Distributed photovoltaic data acquisition method based on 5G communication

Abstract

The invention provides a distributed photovoltaic data acquisition method based on 5G communication, and relates to the technical field of data acquisition, and the method comprises the steps: 1, configuring corresponding acquisition equipment for different working objects in a photovoltaic transformer area, and collecting the working parameters of the corresponding working objects based on the acquisition equipment; 2, transmitting the collected data to a local database based on a 5G base station and an edge node, and performing primary processing on the collected data based on the local database; 3, performing data security analysis on the primarily processed data based on the monitoring host, and determining a working state and a data security coefficient of each working object; and 4, generating an early warning instruction related to each working object and a region instruction related to each working region based on the working state and the data safety coefficient, and issuing the early warning instruction and the region instruction to a dispatching center for display and early warning. Data simplification and sharing of channel capacity under data acquisition are realized layer by layer, and data acquisition efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of data acquisition technology, and in particular to a distributed photovoltaic data acquisition method based on 5G communication. Background Art

[0002] There are many types of equipment involved in photovoltaic areas, mainly including core equipment such as photovoltaic modules and inverters, support and transmission equipment such as bracket systems and cables, as well as auxiliary equipment such as monitoring systems and lightning protection and grounding systems. In order to ensure the normal operation of photovoltaic areas, it is necessary to start from equipment maintenance, environmental management, system monitoring and other aspects. Among them, equipment maintenance is extremely important, and the premise of equipment maintenance is to collect the operating data of the equipment. In general, in the data collection process, data transmission is carried out on a fixed channel to realize the collection of data from the previous level of equipment by the next level of equipment, and the capacity of the channel is subject to certain limitations. There are generally only two previous level equipment and next level equipment, namely the transmission end and the destination end, and no other levels are involved in the middle. When the amount of data is too large, data collection will not only cause a waste of business resources, but also limit the data collection efficiency of the next level of equipment for the previous level of equipment due to capacity limitations.

[0003] Therefore, the present invention proposes a distributed photovoltaic data collection method based on 5G communication. Summary of the invention

[0004] The present invention provides a distributed photovoltaic data collection method based on 5G communication, which is used to achieve data simplification and channel capacity sharing under data collection layer by layer through 5G base stations, edge nodes, local databases, and monitoring hosts, thereby improving data collection efficiency. In addition, by subsequently analyzing the status and safety factor of the work object, it is convenient to carry out subsequent early warnings to achieve timely processing of abnormalities, thereby indirectly improving collection efficiency.

[0005] The present invention provides a distributed photovoltaic data collection method based on 5G communication, comprising: Step 1: configuring corresponding collection equipment for different working objects in the photovoltaic area, and collecting working parameters of the corresponding working objects based on the collection equipment; Step 2: Transmitting the collected data to a local database based on a 5G base station and an edge node, and performing preliminary processing on the collected data based on the local database; Step 3: Perform data security analysis on the preliminarily processed data based on the monitoring host to determine the working status of each work object and the data security factor; Step 4: Generate warning instructions related to each work object and area instructions related to each work area based on the work status and data safety factor, and send them to the dispatch center for display and warning.

[0006] Preferably, the working objects include photovoltaic-related working equipment and equipment for monitoring the operating behaviors of the workers.

