Electricity consumption information acquisition terminal intelligent configuration method and system based on big data

Abstract

The invention relates to the technical field of data processing, and discloses a big data-based intelligent configuration method and system for an electricity consumption information acquisition terminal. The method comprises the steps that an acquisition task management scheme and a parameter configuration template are analyzed and matched through an acquisition mode recognition algorithm, and a terminal configuration strategy and a terminal acquisition task file are obtained; performing block coding and encryption processing on the terminal configuration strategy and the terminal acquisition task file according to an acquisition task structure to obtain a configuration operation sequence and an acquisition operation state; and performing multi-dimensional analysis on the configuration operation sequence and the acquisition operation state according to a scoring standard of the electric energy acquisition feature library to obtain a terminal configuration health degree index and an acquisition execution record. According to the method, the terminal function parameters are flexibly adjusted in a file configuration mode without modifying terminal program codes, and meanwhile, the safety and reliability of the configuration process are ensured, so that the influence of frequent software upgrading on the system stability is avoided.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method and system for intelligently configuring an electricity consumption information collection terminal based on big data. Background Art

[0002] At present, the communication protocols related to the power consumption information collection terminal equipment of the State Grid Corporation are mainly based on the object-oriented data interaction protocol, namely the DL / T 698.45-2017 protocol. According to the requirements of the protocol, the collection terminal equipment needs to have the ability to communicate and interact with the collection master station, electric energy meter and other equipment to meet the functional requirements of data collection, data reporting, file transfer, command execution and so on. Each provincial grid company mainly uses two methods to configure the terminal functions: one is to list the relevant functional parameters one by one through the power consumption information collection master station and send them to the collection terminal equipment; the other is to configure the relevant functional parameters by default according to the requirements when the collection terminal equipment leaves the factory. With the promotion and application of smart electric energy meters, the demand for various new businesses such as distributed photovoltaic, orderly power consumption, and demand response in the substation area is increasing, and the requirements for the type, frequency and quality of power consumption data collection are becoming more and more diversified, which puts higher requirements on the configuration management of the collection terminal equipment.

[0003] However, the existing terminal function configuration method has obvious shortcomings: on the one hand, the method of sending parameters one by one through the main station requires reconfiguration after the terminal is restored to factory settings, which consumes a lot of time and manpower; on the other hand, when the factory default configuration method is used, when the function parameters need to be adjusted, it must be achieved by modifying the device software and upgrading it, which not only requires a lot of functional testing, upgrade implementation and post-upgrade monitoring work, but also frequent upgrades can easily lead to unstable functions of the collection terminal, affecting the continuity and stability of the regular business of electricity consumption information collection. Summary of the invention

[0004] The present application provides a method and system for intelligent configuration of power consumption information collection terminals based on big data, which is used to solve how to flexibly adjust terminal function parameters through configuration files without modifying the terminal program code, while ensuring the security and reliability of the configuration process, thereby avoiding the impact of frequent software upgrades on system stability.

[0005] In the first aspect, the present application provides an intelligent configuration method for an electricity consumption information collection terminal based on big data, and the intelligent configuration method for an electricity consumption information collection terminal based on big data includes: classifying and sorting the collection terminal business data and electricity consumption function data and performing correlation analysis to obtain a terminal function dependency graph and an electric energy collection feature library; hierarchically organizing and standardizing the meter reading parameter information and the collection task structure in the electric energy collection feature library to obtain configuration file format specifications and communication grammar rules; visually arranging the terminal function dependency graph and the configuration file format specifications and mapping the communication grammar rules to obtain a collection task management plan and a parameter configuration template; analyzing and matching the collection task management plan and the parameter configuration template through a collection pattern recognition algorithm to obtain a terminal configuration strategy and a terminal collection task file; block encoding and encryption processing of the terminal configuration strategy and the terminal collection task file according to the collection task structure to obtain a configuration operation sequence and a collection operation status; multi-dimensionally analyzing the configuration operation sequence and the collection operation status according to the scoring criteria of the electric energy collection feature library to obtain a terminal configuration health index and a collection execution record.

