A household photovoltaic multi-path inspection data processing method and system

Abstract

The present invention discloses a household photovoltaic multi-path inspection data processing method and system. The method includes: obtaining the longitude and latitude of a household photovoltaic user, building a household photovoltaic multi-path network node model to obtain the total number of household photovoltaic nodes; according to the total number of household photovoltaic nodes, using the crayfish optimization algorithm to calculate the optimal path for collecting node data; performing encryption operations on the data of each node on the optimal path according to the preset wapiti-bird flock algorithm, and processing the encrypted data of each node using the multi-objective heterogeneous processing rule to obtain the final processing result. It reduces the energy consumption of household photovoltaic multi-path data collection and improves the energy efficiency of household photovoltaic multi-path data collection.

Description

Technical Field

[0001] The present invention belongs to the technical field of household photovoltaic, and particularly relates to a method and system for processing household photovoltaic multi-path inspection data. Background Art

[0002] The trend of small-scale decentralized power generation in the distribution system is increasing. By installing generators in their own buildings, end-users can participate more actively.

[0003] The healthy operation of household photovoltaic depends on regular inspection and maintenance, which is a prerequisite for the efficient power generation of the photovoltaic system. In urban areas, due to complex road conditions, high and concentrated population density, large energy consumption during data transmission, and low frequency of massive data processing, how to reduce energy consumption and efficiently process massive data during the transmission of multi-path information between photovoltaic users and inspectors is a problem. Therefore, a new data processing method is needed to solve this problem. Summary of the Invention

[0004] In view of the above problems, the present invention proposes an improved method for processing household photovoltaic multi-path inspection data, aiming to improve the energy efficiency of household photovoltaic multi-path data collection, reduce the energy consumption of household photovoltaic multi-path data collection, increase the frequency of massive data processing, and have a better feedback effect on users.

[0005] In a first aspect, the present invention provides a method for processing household photovoltaic multi-path inspection data, including:

[0006] Obtaining the longitude and latitude of household photovoltaic users, building a household photovoltaic multi-path network node model, and obtaining the total number of household photovoltaic nodes;

[0007] According to the total number of household photovoltaic nodes, using the crayfish optimization algorithm to calculate the optimal path for collecting node data, where the expression of the optimal path is:

[0008] ,

[0009] ,

[0010] In the formula, is the distance when the household photovoltaic multi-path network sequentially collects all photovoltaic user node data and returns to the starting node, is the minimum distance in path planning, is the maximum planned distance in path planning, is the th path node of the photovoltaic user, is the th photovoltaic user;

[0011] Encrypt the data of each node on the optimal path according to the preset elk-bird flock algorithm, and process the encrypted data of each node using the multi-objective heterogeneous processing rule to obtain the final processing result.

[0012] In a second aspect, the present invention provides a household photovoltaic multi-path inspection data processing system, including:

[0013] A construction module configured to obtain the longitude and latitude of a household photovoltaic user, build a household photovoltaic multi-path network node model, and obtain the total number of household photovoltaic nodes;

[0014] A calculation module configured to calculate the optimal path for collecting node data according to the total number of household photovoltaic nodes using the crayfish optimization algorithm, where the expression of the optimal path is:

[0015] ,

[0016] ,

[0017] In the formula, is the distance when the household photovoltaic multi-path network sequentially collects the data of all photovoltaic user nodes and returns to the starting node, is the minimum distance in path planning, is the maximum planned distance in path planning, is the th path node of the photovoltaic user, is the th photovoltaic user;

[0018] A processing module configured to encrypt the data of each node on the optimal path according to the preset elk-bird flock algorithm, and process the encrypted data of each node using the multi-objective heterogeneous processing rule to obtain the final processing result.

[0019] In a third aspect, there is provided an electronic device, which includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of the household photovoltaic multi-path inspection data processing method of any embodiment of the present invention.