[0007] Preferably, the process of transmitting the collected data to the local database based on the 5G base station and the edge node includes: Based on the settings of each work object, the network requirements are matched from the function-network database; Perform cluster analysis on all network demands to obtain clusters of each clustering result, and lock the first position of the cluster with the second position of the first demand farthest from the cluster and the third position of the second demand farthest from the cluster; Based on the distance between any two positions in the first position, the second position, and the third position, set the required range value to the corresponding clustering result ; ; in, Respectively represent the distance lengths between the first position and the second position, the first position and the third position, and the second position and the third position; max represents the maximum value symbol; Indicates the set area radius of the area where the corresponding clustering result is placed, and is greater than ; Indicates that the corresponding clustering results are The average value of the distance between any two remaining positions other than 3; Represents the variance of all distance lengths involved in the corresponding clustering results; Indicates that the corresponding clustering results are The variance of the remaining distance length outside ; Selecting the shortest distance from the three distance lengths, dividing the circular area with the first position of the cluster as the center, and determining the demand clustering density of the circular area; According to the demand range value, combined with the number of network demands involved in the corresponding clustering result and the demand clustering density, determine the maximum boundary value of the number of slices that can be added based on the corresponding clustering result; The amount of data collected for each work object in the photovoltaic work park at each historical time point in a time period of length T is randomly obtained from the historical database to construct a data volume matrix ,in, Respectively represent the data volume vector at the 1st historical time point and the Nth historical time point, and , Represents the data volume vector at the jth historical time point; represents the amount of collected data of the ith work object based on the jth historical time point; m represents the number of work objects; Extracting column vectors of working objects involved in the same clustering result from the data volume matrix in sequence, and constructing a new clustering matrix; Determining the number of network slices required in general based on the new clustering matrix; A network formed by the sum of the number of network slices and the maximum boundary value of the number of slices that can be added is used as a transmission slice network for all work objects involved in the corresponding clustering result; The collected data is transmitted based on the 5G base station according to the transmission slice network.

[0008] Preferably, the maximum limit value of the number of slices that can be added based on the corresponding clustering result is determined. ,include: ; in, Indicates the number of network requirements involved in the corresponding clustering results; Indicates the number of network requirements involved in the corresponding circular area; Indicates the demand clustering density corresponding to the circular area; Indicates the preset clustering density; Represents the average value of the demand range based on all clustering results; Indicates the floor symbol.

[0009] Preferably, determining the number of network slices required for conventional processing based on the new clustering matrix includes: ; in, , , They represent judgment functions, and , , , Indicates the number of column vectors in the corresponding clustering new matrix; represents the average value of all values ​​in the kth column vector; Represents the coefficient of the middle point on the fitted straight line obtained after curve drawing and linear fitting of the kth column vector; The variance of all values ​​in the kth column vector; Indicates the set transmission volume of each network slice; Indicates the number of network slices normally required.

[0010] Preferably, the preliminary processing refers to data cleaning and data missing value filling processing.

[0011] Preferably, determining the working status and data safety factor of each working object includes: Extracting first processed data of each work object from the preliminarily processed data, and comparing and analyzing the first processed data with standard data of the work object to determine abnormal data; Retrieving a state analysis model matching the work object from a state analysis database; The abnormal data is input into the state analysis model to obtain the working state and data safety factor of the corresponding working object.

[0012] Preferably, generating a warning instruction related to each work object and a regional instruction related to each work area includes: Match the warning instructions for the corresponding work object from the object-state-coefficient-instruction comparison table; Each work object is classified according to the regional division result of the photovoltaic area, and a list of all early warning instructions under the same classification is generated to obtain regional instructions.

[0013] Compared with the prior art, the present invention has the following beneficial effects: Through 5G base stations, edge nodes, local databases, and monitoring hosts, data simplification and channel capacity sharing during data collection are achieved layer by layer, thereby improving data collection efficiency. Subsequently, by analyzing the status and safety factor of the work object, early warning is facilitated to facilitate timely handling of anomalies, thereby indirectly improving collection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 It is a flow chart of a distributed photovoltaic data collection method based on 5G communication provided by an embodiment of the present invention; Figure 2 It is a photovoltaic area networking structure based on 5G communication; Figure 3 It is a structural diagram of the distributed photovoltaic local communication solution; Figure 4 This is a transmission diagram based on the 5G slice private network; Figure 5 It is the call structure diagram of the monitoring host; Figure 6 It is a structural diagram of the distributed photovoltaic peak-shaving command model. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] The present invention provides a distributed photovoltaic data collection method based on 5G communication, such as Figure 1 As shown, including: Step 1: configuring corresponding collection equipment for different working objects in the photovoltaic area, and collecting working parameters of the corresponding working objects based on the collection equipment; Step 2: Transmitting the collected data to a local database based on a 5G base station and an edge node, and performing preliminary processing on the collected data based on the local database; Step 3: Perform data security analysis on the preliminarily processed data based on the monitoring host to determine the working status of each work object and the data security factor; Step 4: Generate warning instructions related to each work object and area instructions related to each work area based on the work status and data safety factor, and send them to the dispatch center for display and warning.