[0006] In a second aspect, the present application provides an intelligent configuration system for power consumption information collection terminals based on big data, and the intelligent configuration system for power consumption information collection terminals based on big data includes: The collection module is used to classify and analyze the collected terminal business data and power consumption function data, and obtain the terminal function dependency graph and power collection feature library; An organization module, used for hierarchically organizing and standardizing the meter reading parameter information and the collection task structure in the electric energy collection feature library to obtain configuration file format specifications and communication grammar rules; An orchestration module, used to perform visual orchestration on the terminal function dependency graph and the configuration file format specification and map the communication grammar rules to obtain a collection task management solution and a parameter configuration template; A matching module, used to analyze and match the acquisition task management scheme and the parameter configuration template through an acquisition pattern recognition algorithm to obtain a terminal configuration strategy and a terminal acquisition task file; An encryption module, used for performing block encoding and encryption processing on the terminal configuration strategy and the terminal acquisition task file according to the acquisition task structure to obtain a configuration operation sequence and an acquisition operation status; The analysis module is used to perform multi-dimensional analysis on the configuration operation sequence and the collection operation status according to the scoring criteria of the power collection feature library to obtain the terminal configuration health index and the collection execution record.

[0007] In the technical solution provided by the present application, by classifying and sorting the terminal business data and the power consumption function data and analyzing the correlation, the terminal function dependency graph and the power collection feature library are obtained, and the intelligent management of the terminal function configuration is realized, avoiding the cumbersome process of traditional parameter issuance one by one. By hierarchically organizing and standardizing the meter reading parameter information and the collection task structure in the power collection feature library, the configuration file format specification and the communication grammar rules are obtained, which improves the standardization and maintainability of the configuration file. The terminal function dependency graph and the configuration file format specification are visually arranged and mapped with communication grammar rules to obtain the collection task management scheme and parameter configuration template, making the configuration process more intuitive and convenient. The collection pattern recognition algorithm is used to analyze and match the collection task management scheme and the parameter configuration template to obtain the terminal configuration strategy and the terminal collection task file, which enhances the intelligence of the configuration process. By block encoding and encryption of the terminal configuration strategy and the terminal collection task file according to the collection task structure, the configuration operation sequence and the collection operation status are obtained, which ensures the security and reliability of the configuration process. The configuration operation sequence and collection operation status are analyzed in multiple dimensions according to the scoring criteria of the power collection feature library, and the terminal configuration health index and collection execution record are obtained, which realizes the scientific evaluation and continuous monitoring of the configuration execution effect. This method significantly improves the configuration efficiency of the power consumption information collection terminal and reduces manual operation errors. At the same time, through the standardized configuration file format and secure transmission mechanism, it ensures the stability of the configuration process and the security of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0009] Figure 1 A schematic diagram of an embodiment of an intelligent configuration method of a power consumption information collection terminal based on big data in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of an intelligent configuration system for power consumption information collection terminals based on big data in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The embodiment of the present application provides a method and system for intelligent configuration of power consumption information collection terminals based on big data. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the intelligent configuration method of the power consumption information collection terminal based on big data includes: Step S101: classify and analyze the collected terminal business data and power consumption function data to obtain a terminal function dependency graph and a power collection feature library; Step S102: hierarchically organize and standardize the meter reading parameter information and the collection task structure in the electric energy collection feature library to obtain the configuration file format specification and communication grammar rules; Step S103: Visually arrange the terminal function dependency graph and the configuration file format specification and map the communication grammar rules to obtain a collection task management solution and a parameter configuration template; Step S104: Analyze and match the collection task management solution and the parameter configuration template through the collection pattern recognition algorithm to obtain the terminal configuration strategy and the terminal collection task file; Step S105: block encoding and encryption processing are performed on the terminal configuration strategy and the terminal acquisition task file according to the acquisition task structure to obtain the configuration operation sequence and the acquisition operation status; Step S106: Perform multi-dimensional analysis on the configuration operation sequence and the collection operation status according to the scoring criteria of the power collection feature library to obtain the terminal configuration health index and the collection execution record.

[0012] It is understandable that the execution subject of the present application can be an intelligent configuration system for power consumption information collection terminals based on big data, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0013] Specifically, the terminal business data and power consumption function data are classified and analyzed for correlation. The terminal business data includes distributed photovoltaic, orderly power consumption, demand response and low voltage monitoring data, while the power consumption function data includes the terminal's data collection frequency, data type, reporting method and other configuration information. When classifying and arranging these data, they are classified based on the data type, and the same type of data is sorted into the same data set. For example, for the collection frequency data, they are classified according to real-time data (collected every 5 minutes), daily data (collected regularly every day), and monthly data (collected regularly every month). When performing correlation analysis, the dependency relationship between the data is found through data correlation analysis, and a terminal function dependency relationship map is constructed. At the same time, the data features are extracted and stored in the power collection feature library.