[0020] In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program instructions are executed by a processor, the processor is enabled to execute the steps of the household photovoltaic multi-path inspection data processing method of any embodiment of the present invention.

[0021] The household photovoltaic multi-path inspection data processing method and system of the present application study the use of a dynamic vortex search algorithm to reduce the energy consumption of data transmission, study the establishment of a household photovoltaic multi-path network node model to obtain the total number of household photovoltaic nodes, study the calculation of the optimal path for collecting node data through a crayfish optimization algorithm, study the realization of privacy protection of node data using a red deer-bird flock algorithm, and study the use of multi-objective heterogeneous cloud processing to aggregate the massive data at the nodes, reducing the energy consumption of household photovoltaic multi-path data collection, improving the energy efficiency of household photovoltaic multi-path data collection, protecting the privacy during the data processing, reducing the latency in the network, increasing the frequency of processing massive data, and having a better feedback effect on users. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a flowchart of a household photovoltaic multi-path inspection data processing method provided by an embodiment of the present invention;

[0024] Figure 2 It is a structural block diagram of a household photovoltaic multi-path inspection data processing system provided by an embodiment of the present invention;

[0025] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0027] Please refer to Figure 1 , which shows a flowchart of a household photovoltaic multi-path inspection data processing method of the present application.

[0028] As Figure 1 shown, the household photovoltaic multi-path inspection data processing method specifically includes the following steps:

[0029] Step S101: Obtain the longitude and latitude of the household PV user, build a multi-path network node model for household PV, and obtain the total number of household PV nodes.

[0030] In this step, assume that the multi-path network node model for household PV has Z layers, and it is a fork structure. is the layer of the multi-path network for household PV. The total number of household PV nodes in the multi-path network for household PV is:

[0031] ,

[0032] Since the tree in the first layer is , we get , is the layer's tree, that is, the expression for the total number of household PV nodes in the multi-path network for household PV is:

[0033] ,

[0034] After transformation, the total number of household PV nodes is:

[0035] ,

[0036] In the formula, is the in the first layer.

[0037] Step S102: According to the total number of household PV nodes, use the crayfish optimization algorithm to calculate the optimal path for collecting node data.

[0038] In this step, obtain the node data and transmit the node data according to the dynamic vortex search algorithm. Specifically:

[0039] Initialize the parameters: the number of candidate solutions , the search dimension , the number of iterations , and set the initial value of the global optimal value to infinity. The expression is:

[0040] ,

[0041] ,

[0042] ,

[0043] ,

[0044] ,

[0045] ,

[0046] In the formula, is the initial center point position, and are the upper and lower bounds of each dimension of the search space respectively, is the calculation radius factor, and are the upper and lower bounds of each dimension of the search space of the th PV user respectively, is the generated vortex radius, is the inverse incomplete function, is the upper limit of integration, is the adaptive factor with the number of iterations, is the candidate solution, is the updated solution, is the current number of iterations, is the center point, is the random number generated from the standard normal distribution, is the random number of the normal distribution with a multiplication sign, is an updated candidate solution;

[0047] Calculate the fitness value of each candidate solution according to the objective function, and select the optimal candidate solution , is the first candidate solution, is the second candidate solution, is the Nth candidate solution;

[0048] If the maximum number of iterations is satisfied, output the result.

[0049] The energy consumption consumed when the node data is transmitted is:

[0050] ,

[0051] ,

[0052] In the formula, is the energy consumption consumed when transmitting and data packets, is the duration required to transmit and data packets together, is the number of data packets transmitted, is the size of the transmitted data packet, For the speed of data transmission, For transmission and the amount of data packets, Indicates a data packet, Indicates an acknowledgment character.