[0018] Preferably, the working objects include photovoltaic-related working equipment and equipment for monitoring the operating behaviors of the workers.

[0019] Preferably, the preliminary processing refers to data cleaning and data missing value filling processing, which belongs to the prior art.

[0020] In this embodiment, the equipment involved may be a transformer box, an inverter, a distribution cabinet, a junction box, a photovoltaic array, an environmental detector, a drone, a camera, an inspection robot, etc., that is, the collected data involves grid-end data, power station internal equipment data, and power station internal environment data.

[0021] In this embodiment, the staff's operation object is, for example, opening or closing a distribution cabinet, which is captured by a camera.

[0022] In this embodiment, data security analysis is implemented based on relevant analysis models, and the working states include: normal operating state and abnormal operating state.

[0023] In this embodiment, the value range of the data security factor is 0 to 1.

[0024] In this embodiment, the regional instruction is implemented based on the early warning instruction.

[0025] In this embodiment, the photovoltaic area networking structure based on 5G communication is as follows: Figure 2 As shown in the figure, the SA networking in this framework provides the power station with the ability to provide 5G customized services; edge computing relieves the pressure on the monitoring center, processes data on-site and feeds the processed data back to the monitoring center; 5G network slicing divides the network according to the application requirements of the power station; UPF sinking shortens the physical distance of data forwarding and protects data security.

[0026] In this embodiment, the local communication scheme is designed as follows: Embedded system development: high-performance processors and embedded operating systems are selected to achieve rapid response and stability of the system. The main control chip used in the intelligent terminal is the SCM701D model. Through the platform-based hardware solution and the autonomous and controllable operating system, an intelligent terminal platform with high reliability, fast speed, large storage capacity and strong openness is built. The external interfaces of the intelligent terminal platform include the exchange board connection interface, MINIUSB interface (image loading interface), debug serial port (TYPE-C interface RS232 protocol), local communication module socket (weak current end), local communication module socket (strong current end), remote signal interface (4-way switch input interface), RS485 / 232 and pulse interface, 2-way Ethernet interface, reserved module socket, remote communication module socket, strong current connection socket and wireless Bluetooth module, etc. These external interfaces can realize the communication of mobile phone apps, the connection of local databases, the communication of 5G networks, etc.

[0027] Communication mode: The distributed photovoltaic intelligent terminal uses HPLC technology for downlink. Users who have configured photovoltaic inverters that support HPLC can directly exchange information with the intelligent terminal in the substation. Users who have configured photovoltaic inverters that do not support HPLC can add a photovoltaic controller on the photovoltaic substation side and use RS485 to communicate with the photovoltaic inverter and grid-connected switch to collect, encrypt and upload data such as voltage, current, active power, reactive power, electricity and switch status. The distributed photovoltaic local communication solution is shown in Figure 3.

[0028] The remote communication solution design is as follows: The 5G slicing private network is used to upload photovoltaic collection data and issue control instructions; the 5G slicing network has the characteristics of ultra-large-scale connection, ultra-low latency, and ultra-high bandwidth, and the network performance is greatly improved. Multi-access edge computing (MEC) and user plane function (UPF) are deployed at the power communication station to upload photovoltaic collection data to the power communication station through the dedicated 5G slicing network, and then the photovoltaic collection data is connected to the secure access area through the power optical transmission network dedicated line or the dispatching data network channel to realize the upload of collection information and the issuance of control instructions, such as Figure 4 shown.

[0029] In this embodiment, the monitoring host also includes data calling, which is as follows: The monitoring host (data center) is located at the center of information interaction between the cloud, edge, and end. The collection business APP is responsible for collecting data from end devices and writing the data into the data center. The analysis business APP obtains the data center data, performs secondary processing and analysis to realize edge computing. The main station business APP obtains the data center data and uploads the data to the cloud main station. All business APPs realize data sharing through the data center, such as Figure 5 shown.

[0030] In this embodiment, the management based on the local database is as follows: the local communication management APP is responsible for acting as the carrier module (CCO) and providing a calling interface to the business APP. The calling interface exists in two forms, namely JSON and ASN.1.