[0014] The meter reading parameter information and collection task structure in the power collection feature library are hierarchically organized and standardized. The meter reading parameter information includes the collection object identifier, collection data items, collection frequency, etc. The collection task structure includes task trigger conditions, execution time, data processing methods, etc. When organizing in layers, the parameters are divided into basic configuration layer, business configuration layer, and extended configuration layer. The basic configuration layer contains necessary configurations such as communication parameters and identity authentication parameters. The business configuration layer contains special configurations for different business scenarios, and the extended configuration layer is reserved for future new services. Standardization converts parameters of different formats into standard data formats to form configuration file format specifications, and formulates corresponding communication grammar rules according to communication requirements. Visual arrangement and communication grammar rule mapping are performed based on the terminal function dependency graph and configuration file format specifications. Visual arrangement converts complex functional dependencies into an intuitive graphical interface for easy configuration management. Configure the collection task by dragging and dropping, set the task execution conditions, data processing rules, etc., and automatically convert the configuration rules into a configuration file in a standard format. Communication grammar rule mapping ensures that the configuration information can be correctly transmitted and parsed according to the specification requirements, thereby forming a complete collection task management solution and parameter configuration template. The acquisition pattern recognition algorithm is used to analyze and match the acquisition task management scheme and parameter configuration template. The algorithm first extracts task features, including acquisition frequency, data type, processing rules, etc., and then matches these features with historical configuration data to identify the most suitable configuration strategy. For example, for the acquisition task of photovoltaic power generation users, based on historical data analysis, it is found that different acquisition frequencies are required in different time periods. The acquisition frequency can be reduced in the morning and evening periods, while the acquisition frequency needs to be increased during the peak power generation period. Based on this, the optimized terminal configuration strategy and acquisition task file are generated. The terminal configuration strategy and terminal acquisition task file are block-encoded and encrypted. Block encoding divides the configuration file according to the functional module, which is convenient for step-by-step loading and updating. Each functional block contains an independent set of configuration parameters, and additional verification information is used for data integrity verification. Encryption processing uses a security algorithm to encrypt the configuration data to ensure the security of the data transmission process. The encrypted data is organized into a configuration operation sequence according to the predefined execution order, and the acquisition operation status is recorded during the execution process.

[0015] Finally, according to the scoring criteria defined in the power collection feature library, a multi-dimensional analysis is performed on the configuration operation sequence and the collection operation status. The scoring criteria include multiple dimensions such as data collection success rate, data accuracy, and communication stability. By calculating the scores of various indicators, the health status of the terminal configuration is comprehensively evaluated to generate the terminal configuration health index. At the same time, the key events in the configuration execution process are recorded to form a complete collection execution record.

[0016] Taking a distributed photovoltaic user in a certain area as an example, in actual application, the acquisition terminal first collects raw data including: photovoltaic power generation data (collected every 5 minutes), power load data (collected every 15 minutes), and power quality data (collected every hour). After data classification and sorting, a data feature set is formed, including acquisition frequency features, data type features, business type features, etc. Through association analysis, it is found that there is a significant correlation between photovoltaic power generation and power load, and the association relationship is recorded in the terminal function dependency map. Based on these characteristics and associations, the system generates targeted configuration strategies, such as increasing the collection frequency to once every 3 minutes during the peak power generation period (10:00-14:00), and reducing it to once every 30 minutes during the night period (20:00-6:00 the next day), effectively balancing data collection needs and system resource usage. During the configuration execution process, various indicators are continuously monitored. When collection abnormalities occur (such as the collection success rate is less than 95%), the collection strategy is adjusted in time to ensure the stability and reliability of data collection.

[0017] In the embodiment of the present application, by classifying and sorting the terminal business data and the power consumption function data and analyzing the association, the terminal function dependency graph and the power collection feature library are obtained, and the intelligent management of the terminal function configuration is realized, avoiding the cumbersome process of issuing parameters one by one in the traditional way. By hierarchically organizing and standardizing the meter reading parameter information and the collection task structure in the power collection feature library, the configuration file format specification and the communication grammar rules are obtained, and the standardization and maintainability of the configuration file are improved. The terminal function dependency graph and the configuration file format specification are visually arranged and mapped with communication grammar rules to obtain the collection task management scheme and the parameter configuration template, making the configuration process more intuitive and convenient. The collection task management scheme and the parameter configuration template are analyzed and matched by the collection pattern recognition algorithm to obtain the terminal configuration strategy and the terminal collection task file, thereby enhancing the intelligence of the configuration process. By block encoding and encryption of the terminal configuration strategy and the terminal collection task file according to the collection task structure, the configuration operation sequence and the collection operation status are obtained, and the security and reliability of the configuration process are guaranteed. The configuration operation sequence and collection operation status are analyzed in multiple dimensions according to the scoring criteria of the power collection feature library, and the terminal configuration health index and collection execution record are obtained, which realizes the scientific evaluation and continuous monitoring of the configuration execution effect. This method significantly improves the configuration efficiency of the power consumption information collection terminal and reduces manual operation errors. At the same time, through the standardized configuration file format and secure transmission mechanism, it ensures the stability of the configuration process and the security of the data.