[0053] The change in temperature will affect the behavior of crayfish, causing crayfish to enter different stages. The temperature definition calculation formula is:

[0054] ,

[0055] In the formula, Is the temperature of the environment where the crayfish is located, Is random data;

[0056] When the crayfish is in the temperature range of 20°C to 35°C, the mathematical model of the crayfish intake, the expression is:

[0057] ,

[0058] In the formula, Is the crayfish intake, Is the temperature most suitable for the growth of crayfish, , Are the standard deviation and the constant of the intake at different temperatures respectively;

[0059] The expression for the crayfish in the heat avoidance stage is:

[0060] ,

[0061] ,

[0062] ,

[0063] ,

[0064] In the formula, Is the food position, Is the size of the food, Is the food factor, Is the Fitness value of the nth crayfish, Is the fitness value of the food position, Is the optimal position obtained so far for the number of iterations, Is to represent an individual At Position in the Is the position of a random individual in Position in the Indicates the updated food position.

[0065] Determine the optimal path planning of the crayfish optimization algorithm, where the expression of the optimal path is:

[0066] ,

[0067] ,

[0068] In the formula, is the distance when the household photovoltaic multi-path network collects the data of all photovoltaic user nodes in sequence and returns to the starting node. is the minimum distance in the path planning. is the maximum planned distance in the path planning. is the th path node of the photovoltaic user. is the th photovoltaic user.

[0069] In a specific embodiment, three different algorithms are compared as shown in Table 1, and the proposed mathematical algorithm is verified by Matlab numerical simulation.

[0070] ,

[0071] The comparison results of energy consumption are shown in Table 2 and the data processing frequency results are shown in Table 3.

[0072] ,

[0073] As can be seen from Table 2, after adopting the DAB algorithm, the energy consumption when the number of nodes is 3 - 45 is smaller than that of the DA algorithm and the DB algorithm, and it is more energy-efficient. The more obvious the energy-saving effect is when the number of nodes is more. Until the number of nodes is more than 27, the reduction of energy consumption is not very obvious. The comparison results prove the superiority of the improved household photovoltaic multi-path inspection data processing method proposed by the present invention.

[0074] ,

[0075] As can be seen from the table, compared with DA, DAB has a higher system processing frequency and a shorter response time, and has obvious advantages in processing massive data. When the data volume is 556GB, DAB can increase the system frequency to more than 7GHZ in 0.06S. DA, the traditional system, only needs 0.4S to respond, and the processing frequency is about 3.273GHZ. The designed system processing frequency is more than 50% higher than that of the traditional system, solving the problem of low processing frequency caused by the weak analysis ability and slow response speed of the traditional system.

[0076] Step S103, encrypt the data of each node on the optimal path according to the preset wapiti-bird flock algorithm, and process the encrypted data of each node using the multi-objective heterogeneous processing rule to obtain the final processing result.

[0077] In this step, initialize the solution: Initialize the solution with random numbers;

[0078] Fitness evaluation: After initializing the solution, for optimal key generation, regard the objective as the minimum function, and consider the solution with the minimum fitness as the optimal solution. The expression is:

[0079] ,

[0080] In the formula, , , are all weight constants, and the values of the weight constants are = 0.1, = 0.3, = 0.3, is the degree of modification between the original data and the processed data, is the rate of data hiding, is the rate of information preservation;

[0081] Position update: If , is the flight behavior frequency, is a random number generated between 0 and 1, is the foraging probability. In this case, the position of the bird flock is updated according to the position of the food source. The expression is:

[0082] ,

[0083] In the formula, is the flight behavior frequency, is the foraging probability, is the position of individual in the dimension in the next iteration, is the position of individual in the dimension in this iteration, and are the best positions of the wapiti and the bird flock respectively before, and are both updated normal constants.