[0031] In this embodiment, during the data collection process, the data collection tasks of the subordinates for the superiors can also be prioritized according to data usage, timeliness, importance, etc. For example, they can be sorted according to the priority of first-level events, 1min high-frequency collection, daily freezing, 15min, hourly high-frequency collection, second-level events, and third-level events to ensure reliable upload of minute-level data.

[0032] In this embodiment, if there is a busy channel during the process of the lower level collecting data from the upper level, the peak load order model can be used for regulation, such as Figure 6 As shown in Figure 1, the model consists of three parts: command type, command mode, and local dispatching allocation mode. There are two types of peak load regulation commands: power-based command and capacity-based command.

[0033] The beneficial effects of the above technical solution are: through 5G base stations, edge nodes, local databases, and monitoring hosts, data simplification and channel capacity sharing under data collection are achieved layer by layer, thereby improving data collection efficiency. Subsequently, through status and safety factor analysis of the work object, subsequent early warning is facilitated to achieve timely handling of anomalies, thereby indirectly improving collection efficiency.

[0034] The present invention provides a distributed photovoltaic data collection method based on 5G communication, in which the collected data is transmitted to a local database based on a 5G base station and an edge node, including: Based on the settings of each work object, the network requirements are matched from the function-network database; Perform cluster analysis on all network demands to obtain clusters of each clustering result, and lock the first position of the cluster with the second position of the first demand farthest from the cluster and the third position of the second demand farthest from the cluster; Based on the distance between any two positions in the first position, the second position, and the third position, set the required range value to the corresponding clustering result ; ; in, Respectively represent the distance lengths between the first position and the second position, the first position and the third position, and the second position and the third position; max represents the maximum value symbol; Indicates the set area radius of the area where the corresponding clustering result is placed, and is greater than ; Indicates that the corresponding clustering results are The average value of the distance between any two remaining positions other than 3; Represents the variance of all distance lengths involved in the corresponding clustering results; Indicates that the corresponding clustering results are The variance of the remaining distance length outside ; Selecting the shortest distance from the three distance lengths, dividing the circular area with the first position of the cluster as the center, and determining the demand clustering density of the circular area; According to the demand range value, combined with the number of network demands involved in the corresponding clustering result and the demand clustering density, determine the maximum boundary value of the number of slices that can be added based on the corresponding clustering result; The amount of data collected for each work object in the photovoltaic work park at each historical time point in a time period of length T is randomly obtained from the historical database to construct a data volume matrix ,in, Respectively represent the data volume vector at the 1st historical time point and the Nth historical time point, and , Represents the data volume vector at the jth historical time point; represents the amount of collected data of the ith work object based on the jth historical time point; m represents the number of work objects; Extracting column vectors of working objects involved in the same clustering result from the data volume matrix in sequence, and constructing a new clustering matrix; Determining the number of network slices required in general based on the new clustering matrix; A network formed by the sum of the number of network slices and the maximum boundary value of the number of slices that can be added is used as a transmission slice network for all work objects involved in the corresponding clustering result; The collected data is transmitted based on the 5G base station according to the transmission slice network.

[0035] Preferably, the maximum limit value of the number of slices that can be added based on the corresponding clustering result is determined. ,include: ; in, Indicates the number of network requirements involved in the corresponding clustering results; Indicates the number of network requirements involved in the corresponding circular area; Indicates the demand clustering density corresponding to the circular area; Indicates the preset clustering density; Represents the average value of the demand range based on all clustering results; Indicates the floor symbol.

[0036] Preferably, determining the number of network slices required for conventional processing based on the new clustering matrix includes: ; in, , , They represent judgment functions, and , , , Indicates the number of column vectors in the corresponding clustering new matrix; represents the average value of all values ​​in the kth column vector; Represents the coefficient of the middle point on the fitted straight line obtained after curve drawing and linear fitting of the kth column vector; The variance of all values ​​in the kth column vector; Indicates the set transmission volume of each network slice; Indicates the number of network slices normally required.

[0037] In this embodiment, the set implementation function refers to the function performed by the working object, for example, the function of the photovoltaic array is to collect light energy and convert it into electrical energy.

[0038] In this embodiment, the function-network database includes different settings to implement functions and matching network requirements, because the network transmission channels required by different objects are different, and the size of the network transmission channels is reflected in the number of network slices.