[0018] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Clean and standardize the distributed photovoltaic, orderly power consumption, demand response and low voltage monitoring data in the terminal business data to obtain the business data set, and divide the data type and perform frequency analysis on the business data set to obtain the business feature vector; (2) Perform functional item analysis and attribute extraction on the electricity consumption function data to obtain a functional attribute table, and map and match the functional attribute table with the business feature vector to obtain a functional business association matrix; (3) Calculate the weight and threshold of the functional business association matrix to obtain the core functional links, and then divide and hierarchically analyze the core functional links to obtain the functional hierarchy structure; (4) Marking the dependency relationships of each function node in the function hierarchy to obtain a function dependency tag set, and constructing a dependency tree based on the function dependency tag set to obtain a terminal function dependency graph; (5) Extract characteristic parameters and label data types of nodes in the terminal function dependency graph to obtain the power collection feature library.

[0019] Specifically, the terminal business data is processed. The terminal business data includes four types of data: distributed photovoltaic data in the substation area (including power generation, voltage, current, etc.), ordered power consumption data (including power load, power consumption period, power quality, etc.), demand response data (including response time, response capacity, response effect, etc.) and low voltage monitoring data (including voltage value, duration, frequency of occurrence, etc.). In the data cleaning process, the missing values ​​of the data are first processed, and the missing data are completed by the adjacent value average filling method. For abnormal values, reasonable upper and lower limit thresholds are set for identification and processing, such as photovoltaic power generation power should not exceed the installed capacity, and the voltage value should be within the range of ±7% of the rated voltage. Standardization processing unifies data of different dimensions to the same scale, and uses the maximum and minimum value standardization method to map the data to the [0,1] interval. Through data cleaning and standardization processing, a business data set in a standard format is formed.

[0020] The data types of business data sets are divided into three categories according to data collection characteristics: real-time data, quasi-real-time data, and scheduled data. Real-time data requires a collection frequency of minutes, such as photovoltaic power data; quasi-real-time data collection frequency is at the hourly level, such as power load data; scheduled data is collected at a fixed time point, such as daily frozen power data. Frequency analysis statistics the collection frequency distribution of various types of data to form a business feature vector. At the same time, the power function data is parsed and processed. The power function data describes the functional configuration information of the terminal, including data collection function, data processing function, communication function, etc. Through functional item parsing, the key attributes of each function are extracted, such as the collection object, collection cycle, collection method, etc. of the collection function, to form a functional attribute table. The functional attribute table is mapped and matched with the business feature vector, the association relationship between the function and the business requirements is established, and the functional business association matrix is ​​generated.

[0021] The function-business association matrix is ​​weighted, and the weight value reflects the importance of the function to the business. The weight calculation rules are set to consider factors such as data collection frequency, data importance, and business priority. Through threshold screening, the associations with higher weight values ​​are retained to obtain the core function links. The core function links are grouped and divided, and the functions with strong correlation are organized into function groups. The hierarchical relationship between the function groups is determined through hierarchical analysis to establish a functional hierarchy. Dependency analysis is performed on each function node in the functional hierarchy to mark the dependency types between functions, including unidirectional dependency, bidirectional dependency, conditional dependency, etc. Dependency tags are marked with standardized marking symbols and recorded in the function dependency tag set. Based on the function dependency tag set, a tree structure is used to describe the hierarchical relationship and dependency relationship between functions, and a terminal function dependency map is constructed.

[0022] Finally, the characteristic parameters of each functional node in the terminal function dependency graph are extracted, including function type, execution conditions, data format, etc., and the data type of the node is marked. This information is organized and archived in the power acquisition feature library to provide basic data support for subsequent configuration management.

[0023] Taking a distributed photovoltaic user in a certain area as a specific example, the installed capacity of this user is 100kW, and the data to be collected include: photovoltaic power generation (once every 5 minutes), voltage and current (once every 15 minutes), electric energy (once every 1 hour), etc. During the data cleaning process, it was found that some power generation data was missing, and it was filled by the average value of the two time points before and after; for abnormal data, such as the power generation recorded at a certain moment is 120kW, which exceeds the installed capacity, it is marked as abnormal and removed. After data standardization, the data is classified according to the collection frequency: power generation belongs to real-time data (5 minutes level), voltage and current belong to quasi-real-time data (15 minutes level), and electric energy belongs to scheduled data (1 hour level). Analysis of the power consumption function configuration found that the terminal has basic collection functions, time-sharing collection functions, event recording functions, etc. Through functional business mapping, it was found that the association weight of the real-time data collection function with the power generation monitoring business is 0.9, the association weight with the voltage monitoring business is 0.7, and the association weight with the electric energy statistics business is only 0.3. Set the weight threshold to 0.6, and select power generation monitoring and voltage monitoring as core functions. In the functional hierarchy, these two functions are classified into the real-time monitoring function group, and the dependency between them is marked: voltage anomalies will affect the acquisition accuracy of power generation, so the voltage monitoring function is a dependent function of power generation monitoring. Finally, the characteristic parameters of these functional nodes are recorded in the power acquisition feature library, including acquisition frequency, data accuracy, trigger conditions and other information, to provide data support for the intelligent configuration of the terminal.