[0084] If , in this case, the existing bird flock performs a vigilant behavior. However, the situation depends on a random solution. Therefore, the vigilant behavior of the bird flock is updated using the update specified in wapiti, and the expression is:

[0085] ,

[0086] In the formula, and are the upper and lower limits of the solution respectively, is the difference between the previous best position of wapiti and the position before the update, is the difference between the previous best position of the bird flock and the position before the update;

[0087] If , each solution is a producer, then this update is performed, and the behavior of the producer, the expression is:

[0088] ,

[0089] If , in this case, the behavior of the predator is considered, and the expression is:

[0090] ,

[0091] In the formula, is the flight length of the bird, is the position of individual in the dimension during this iteration;

[0092] Data cleaning model, the expression is:

[0093] ,

[0094] In the formula, is the processed data stored in the data cloud, is the optimal solution similarly used in data processing, is the optimal solution of the wapiti-bird flock algorithm;

[0095] Data recovery model, the expression is:

[0096] ,

[0097] In the formula, is the recovered data, is the product of functions.

[0098] It should be noted that starting from the starting node, the parent nodes are calculated in sequence, and each parent node aggregates the data of all child nodes in each step. Among them, the aggregation expression is:

[0099] ,

[0100] wherein, represents the per-node embedding of data from to all reachable nodes, is the per-node embedding of data from to all reachable nodes, and represent two non-linear transformation functions, is the eigenvector of node ;

[0101] is the length from the node to the exit node, is the length from the task to the entry root node, reflecting the position of the node in the work, and the calculation formula is:

[0102] ,

[0103] wherein, is the average speed of the actuator, is the child node, is the task of node i, is the size of the converted data, is the data size of the current task, is the path node of the th photovoltaic user, is the path node of the th photovoltaic user;

[0104] ,

[0105] wherein, is the average size of the converted data, is the set of parent nodes, is the task of node , is the data carried from the entrance to the node;

[0106] Each node is scored through the scoring function . After obtaining the score of the node, the multi-objective heterogeneous cloud selects multiple node data for processing according to the score. The calculation formula is:

[0107] ,

[0108] wherein, is the selection formula of , is the score of the node at , is the score of the random node position;

[0109] Process the data with high scores in sequence. When the support vector lies on the hyperplane, establish a frequency processing equation formula, and process each encrypted node data according to the frequency processing equation formula to obtain the final processing result. The frequency processing equation formula is specifically as follows:

[0110] ,

[0111] In the formula, is the error correction coefficient, is the data frequency limit value function, is the harmonic control coefficient, is for transmission the number of data packets, is the th PV user, is the number of iterations in the previous generation;

[0112] In summary, for the method of this application, first, study the use of the dynamic vortex search algorithm to reduce the energy consumption of data transmission, study the establishment of a household PV multi-path network node model to obtain the total number of household PV nodes, study the calculation of the optimal path for collecting node data through the crayfish optimization algorithm, study the use of the elk-bird flock algorithm to achieve privacy protection of node data, and study the use of multi-objective heterogeneous cloud processing to aggregate the massive data at the nodes, reducing the energy consumption of household PV multi-path data collection, improving the energy efficiency of household PV multi-path data collection, protecting the privacy during the data processing, reducing the latency in the network, increasing the frequency of processing massive data, and having a better feedback effect on users.

[0113] Please refer to Figure 2 , which shows the structural block diagram of a household PV multi-path inspection data processing system of this application.

[0114] As Figure 2 shown, the household PV multi-path inspection data processing system 200 includes a construction module 210, a calculation module 220, and a processing module 230.

[0115] Among them, the construction module 210 is configured to obtain the longitude and latitude of the household PV user, build a household PV multi-path network node model, and obtain the total number of household PV nodes;

[0116] The calculation module 220 is configured to calculate the optimal path for collecting node data according to the total number of household PV nodes by using the crayfish optimization algorithm, where the expression of the optimal path is:

[0117] ,

[0118] ,

[0119] In the formula, is the distance when the household photovoltaic multi-path network sequentially collects data of all photovoltaic user nodes and returns to the starting node. is the minimum distance in path planning. is the maximum planned distance in path planning. is the th path node of the photovoltaic user. is the th photovoltaic user.

[0120] The processing module 230 is configured to encrypt each node data on the optimal path according to the preset elk-bird swarm algorithm, and process each encrypted node data by using the multi-object heterogeneous processing rule to obtain the final processing result.