[0039] In this embodiment, cluster analysis is implemented based on the K-means algorithm, which belongs to the prior art, and thus clusters can be directly determined. Then, based on the clustering results, the position of each demand in the results can be effectively determined.

[0040] In this embodiment, the shortest distance is based on The circular area is divided based on the first position and with the shortest distance as the radius.

[0041] In this embodiment, demand clustering density=the number of demands involved in the circular area / the area of ​​the circular area.

[0042] In this embodiment, the maximum boundary value is determined so that when the number of network slices required for the conventional operation is insufficient, the spare slices below the maximum boundary value can be screened to ensure the effective collection of upper-level data by the lower-level.

[0043] In this embodiment, the historical database contains the data volumes generated by different work objects at different times.

[0044] In this embodiment, the transmission slice network is used to transmit the collected data related to the work object involved.

[0045] The beneficial effects of the above technical solution are: cluster analysis is performed according to network requirements to preliminarily classify the device-related collected data, so that different data can be transmitted using different slices to avoid data confusion and data congestion, and the maximum boundary value is determined by the demand range value and demand clustering density to set spare slices to ensure that the lower level can effectively collect the upper level data when the data volume is too large.

[0046] The present invention provides a distributed photovoltaic data collection method based on 5G communication, which determines the working status of each working object and the data safety factor, including: Extracting first processed data of each work object from the preliminarily processed data, and comparing and analyzing the first processed data with standard data of the work object to determine abnormal data; Retrieving a state analysis model matching the work object from a state analysis database; The abnormal data is input into the state analysis model to obtain the working state and data safety factor of the corresponding working object.

[0047] In this embodiment, each work object has corresponding first data.

[0048] In this embodiment, standard data is pre-set. For example, the collected data of the inverter is: DC side voltage, DC side current, DC side power, AC side voltage, AC side current, AC side power, AC side active power, AC side reactive power, AC side power factor, etc., and the standard value corresponding to each parameter is set in advance as a comparison standard.

[0049] In this embodiment, the state analysis database includes state analysis models of different working objects, and the state analysis models are obtained by training a neural network model based on parameter combinations of different working objects and analysis results of the parameter combinations (state and data safety factors) as samples. Therefore, the required results can be directly obtained based on the relevant models.

[0050] The beneficial effect of the above technical solution is: by comparing and analyzing the first processed data with the standard data, the abnormal data is determined, and then the working state and data safety factor of the working object are determined through the state analysis model, providing a basis for subsequent early warning.

[0051] The present invention provides a distributed photovoltaic data collection method based on 5G communication, which generates early warning instructions related to each working object and regional instructions related to each working area, including: Match the warning instructions for the corresponding work object from the object-state-coefficient-instruction comparison table; Each work object is classified according to the regional division result of the photovoltaic area, and a list of all early warning instructions under the same classification is generated to obtain regional instructions.

[0052] In this embodiment, the object-state-coefficient-instruction comparison table includes warning instructions for different working objects under different working states and data safety factors, which can be a combination of one or more of sound, light, and text description.

[0053] In this embodiment, the area division result may be an object result of a power grid end area, a power station internal equipment area, and a power station internal environment area.

[0054] In this embodiment, the list refers to statistics of all warning instructions under this category.

[0055] In this embodiment, the regional instruction is a general term for all warning instructions involved in the list.

[0056] The beneficial effects of the above technical solution are: obtaining early warning instructions from the comparison table, and combining the regional division results to achieve reasonable classification of the early warning instructions, and obtaining regional instructions to provide a basis for subsequent early warnings.

[0057] 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 distributed photovoltaic data collection method based on 5G communication, characterized in that: include: Step 1: configuring corresponding collection equipment for different working objects in the photovoltaic area, and collecting working parameters of the corresponding working objects based on the collection equipment; Step 2: Transmitting the collected data to a local database based on a 5G base station and an edge node, and performing preliminary processing on the collected data based on the local database; Step 3: Perform data security analysis on the preliminarily processed data based on the monitoring host to determine the working status of each work object and the data security factor; Step 4: Generate warning instructions related to each work object and area instructions related to each work area based on the work status and data safety factor, and send them to the dispatch center for display and warning.