[0024] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Identify the data type and define the effective range of the meter reading parameter information in the electric energy acquisition feature library to obtain a parameter attribute table, and then classify and organize the parameter attribute table to obtain a meter reading parameter hierarchical table; (2) Extract basic elements and analyze attributes of the collection task structure to obtain a task element set, and then serialize and arrange the task element set according to the data collection process to obtain a task process chain; (3) The meter reading parameter hierarchical table is associated and mapped according to the task type to obtain a parameter mapping relationship set, and the parameter mapping relationship set and the task process chain are integrated to obtain a functional configuration structure; (4) Perform semantic description and format definition on the functional configuration structure to obtain the configuration file format specification, and perform protocol matching on the parameter transmission method in the configuration file format specification to obtain a protocol mapping table; (5) Perform communication rule extraction and grammar conversion on the protocol mapping table to obtain communication grammar rules.

[0025] Specifically, when processing the meter reading parameter information in the electric energy acquisition feature library, it is first necessary to clarify the data type identifier of the parameter. The data type identifier includes integer data (such as the number of acquisitions), floating point data (such as voltage value), string data (such as terminal address), Boolean data (such as acquisition status), etc. For different types of data, it is necessary to define their valid ranges: integer data sets the maximum and minimum values, floating point data sets the precision and range, string data sets the length limit, and Boolean data sets the value range. Based on the data type identifier and valid range information, a parameter attribute table is generated to record the attribute characteristics of each parameter. The parameter attribute table is then classified and organized, and divided into basic parameter class, business parameter class, and extended parameter class according to the purpose of the parameter. The basic parameter class includes basic configuration parameters such as terminal communication address and communication rate; the business parameter class includes business-related parameters such as acquisition time, acquisition cycle, and data item identifier; and the extended parameter class includes optional configuration parameters such as alarm threshold and statistical rules. Based on the classification, hierarchical division is performed to construct a hierarchical structure of parameters to form a hierarchical table of meter reading parameters.

[0026] The processing of the collection task structure starts with the extraction of basic elements, and the key elements of the task are extracted, including task identification, execution conditions, data items, collection rules, etc. The attributes of these elements are analyzed to determine the attribute characteristics of each element, such as the uniqueness of the task identification, the triggering method of the execution conditions, the collection method of the data items, the processing logic of the collection rules, etc. The analyzed elements are organized into a task element set, and then the task elements are arranged in the execution order according to the business process of data collection to form a complete task process chain. In the association mapping processing between the meter reading parameter hierarchical table and the task type, different types of collection tasks are first identified, such as scheduled collection tasks, event collection tasks, and on-demand collection tasks. For each task type, the relevant parameter items are screened out from the meter reading parameter hierarchical table, the corresponding relationship between the parameters and the tasks is established, and the parameter mapping relationship set is generated. The parameter mapping relationship set is integrated with the task process chain to determine the parameter configuration required for each task node to form a complete functional configuration structure. When the functional configuration structure is semantically described, a standardized description language is used to define the meaning, function and usage of each configuration item. In the format definition link, the organization form, data format, file structure, etc. of the configuration file are specified to generate the configuration file format specification. Based on the configuration file format specification, select the appropriate communication protocol for parameter transmission, establish the corresponding relationship between parameters and protocol fields, and form a protocol mapping table.

[0027] Finally, the protocol mapping table is processed to extract communication rules, including data frame format, transmission process, verification method, etc., and these rules are converted into standard communication syntax to form communication syntax rules. These rules guide the data interaction process between the terminal and the master station.

