal layout of a single edge node, a multi-island genetic algorithm forward optimization model is established to obtain the forward optimal layout of the overall transmission, distribution and sensing data. The expression for the deviation of the gene population in the optimal layout of a single edge node is: ； In the formula, This represents the deviation of an individual in the population within a single edge node's positive optimal layout. For the equilibrium constant term, For the first Each edge node transmits and modifies the fitness value of an individual in the population. A positive fitness value is set for the actual transmission and distribution of each edge node. This represents the number of gene populations in the optimal forward layout of a single edge node. The migration rate expression for the population in the positive optimal layout of a single edge node is: ； In the formula, For the first The migration rate of each individual population at each edge node is used for transmission and matching. The boundary threshold for population migration is denoted as , where The meaning is that when the deviation of the gene population in the positive optimal layout of a single edge node is less than the boundary threshold of population migration, all fitness values ​​of the individuals in the population that are used for transmission and matching at each edge node are obtained. By judging the probability of the fitness of a single individual in the whole, the migration rate of a single individual is determined. Otherwise, no migration is performed. The expression for the forward optimization model of the multi-island genetic algorithm is as follows: ； In the formula, For the first The edge node transmission and distribution population is in the first edge nodes and the edge nodes The optimized population under the combined effect For the first The edge node about the first The function relating the migration rate of individuals in the population to the migration rate of each edge node is used. For the first Each edge node transmits a set of indicator functions representing the fitness values ​​of individuals in the population. The symbol for convolution. For the first The functional relationship between the migration rate of individuals in the population transmitted by each edge node and the first... The convolution value of the set of fitness values ​​of individuals in the population is used to transmit the variants of each edge node. For the first The edge node about the first The function relating the migration rate of individuals in the population to the migration rate of each edge node is used. For the first Each edge node transmits an indicator function that uses the set of fitness values ​​of individuals in the population.

5. The IoT transmission, transformation, distribution, and sensing data security management method according to claim 4, characterized in that, The step of establishing a multi-island genetic algorithm reverse optimization model based on the reverse optimal layout of a single edge node to obtain the reverse optimal layout of the overall transmission, distribution, and sensing data specifically includes: The power consumption of end users, power distribution network, voltage transformation and power transmission are allocated sequentially to optimize the sensing data of power transmission, distribution and consumption, and this allocation method is marked as reverse allocation. Based on the reverse optimal layout of a single edge node and the reverse adaptive function of the transmission and distribution sensing data of each edge node, an adaptive genetic operator is set to constrain the newly added user population individuals; Based on the adaptive genetic operator, the migration rate of the population in the reverse optimal layout of a single edge node is dynamically adjusted to ensure that the newborn individuals meet the overall constraints of the transmission, transformation, allocation and utilization. Based on the migration rate of the population in the reverse optimal layout of a single edge node, a multi-island genetic algorithm reverse optimization model is established to obtain the reverse optimal layout of the overall transmission, distribution and sensing data. The expression for the adaptive genetic operator is: ； In the formula, The migration rate of the population in the reverse optimal layout of a single edge node after the adaptive genetic operator is constrained. This represents the minimum migration rate of the population in the reverse optimal layout of a single edge node. This represents the maximum migration rate of the population in the reverse optimal layout of a single edge node. The maximum value of the reverse fitness of the sensing data is used to power the transmission and transformation of each edge node. The sensor data is used to provide inverse adaptive adaptation for each edge node's power transmission and transformation. The minimum value of the reverse fitness of the sensing data used by each edge node for transmission, transformation, and distribution; The expression for the multi-island genetic algorithm inverse optimization model is as follows: ； In the formula, For the first The edge node transmission and distribution population is in the first edge nodes and the edge nodes The optimized population under the combined effect For the first The edge node about the first The function relating the migration rate of individuals in the population to the migration rate of each edge node is used. For the first Each edge node transmits a set of indicator functions representing the fitness values ​​of individuals in the population. The symbol for convolution. For the first The functional relationship between the migration rate of individuals in the population transmitted by each edge node and the first... The convolution value of the set of fitness values ​​of individuals in the population is used to transmit the variants of each edge node. For the first The edge node about the first The function relating the migration rate of individuals in the population to the migration rate of each edge node is used. For the first Each edge node transmits an indicator function that uses the set of fitness values ​​of individuals in the population.

