Household photovoltaic multi-path inspection data processing method and system

Abstract

The invention discloses a household photovoltaic multi-path inspection data processing method and system, and the method comprises the steps: obtaining the longitude and latitude of a household photovoltaic user, building a household photovoltaic multi-path network node model, and obtaining the total number of household photovoltaic nodes; according to the total number of the household photovoltaic nodes, calculating an optimal path for collecting node data by adopting a crayfish optimization algorithm; and according to a preset red deer-bird flock algorithm, performing encryption operation on each node data on the optimal path, and processing each encrypted node data by adopting a multi-target heterogeneous processing rule to obtain a final processing result. The energy consumption of household photovoltaic multi-path data acquisition is reduced, and the energy efficiency of household photovoltaic multi-path data acquisition is improved.

Description

Technical Field

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

[0002] There is a growing trend towards small-scale decentralized generation within the distribution system, where end users can become more actively involved by installing generators in their own buildings.

[0003] The healthy operation of household photovoltaics is inseparable from regular inspection and maintenance, which is the premise of efficient power generation of photovoltaic systems. However, due to the complex road conditions and high and concentrated population density in urban areas, the energy consumption during data transmission is large, and the frequency of massive data processing is low. 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 response to the above problems, the present invention proposes an improved household photovoltaic multi-path inspection data processing method, which aims 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 provide better feedback to users.

[0005] In a first aspect, the present invention provides a household photovoltaic multi-path inspection data processing method, comprising: 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, is the minimum distance in path planning, is the maximum planning distance in path planning, For the Path nodes of PV users, For the PV users; The data of each node on the optimal path is encrypted according to the preset red deer-bird algorithm, and the encrypted data of each node is processed using multi-objective heterogeneous processing rules to obtain the final processing result.

[0006] In a second aspect, the present invention provides a household photovoltaic multi-path inspection data processing system, comprising: 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; The calculation module is configured to calculate the optimal path for collecting node data using the crayfish optimization algorithm according to the total number of household photovoltaic nodes, 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, is the minimum distance in path planning, is the maximum planning distance in path planning, For the Path nodes of PV users, For the PV users; 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.

[0007] In a third aspect, an electronic device is provided, comprising: 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 so that the at least one processor can perform the steps of the household photovoltaic multi-path inspection data processing method of any embodiment of the present invention.

[0008] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of the household photovoltaic multi-path inspection data processing method of any embodiment of the present invention.

[0009] The household photovoltaic multi-path inspection data processing method and system of the present application studies the use of a dynamic vortex search algorithm to reduce the energy consumption of data transmission, studies the establishment of a household photovoltaic multi-path network node model to obtain the total number of household photovoltaic nodes, studies the calculation of the optimal path for collecting node data through a crayfish optimization algorithm, studies the use of a red deer-bird flock algorithm to achieve privacy protection of node data, and studies the use of a multi-target heterogeneous cloud to process the massive data aggregated at the nodes, thereby reducing the energy consumption of household photovoltaic multi-path data collection, improving the energy efficiency of household photovoltaic multi-path data collection, protecting privacy protection during data processing, reducing network delays, and increasing the frequency of massive data processing, providing better feedback to users. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A flowchart of a household photovoltaic multi-path inspection data processing method provided by an embodiment of the present invention; Figure 2 A structural block diagram of a household photovoltaic multi-path inspection data processing system provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] See also Figure 1 , which shows a flow chart of a household photovoltaic multi-path inspection data processing method of the present application.

[0014] like Figure 1 As shown, the household photovoltaic multi-path inspection data processing method specifically includes the following steps: Step S101, 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.

[0015] In this step, it is assumed that the household photovoltaic multi-path network node model has Z layers, which are 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 .

[0016] Step S102, according to the total number of household photovoltaic nodes, the crayfish optimization algorithm is used to calculate the optimal path for collecting node data.

[0017] In this step, node data is obtained and transmitted 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 Respectively The upper and lower bounds of each dimension of the photovoltaic user search space are 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.

[0018] 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 data packets, Indicates confirmation character.

[0019] 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.

[0020] Determine the optimal path planning of the crayfish optimization algorithm, where 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, is the minimum distance in path planning, is the maximum planning distance in path planning, For the Path nodes of PV users, For the PV users.

[0021] In a specific embodiment, three different algorithms are compared as shown in Table 1, and Matlab numerical simulation is used to verify the proposed mathematical algorithm.

[0022] , The energy consumption comparison results are shown in Table 2 and the data processing frequency results are shown in Table 3.

[0023] , It can be seen from Table 2 that 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 nodes there are, the more obvious the energy saving is. Until the nodes are above 27, the energy consumption reduction is not very obvious. The comparison results prove the superiority of the improved household photovoltaic multi-path inspection data processing method proposed in the present invention.

[0024] , 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 above 7GHZ in 0.06S, while the traditional system of DA 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, which solves the problem of low processing frequency caused by weak analysis ability and slow response speed of the traditional system.