[0121] It should be understood that Figure 2 The various modules described in Figure 1 correspond to the respective steps in the method described in reference Figure 2 . Therefore, the operations, features and corresponding technical effects described above for the method also apply to

[0122] In some other embodiments, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the program instructions are executed by a processor, the processor is enabled to execute the household photovoltaic multi-path inspection data processing method in any of the above method embodiments;

[0123] As an implementation manner, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are set as:

[0124] Obtain the longitude and latitude of household photovoltaic users, build a household photovoltaic multi-path network node model, and obtain the total number of household photovoltaic nodes;

[0125] According to the total number of household photovoltaic nodes, use the crayfish optimization algorithm to calculate the optimal path for collecting node data, where the expression of the optimal path is:

[0126] ,

[0127] ,

[0128] In the formula, is the distance when the household photovoltaic multi-path network sequentially collects data of all photovoltaic user nodes and returns to the starting node. is the minimum distance in path planning. is the maximum planned distance in path planning. is the path node of the th photovoltaic user, is the th photovoltaic user;

[0129] Encrypt the data of each node on the optimal path according to the preset elk - bird flock algorithm, and process the encrypted data of each node using the multi - objective heterogeneous processing rule to obtain the final processing result.

[0130] A computer - readable storage medium may include a storage program area and a storage data area. Among them, the storage program area can store an operating system and application programs required for at least one function; the storage data area can store data created according to the use of the household photovoltaic multi - path inspection data processing system, etc. In addition, the computer - readable storage medium may include high - speed random - access memory, and may also include memories, such as at least one magnetic disk storage device, a flash memory device, or other non - volatile solid - state storage devices. In some embodiments, the computer - readable storage medium may optionally include a memory remotely set relative to the processor, and these remote memories can be connected to the household photovoltaic multi - path inspection data processing system through a network. Examples of the above - mentioned network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and their combinations.

[0131] Figure 3 is a schematic structural diagram of the electronic device provided by an embodiment of the present invention. As Figure 3 shown, the device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 can be connected through a bus or other means, Figure 3 taking the connection through the bus as an example. The memory 320 is the above - mentioned computer - readable storage medium. The processor 310 executes various functional applications and data processing of the server by running non - volatile software programs, instructions, and modules stored in the memory 320, that is, implementing the household photovoltaic multi - path inspection data processing method in the above - mentioned method embodiment. The input device 330 can receive input digital or character information, and generate key signal inputs related to the user settings and function controls of the household photovoltaic multi - path inspection data processing system. The output device 340 may include a display device such as a display screen.

[0132] The above - mentioned electronic device can execute the method provided by the embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be referred to the method provided by the embodiment of the present invention.

[0133] As an implementation manner, the above-mentioned electronic device is applied to a household photovoltaic multi-path inspection data processing system and is used for a client, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0134] Obtain the longitude and latitude of a household photovoltaic user, build a household photovoltaic multi-path network node model, and obtain the total number of household photovoltaic nodes;

[0135] According to the total number of household photovoltaic nodes, use the crayfish optimization algorithm to calculate the optimal path for collecting node data, where the expression of the optimal path is:

[0136] ,

[0137] ,

[0138] In the formula, is the distance when the household photovoltaic multi-path network sequentially collects the data of all photovoltaic user nodes and returns to the starting node, is the minimum distance in path planning, is the maximum planned distance in path planning, is the th path node of a photovoltaic user, is the th photovoltaic user;

[0139] Perform an encryption operation on the data of each node on the optimal path according to the preset elk-bird flock algorithm, and process the encrypted data of each node using the multi-object heterogeneous processing rule to obtain the final processing result.

[0140] Through the description of the above implementation manners, those skilled in the art can clearly understand that each implementation manner can be realized by means of software plus a necessary general hardware platform, and of course, it can also be realized by hardware. Based on such an understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiments.