6. The IoT transmission, transformation, distribution, and sensing data security management method according to claim 5, characterized in that, The establishment of the data security management model for transmission, transformation, distribution and utilization sensing data, which involves acquiring changes in sensing data at each stage of transmission, transformation, distribution and utilization, and selecting the optimal layout of sensing data for transmission, transformation, distribution and utilization, specifically includes: Based on the changes in the sensing data of power transmission and end-user electricity consumption, determine the degree of change in the sensing data at both ends of the transmission, transformation, distribution and consumption. Based on the Pearson correlation coefficient, the weights of the impact of changes in power transmission and end-user electricity consumption on the entire transmission, transformation, distribution and utilization operation are obtained. Based on the forward optimization model and the backward optimization model of the multi-island genetic algorithm, and combined with the degree of change and influence weight of the sensing data at both ends of the transmission, transformation, distribution and utilization, a safety management model for the sensing data of the transmission, transformation, distribution and utilization is established. Based on the data security management model for transmission, transformation, distribution and utilization sensing data, the changes in sensing data in each link of transmission, transformation, distribution and utilization are obtained, and the optimal layout of sensing data for transmission, transformation, distribution and utilization is selected. The expression for the degree of change in the sensing data at both ends of the transmission and distribution system is: ； In the formula, To measure the degree of change in sensing data at both ends of the transmission, distribution, and utilization system. The total amount detected by sensing data at both ends of the transmission and distribution system. The original amount of sensing data at both ends of the transmission and distribution system; The expression for the data security management model of transmission, transformation, distribution and utilization sensing is: ； In the formula, To sense the degree of data change at the power transmission end, The weighting of the impact of changes in sensing data at the power transmission end on the overall operation of power transmission, transformation, distribution, and consumption. Weight the impact of changes in end-user perceived data on the overall operation of transmission, transformation, distribution, and utilization. To enable end users to perceive the degree of data change.

7. The IoT transmission, transformation, distribution, and sensing data security management method according to claim 6, characterized in that, The aforementioned data security management platform for transmission, transformation, distribution, and utilization sensing, based on Internet of Things (IoT) technology, specifically includes the following: Based on IoT technology, it receives and collects sensing data from various edge nodes for power transmission, distribution, and use. Establish a data security management platform for transmission, transformation, distribution and utilization sensing data to build the operating environment for the data security management model for transmission, transformation, distribution and utilization sensing data and ensure the normal operation of the program; Based on the data security management platform for transmission, transformation, distribution and utilization sensing, it receives, stores and displays the sensing data information and optimal layout of each transmission, transformation and utilization system.

8. A data security management system for Internet of Things (IoT) transmission, transformation, distribution, and sensing, characterized in that: The method for implementing the IoT transmission, transformation, distribution, and sensing data security management method as described in any one of claims 1-7 includes: The edge node module is used to establish edge nodes according to each link of transmission, transformation, distribution and utilization, and to receive and analyze the sensing data of transmission, transformation, distribution and utilization through each edge node; The single-point optimization module is used to optimize the perception data of each edge node based on a genetic algorithm to obtain the forward-inverse optimal layout of a single edge node. The overall optimal module is used to establish a multi-island genetic algorithm forward optimization model based on the forward optimal layout of a single edge node to obtain the forward optimal layout of the overall transmission, transformation, distribution and utilization sensing data; to establish a multi-island genetic algorithm reverse optimization model based on the reverse optimal layout of a single edge node to obtain the reverse optimal layout of the overall transmission, transformation, distribution and utilization sensing data; and to establish a transmission, transformation, distribution and utilization sensing data security management model, which selects the optimal layout of transmission, transformation, distribution and utilization sensing data by acquiring the changes in sensing data in each link of transmission, transformation, distribution and utilization. The management platform module is used to establish a data security management platform for transmission, transformation, distribution and utilization sensing based on Internet of Things (IoT) technology. It is used to receive, store and display the sensing data information and optimal layout of each transmission, transformation and distribution unit.

9. The Internet of Things (IoT) transmission, transformation, distribution, and sensing data security management system according to claim 8, characterized in that, The overall optimal module includes: A forward optimization unit is used to establish a multi-island genetic algorithm forward optimization model based on the forward optimal layout of a single edge node, and obtain the forward optimal layout of the overall transmission, transformation, distribution and sensing data. The reverse optimization unit is used to establish a multi-island genetic algorithm reverse optimization model based on the reverse optimal layout of a single edge node, and obtain the reverse optimal layout of the overall transmission, transformation, distribution and sensing data. The integrated optimization unit is used to establish a data security management model for transmission, transformation, distribution and utilization sensing data. By acquiring the changes in sensing data in each link of transmission, transformation, distribution and utilization, the optimal layout of transmission, transformation, distribution and utilization sensing data is selected.

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