[0028] Taking the electricity consumption information collection of a photovoltaic user as an example, the parameters that the terminal needs to collect include: collection time interval (integer, range 5-60 minutes), voltage value (floating point, precision 0.1V, range 180-250V), collection address (string, length not exceeding 32 bytes), etc. The data type and valid range of these parameters are recorded in the parameter attribute table. After classification and organization, the collection time interval is classified into the collection control layer of the business parameter class, the voltage value is classified into the data item layer of the business parameter class, and the collection address is classified into the communication layer of the basic parameter class. In the collection task structure, the basic elements include: task number T001, execution condition (trigger every 5 minutes), collection data item (voltage value), processing rule (out of range record alarm), etc. These elements are arranged in the order of "task initialization → data collection → data processing → result reporting" to form a task process chain. For the timed collection task of voltage data, the collection time interval, voltage range, data accuracy and other parameters are matched from the parameter layer table to generate parameter mapping relationships. These mapping relationships are integrated into the task process to form a complete voltage data collection function configuration structure. In the semantic description, the format of the configuration file is clearly defined, such as using XML format to organize configuration information: the task node contains attribute nodes and data nodes, the attribute node records the task characteristics, and the data node records the acquisition parameters. According to the requirements of the DL / T698.45 protocol, the mapping relationship between parameters and protocol messages is established, such as the voltage value is represented by a 4-byte floating point number, and the data identifier is 0x2001. The communication grammar rules finally generated specify the data encoding method, transmission order, verification rules, etc., to ensure that the configuration information can be accurately transmitted and executed.

[0029] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Perform node distribution analysis and hierarchical relationship extraction on the terminal function dependency graph to obtain a function node hierarchy table, and perform visual layout planning on the function node hierarchy table to obtain a visual layout solution; (2) Mapping the data structure in the configuration file format specification to interface elements to obtain an interface control set, and spatially arranging the interface control set according to the visualization layout scheme to obtain an interface layout diagram; (3) Perform parameter association analysis and constraint extraction on the communication grammar rules to obtain a parameter constraint set, and perform rule conversion and verification planning on the parameter constraint set to obtain a verification rule table; (4) Bind and map the controls in the interface layout diagram and the validation rule table to obtain the interaction control rules, organize the interaction control rules into task flows, and obtain the collection task management solution; (5) Perform parameter template processing on the configuration items in the collection task management plan to obtain a parameter configuration template.

[0030] Specifically, the terminal function dependency graph is deeply analyzed. Node distribution analysis includes statistics on the connectivity, depth and breadth of each function node, while hierarchical relationship extraction is to identify the superior-subordinate relationship between nodes. For example, the data acquisition function node includes sub-nodes such as data reading, data processing, and data storage, and there is a clear execution order and dependency relationship between these nodes. The analysis results are organized into a function node hierarchy table to record the hierarchical position and association relationship of each node. Visual layout planning is carried out based on the function node hierarchy table, and the node hierarchy is displayed in a tree structure. The relationship between nodes is clearly visible through reasonable spatial distribution to form a visual layout scheme. When mapping the data structure in the configuration file format specification to interface elements, the abstract data structure needs to be converted into a specific interface control. Different types of fields in the data structure are mapped to corresponding interface controls: numeric type fields are mapped to numeric input boxes, enumeration type fields are mapped to drop-down selection boxes, Boolean type fields are mapped to radio buttons or check boxes, and text type fields are mapped to text input boxes. These interface controls constitute an interface control set, and then they are spatially arranged according to the previously obtained visual layout scheme to ensure that the position and organization of the controls are in line with user operation habits, and finally form a complete interface layout diagram.

[0031] The processing of communication grammar rules begins with parameter association analysis and constraint extraction. Analyze the association between the parameters in the communication grammar rules, such as the value of parameter A will affect the value range of parameter B, and parameter C and parameter D must be configured at the same time. Extract these constraint relationships to form a parameter constraint set. Perform rule conversion on the parameter constraint set, convert the constraint relationship into an executable validation rule, and formulate a validation execution plan, determine the validation order and validation method of the rule, and generate a validation rule table. Bind and map the controls in the interface layout diagram to the validation rule table to establish an association between the control value change and the rule validation. For example, when the value of the acquisition frequency input box is modified, the relevant validation rules are triggered to check whether the value meets the constraint conditions. These mapping relationships are recorded in the interaction control rules, and these rules are organized according to the business process to form a complete acquisition task management solution. Finally, the configuration items in the acquisition task management solution are parameter templated. Extract common configuration combinations to form configuration templates, including parameter default values, value ranges, constraints, and other information. These templates can be quickly applied to similar acquisition task configuration scenarios.

[0032] Taking the collection task configuration of a distributed photovoltaic user in a certain area as an example, the function node analysis shows that there is a dependency between the voltage data collection node and the power calculation node, because the power calculation requires voltage data as input. In the interface design, the voltage collection configuration part includes controls such as the collection interval (digital input box, the default value is 5 minutes), trigger condition (drop-down box, options include timed trigger, over-limit trigger), and alarm threshold (digital input box, range 0-100%). The validation rule defines the parameter constraints: when the over-limit trigger is selected, the alarm threshold must be set; the collection interval is limited by the terminal processing capacity and the minimum must not be less than 1 minute. These controls and rules are bound and mapped to form a complete interactive control mechanism. The configuration template presets configuration combinations such as "standard collection mode" (collection interval 5 minutes, timed trigger) and "fine collection mode" (collection interval 1 minute, over-limit trigger, alarm threshold 90%), which are convenient and quick to apply.