2. The distributed photovoltaic data acquisition method based on 5G communication according to claim 1 is characterized in that: The working objects include photovoltaic-related working equipment and equipment for monitoring the operating behaviors of the staff.

3. The distributed photovoltaic data acquisition method based on 5G communication according to claim 1 is characterized in that: The process of transmitting the collected data to the local database based on 5G base stations and edge nodes includes: Based on the settings of each work object, the network requirements are matched from the function-network database; Perform cluster analysis on all network demands to obtain clusters of each clustering result, and lock the first position of the cluster with the second position of the first demand farthest from the cluster and the third position of the second demand farthest from the cluster; Based on the distance between any two positions in the first position, the second position, and the third position, set the required range value to the corresponding clustering result ; ； in, Respectively represent the distance lengths between the first position and the second position, the first position and the third position, and the second position and the third position; max represents the maximum value symbol; Indicates the set area radius of the area where the corresponding clustering result is placed, and is greater than ; Indicates that the corresponding clustering results are The average value of the distance between any two remaining positions other than 3; Represents the variance of all distance lengths involved in the corresponding clustering results; Indicates that the corresponding clustering results are The variance of the remaining distance lengths beyond ; Selecting the shortest distance from the three distance lengths, dividing the circular area with the first position of the cluster as the center, and determining the demand clustering density of the circular area; According to the demand range value, combined with the number of network demands involved in the corresponding clustering result and the demand clustering density, determine the maximum boundary value of the number of slices that can be added based on the corresponding clustering result; The amount of data collected for each work object in the photovoltaic work park at each historical time point in a time period of length T is randomly obtained from the historical database to construct a data volume matrix ,in, Respectively represent the data volume vector at the 1st historical time point and the Nth historical time point, and , Represents the data volume vector at the jth historical time point; represents the amount of collected data of the ith work object based on the jth historical time point; m represents the number of work objects; Extracting column vectors of working objects involved in the same clustering result from the data volume matrix in sequence, and constructing a new clustering matrix; Determining the number of network slices required in general based on the new clustering matrix; A network formed by the sum of the number of network slices and the maximum boundary value of the number of slices that can be added is used as a transmission slice network for all work objects involved in the corresponding clustering result; The collected data is transmitted based on the 5G base station according to the transmission slice network.

4. The distributed photovoltaic data acquisition method based on 5G communication according to claim 3 is characterized in that: Determine the maximum limit value of the number of slices that can be added based on the corresponding clustering results ,include: ； in, Indicates the number of network requirements involved in the corresponding clustering results; Indicates the number of network requirements involved in the corresponding circular area; Indicates the demand clustering density corresponding to the circular area; Indicates the preset cluster density; Represents the average value of the demand range based on all clustering results; Indicates the floor symbol.

5. The distributed photovoltaic data collection method based on 5G communication according to claim 3 is characterized in that: The number of network slices required in general is determined based on the new clustering matrix, including: ； in, , , They represent judgment functions, and , , , Indicates the number of column vectors in the corresponding clustering new matrix; represents the average value of all values ​​in the kth column vector; Represents the coefficient of the middle point on the fitted straight line obtained after curve drawing and linear fitting of the kth column vector; The variance of all values ​​in the kth column vector; Indicates the set transmission volume of each network slice; Indicates the number of network slices normally required.

6. The distributed photovoltaic data collection method based on 5G communication according to claim 1 is characterized in that: Preliminary processing refers to data cleaning and filling in missing values.

7. The distributed photovoltaic data collection method based on 5G communication according to claim 1 is characterized in that: Determine the working status and data security factor of each work object, including: Extracting first processed data of each work object from the preliminarily processed data, and comparing and analyzing the first processed data with standard data of the work object to determine abnormal data; Retrieving a state analysis model matching the work object from a state analysis database; The abnormal data is input into the state analysis model to obtain the working state and data safety factor of the corresponding working object.

8. The distributed photovoltaic data collection method based on 5G communication according to claim 1 is characterized in that: Generate warning instructions related to each work object and area instructions related to each work area, including: Match the warning instructions for the corresponding work object from the object-state-coefficient-instruction comparison table; Each work object is classified according to the regional division result of the photovoltaic area, and a list of all early warning instructions under the same classification is generated to obtain regional instructions.

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