[0141] 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 foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A household photovoltaic multi-path inspection data processing method, characterized in that: include: Obtain the longitude and latitude of household photovoltaic users, build a household photovoltaic multi-path network node model, and obtain the total number of household photovoltaic nodes; According to the total number of household photovoltaic nodes, the crayfish optimization algorithm is used to calculate the optimal path for collecting node data, wherein the expression of the optimal path is: ， ， In the formula, The distance when the household photovoltaic multi-path network collects the data of all photovoltaic user nodes in sequence and returns to the starting node, For the Path nodes of PV users, For the PV users; The encrypting operation is performed on each node data on the optimal path according to the preset red deer-bird swarm algorithm, and the encrypted data of each node is processed by adopting a multi-objective heterogeneous processing rule to obtain a final processing result, wherein the encrypting operation is performed on each node data on the optimal path according to the preset red deer-bird swarm algorithm includes: Initialize solution: Initialize the solution with random numbers; Fitness evaluation: After the initialization solution, for the optimal key generation, the target is regarded as the minimum function, and the solution that achieves the minimum fitness is considered to be the optimal solution. The expression is: ， In the formula, , , are weight constants, and the value of the weight constant is =0.1, =0.3, =0.3, is the degree of modification between the original data and the processed data, is the rate of data hiding, The rate at which information is saved; Location Update: If , is the frequency of flight behavior, To generate a random number between 0 and 1, in this case, the position of the flock is updated according to the location of the food source, the expression is: ， In the formula, is the frequency of flight behavior, is the foraging probability, For the individual in the next iteration exist The location of the dimension, is the number of individuals in this iteration exist The location of the dimension, and The best positions in front of the red deer and the flock of birds, and are all updated normal numbers; if , in this case, the existing flock performs vigilance behavior, but the situation depends on the random solution, so the vigilance behavior of the flock is updated using the update specified in the red deer, expressed as: ， In the formula, and are the upper and lower bounds of the solution, respectively. is the difference between the best position of the red deer before and the position before the update, is the difference between the previous best position of the flock and the position before the update; if , each solution is a producer, then this update is performed, and the behavior of the producer is expressed as: ， if , in this case, taking into account the behavior of predators, the expression is: ， In the formula, is the flight length of the bird, is the number of individuals in this iteration exist The location of the dimension; Data cleaning model, the expression is: ， In the formula, For processed data stored in the data cloud, is the optimal solution for similar use in data processing, is the optimal solution of the red deer-bird algorithm; Data recovery model, expressed as: ， In the formula, To recover the data, for Product of functions; The encrypted data of each node is processed by adopting multi-objective heterogeneous processing rules to obtain the final processing results including: The parent nodes are calculated sequentially from the starting node. Each parent node aggregates the data of all child nodes in each step, where the aggregate expression is: ， In the formula, To indicate from per-node embedding of data to all reachable nodes, For per-node embedding of data to all reachable nodes, and represents two nonlinear transformation functions, For Node The eigenvector of is the length from the node to the exit node, It is the length from the task to the root node, reflecting the position of the node in the work, and the calculation formula is: ， In the formula, is the average speed of the actuator, For child nodes, is the task of node i, To convert the size of the data, is the data size of the current task, For the Path nodes of PV users, For the Path nodes of PV users; ， In the formula, is the average size of the converted data, is the parent node set, For Node mission, The data from the entrance to the node; Through the score function Each node is scored. After obtaining the node score, the multi-target heterogeneous cloud selects multiple nodes for data processing according to the score. The calculation formula is: ， In the formula, for The selection formula is For The node's score, is the score of the random node position; The data with high scores are processed in order. When the support vector is on the hyperplane, a frequency processing equation is established. The encrypted data of each node is processed according to the frequency processing equation to obtain the final processing result. The frequency processing equation is as follows: ， In the formula, is the error correction coefficient, is the data frequency limit value function, is the harmonic control coefficient, is the number of packets transmitted, is the number of iterations of the previous generation; Before calculating the optimal path for collecting node data using the crayfish optimization algorithm according to the total number of household photovoltaic nodes, the method further includes: Obtain node data, and transmit the node data according to the dynamic vortex search algorithm to reduce the energy consumption of data transmission. The energy consumption of node data during transmission is: ， ， In the formula, For simultaneous transmission and The energy consumed by the data packet, For transmission and The duration of the data packet. For transmission The number of packets, The size of the transmitted data packet, The speed at which data is transmitted. For transmission and the amount of packets, Indicates a data packet, Indicates confirmation character.