[0033] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Perform feature decomposition and task extraction on the collection task management plan to obtain a task feature set, and then perform data annotation and type identification on the task feature set to obtain a task type table; (2) Analyze the parameter attributes and divide the value intervals of the parameter configuration template to obtain the parameter constraint library. Then, calculate the correlation between the parameter constraint library and the task type table through the acquisition pattern recognition algorithm to obtain the task parameter matching table. (3) Integrate rules and filter conditions on the task parameter matching table to obtain a configuration rule set, and perform strategy analysis and partition organization on the configuration rule set to obtain a terminal configuration strategy; (4) Perform data format conversion and file structure organization on the task content in the terminal configuration strategy to obtain the basic task file, and perform parameter supplement and integrity check on the basic task file to obtain the terminal acquisition task file.

[0034] Specifically, the task is disassembled at the element level to extract features such as acquisition type, execution conditions, acquisition object, and data items. For example, for the voltage data acquisition task, the acquisition frequency feature (periodic acquisition), data feature (three-phase voltage), and trigger feature (timing trigger) are extracted. The extracted task feature set is labeled, and identifiers and attribute descriptions are added to each feature, such as labeling the acquisition frequency as a periodic feature and labeling the voltage data as an analog feature. Through feature type identification, the labeled features are classified according to functional attributes to form a task type table. Then, the parameter configuration template is analyzed in depth. Attribute analysis is performed on each parameter item in the template to determine the data type, value range, default value and other characteristics of the parameter. For example, the data type of the voltage acquisition threshold parameter is a floating point number, and the value range is ±7% of the rated voltage. The numerical interval is divided according to the parameter characteristics, and a parameter constraint library is established. The acquisition pattern recognition algorithm is used to process the parameter constraint library and the task type table to calculate the degree of association between the parameter and the task type. The algorithm calculates the correlation score by analyzing the frequency and importance of the parameters in different task types, and generates a task parameter matching table.

[0035] Integrate the rules of the task parameter matching table and combine the parameters with high correlation to form configuration rules. Determine the applicable conditions of the rules through condition screening, such as a set of parameter configurations suitable for high-frequency collection scenarios. Record the screened rules in the configuration rule set. Perform policy analysis on the configuration rule set, evaluate the execution effect and resource consumption of each rule, and organize them according to business scenarios to generate terminal configuration policies. Finally, process the specific task content in the terminal configuration policy. Convert the task configuration data into a standard data format, build the organizational structure of the file, and generate the basic task file. Supplement the parameters of the basic task file to ensure the completeness of the configuration items, perform integrity checks, verify the validity and consistency of the parameters, and finally form the terminal collection task file.

[0036] Taking the electric energy collection task of the substation assessment as an example, the feature decomposition extracts the collection time feature (zero o'clock every day), data item feature (positive active electric energy), data accuracy feature (0.01kWh), etc. The task type is identified as "daily frozen collection type". The parameter configuration includes the collection time point, data accuracy, storage cycle, etc. Through the correlation calculation, it is found that the correlation between the collection time point parameter and the daily frozen task type is 0.95, and the correlation between the data accuracy parameter is 0.85. The configuration rule stipulates that the collection time point must be set for daily frozen data collection and the data accuracy must not be less than 0.01kWh. In the terminal collection task file, the collection object identifier, collection time (00:00:00), data item identifier (positive active electric energy 0x00010000), data accuracy (0.01) and other configuration information are recorded, and the data integrity is ensured by CRC check.

[0037] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) The terminal configuration strategy is divided into task levels and functional groups according to the collection task structure to obtain a strategy grouping table, and the strategy grouping table is then arranged into task sequences and dependency annotations to obtain a task dependency chain; (2) Calculate the priority and schedule the time sequence of each node in the task dependency chain to obtain a time sequence task table, and define the data encoding format and perform mapping conversion on the time sequence task table to obtain an encoding rule set; (3) The coding rule set is classified and encoded and format converted according to the data type to obtain the coding task set, and the terminal collection task file is content parsed and structure split according to the coding task set to obtain the task block table; (4) Perform security level assessment and encryption strategy allocation on each data block in the task block table to obtain an encryption strategy table, and perform key generation and distribution processing on the encryption strategy table to obtain a key distribution table; (5) Encrypting the key distribution table and the task block table and generating check codes to obtain encrypted data blocks, and performing transmission grouping and sequence arrangement on the encrypted data blocks to obtain a transmission sequence table; (6) Setting the status identification and executing the sequence of the transmission sequence table to obtain the configuration operation sequence, and performing data collection monitoring and status recording on the configuration operation sequence to obtain the operation status set; (7) Update the data and synchronize the status of the running status set to obtain the collected running status.