[0025] Step S103, encrypting the data of each node on the optimal path according to the preset red deer-bird algorithm, and processing the encrypted data of each node using multi-objective heterogeneous processing rules to obtain the final processing result.

[0026] In this step, the solution is initialized: the solution with random numbers is initialized; 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, is the foraging probability. In this case, the position of the flock is updated according to the location of the food source, expressed as: , 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.

[0027] 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 The product of functions.

[0028] It should be noted that the parent nodes are calculated sequentially starting from the starting node, and 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 brought 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, For transmission The number of packets, For the PV users, is the number of iterations of the previous generation; In summary, the method of the present application, first, studies the use of a dynamic vortex search algorithm to reduce the energy consumption of data transmission, studies the establishment of a household photovoltaic multi-path network node model to obtain the total number of household photovoltaic nodes, studies the calculation of the optimal path for collecting node data through a crayfish optimization algorithm, studies the use of a red deer-bird flock algorithm to achieve privacy protection of node data, studies the use of a multi-objective heterogeneous cloud to process massive data aggregated at nodes, reduces the energy consumption of household photovoltaic multi-path data collection, improves the energy efficiency of household photovoltaic multi-path data collection, protects privacy protection during data processing, reduces network latency, increases the frequency of massive data processing, and provides better feedback to users.

[0029] See also Figure 2 , which shows a structural block diagram of a household photovoltaic multi-path inspection data processing system of the present application.

[0030] like Figure 2 As shown, the household photovoltaic multi-path inspection data processing system 200 includes a construction module 210 , a calculation module 220 and a processing module 230 .

[0031] Among them, the construction module 210 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; The calculation module 220 is configured to calculate the optimal path for collecting node data using the crayfish optimization algorithm according to the total number of household photovoltaic nodes, 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, is the minimum distance in path planning, is the maximum planning distance in path planning, For the Path nodes of PV users, For the PV users; The processing module 230 is configured to perform encryption operations on each node data on the optimal path according to a preset red deer-bird algorithm, and process each encrypted node data using a multi-objective heterogeneous processing rule to obtain a final processing result.

[0032] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0033] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, and when the program instructions are executed by a processor, the processor executes the household photovoltaic multi-path inspection data processing method in any of the above method embodiments; As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows: 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, is the minimum distance in path planning, is the maximum planning distance in path planning, For the Path nodes of PV users, For the PV users; The data of each node on the optimal path is encrypted according to the preset red deer-bird algorithm, and the encrypted data of each node is processed using multi-objective heterogeneous processing rules to obtain the final processing result.

[0034] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may 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 a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the household photovoltaic multi-path inspection data processing system via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0035] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also 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 may be connected via a bus or other means. Figure 3 The example of the bus connection is taken. The memory 320 is the computer-readable storage medium mentioned above. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the household photovoltaic multi-path inspection data processing method of the above method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the household photovoltaic multi-path inspection data processing system. The output device 340 may include display devices such as display screens.

[0036] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0037] As an implementation mode, the electronic device is applied to a household photovoltaic multi-path inspection data processing system, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: 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, is the minimum distance in path planning, is the maximum planning distance in path planning, For the Path nodes of PV users, For the PV users; The data of each node on the optimal path is encrypted according to the preset red deer-bird algorithm, and the encrypted data of each node is processed using multi-objective heterogeneous processing rules to obtain the final processing result.

[0038] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0039] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A 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, is the minimum distance in path planning, is the maximum planning distance in path planning, For the Path nodes of PV users, For the PV users; The data of each node on the optimal path is encrypted according to the preset red deer-bird algorithm, and the encrypted data of each node is processed using multi-objective heterogeneous processing rules to obtain the final processing result.

2. A household photovoltaic multi-path inspection data processing method according to claim 1, characterized in that: 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, 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 Respectively The upper and lower bounds of each dimension of the photovoltaic user search space are is the generated vortex radius, is the inverse incomplete function, is the upper limit of the score, 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 2, characterized in that: The energy consumption of the 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 of data transmission, For transmission and the amount of packets, Indicates data packets, Indicates confirmation character.

4. 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 .

5. 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 an 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.

6. A household photovoltaic multi-path inspection data processing method according to claim 1, characterized in that: The encryption operation of each node data on the optimal path according to the preset red deer-bird algorithm includes: Initialize solution: Initialize the solution with random numbers; Fitness evaluation: After the initialization solution, for 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 The product of functions.

7. A household photovoltaic multi-path inspection data processing method according to claim 1, characterized in that: 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 brought 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, For transmission The number of packets, For the PV users, is the number of iterations of the previous generation.

8. A household photovoltaic multi-path inspection data processing system, 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; The calculation module is configured to calculate the optimal path for collecting node data using the crayfish optimization algorithm according to the total number of household photovoltaic nodes, 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, is the minimum distance in path planning, is the maximum planning distance in path planning, For the Path nodes of PV users, For the PV users; 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.

9. 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 execute the method described in any one of claims 1 to 7.

10. 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 7 is implemented.

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