2. A household photovoltaic multi-path inspection data processing method according to claim 1, characterized in that: Obtain node data and transmit the node data according to the dynamic vortex search algorithm, specifically: Initialization parameter: number of candidate solutions , search dimension , number of iterations , set the initial value of the global optimal value to infinity, the expression is: ， ， ， ， ， ， In the formula, is the initial center point position, and are the upper and lower bounds of each dimension of the search space, respectively. To calculate the radius factor, and are the upper and lower bounds of each dimension of the PV user search space, respectively. is the generated vortex radius, is the inverse incomplete function, is the upper limit of points, is an adaptive factor with the number of iterations, For candidate solutions, For an updated solution, is the current iteration number, is the center point, To generate random numbers from a standard normal distribution, is a random number from a normal distribution with a multiplication sign, is a candidate solution for the update; Calculate the fitness value of each candidate solution according to the objective function and select the optimal candidate solution , is the first candidate solution, is the second candidate solution, is the Nth candidate solution; If the maximum number of iterations is met, the result is output.

3. A household photovoltaic multi-path inspection data processing method according to claim 1, characterized in that: The household photovoltaic multi-path network node model is constructed, and the total number of household photovoltaic nodes is obtained, including: Assume that the household photovoltaic multi-path network node model has Z layers, which is Fork structure, It is the first household photovoltaic multi-path network Layer, the total number of household photovoltaic nodes in the household photovoltaic multi-path network for: ， Because the first layer The tree is ,get , For the Layer Tree, that is, the total number of household photovoltaic nodes in the household photovoltaic multi-path network The expression is: ， After deformation, the total number of household photovoltaic nodes for: ， In the formula, For the first layer .

4. A household photovoltaic multi-path inspection data processing method according to claim 1, characterized in that: The method of using the crayfish optimization algorithm to calculate the optimal path for collecting node data includes: Changes in temperature will affect the behavior of crayfish, causing them to enter different stages. The temperature definition calculation formula is: ， In the formula, is the temperature of the environment where the crayfish is located, is random data; When the temperature of crayfish is between 20℃ and 35℃, the mathematical model of crayfish intake is expressed as: ， In the formula, For crayfish intake, The temperature that is most suitable for the growth of crayfish. , are the standard deviation and the constants of uptake at different temperatures, respectively; The expression for crayfish in the summer stage is: ， ， ， ， In the formula, For food location, For the size of the food, For food factors, For the The fitness value of a crayfish, is the fitness value of the food location, is the optimal position obtained so far in the number of iterations, To represent individual exist The location in the dimension, For a random individual The location of the dimension, Indicates the updated food location; Determine the optimal path planning for the crayfish optimization algorithm.

5. A household photovoltaic multi-path inspection data processing system using the method described in claim 1, characterized in that: include: The construction module is configured to obtain the longitude and latitude of household photovoltaic users, build a household photovoltaic multi-path network node model, and obtain the total number of household photovoltaic nodes; A calculation module configured to calculate an optimal path for collecting node data using a crayfish optimization algorithm according to the total number of household photovoltaic nodes; The processing module is configured to perform encryption operations on each node data on the optimal path according to a preset red deer-bird algorithm, and process the encrypted node data using a multi-objective heterogeneous processing rule to obtain a final processing result.

6. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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