[0038] Specifically, the terminal configuration strategy is divided into task hierarchies and functional groups. The tasks are divided into hierarchies according to the collection task structure, including the basic collection layer (such as voltage and current collection), statistical calculation layer (such as electric energy statistics), and monitoring alarm layer (such as over-limit alarm). At the same time, they are grouped according to functional characteristics, such as real-time collection group, timing collection group, event collection group, etc., to form a strategy grouping table. The tasks in the strategy grouping table are sequenced, the order of task execution is determined, and the dependencies between tasks are marked. For example, the electric energy calculation task depends on the voltage and current collection task, and a task dependency chain is generated. Priority evaluation is performed on each node in the task dependency chain, and the task priority is calculated based on factors such as task importance and timeliness requirements. The execution time of high-priority tasks is prioritized, time resources are reasonably allocated, and a time sequence task table is generated. The data encoding format of the tasks in the time sequence task table is defined, the data storage format and transmission format are specified, and the mapping relationship between data formats is established to form a set of encoding rules.

[0039] Classify the encoding rule set according to the data type, and use a unified encoding method for the same type of data, such as floating-point data encoded using the IEEE754 standard and integer data represented by the complement code. Organize the encoded tasks into an encoding task set, and then parse the terminal acquisition task file, perform structured splitting of the file content according to the format requirements of the encoding task set, and generate a task block table. Evaluate the security level of the data blocks in the task block table, determine the security protection level according to the importance and sensitivity of the data, and assign corresponding encryption strategies to data blocks of different security levels to form an encryption strategy table. Generate encryption keys based on the encryption strategy, formulate a key distribution plan, and record it in the key distribution table.

[0040] The data in the key distribution table and task block table are encrypted, and a data checksum is generated to ensure the integrity of the data, forming an encrypted data block. The encrypted data blocks are grouped according to the transmission requirements, the transmission order is arranged, and a transmission sequence table is generated. A status identifier is set for each data packet in the transmission sequence table to track the transmission process, and an execution sequence is generated to guide the data transmission process, forming a configuration operation sequence. During the execution process, the data collection situation is monitored in real time, the status information during the collection process is recorded, and an operation status set is generated.

[0041] Finally, the data in the running status set is updated to ensure the timeliness of the status information, and the status is synchronized between the terminal and the main station to obtain the final collection running status.

[0042] Taking the electric energy collection task of a certain substation as an example, in the task hierarchy division, it is divided into three levels: voltage and current collection (basic layer), electric energy calculation (calculation layer), and over-limit alarm (alarm layer). The task dependency chain shows that electric energy calculation depends on voltage and current data, and over-limit alarm depends on electric energy data. In terms of timing arrangement, voltage and current are collected once every 1 minute, electric energy is calculated once every 15 minutes, and data encoding uses IEEE754 standard to store floating point numbers. Data security level assessment results: electric energy data is at a high level and is encrypted using AES-256; voltage and current data is at a medium level and is encrypted using AES-128. During the execution process, each collection cycle generates a status record, which contains information such as collection time, collection results, and data quality. Through status synchronization, the master station is ensured to grasp the real-time operation status of the terminal.

[0043] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Extract and standardize the scoring indicators of the power collection feature library to obtain a scoring indicator system, and classify and organize the scoring indicator system according to the evaluation dimensions to obtain a multi-dimensional evaluation table; (2) Extract parameters and annotate the status of the execution data in the configuration run sequence to obtain a sequence status table, and then map the indicators of the sequence status table according to the multidimensional evaluation table to obtain a sequence evaluation set; (3) Perform data statistics and performance calculation on the collected operating status to obtain an operating index table, and perform data normalization on the operating index table according to the multi-dimensional evaluation table to obtain a status evaluation set; (4) Perform weight assignment and score calculation on the sequence evaluation set and the state evaluation set to obtain a score result table, and perform threshold analysis and grade division on the score result table to obtain a health level table; (5) Extract indicators and integrate data from the health level table to obtain the terminal configuration health indicator, and merge and time-mark the sequence status table and operation indicator table to obtain the execution log table; (6) Screen the execution log table for data and extract key information to obtain a key event table, and then classify the key event table by state and organize it by time sequence to obtain an event sequence table; (7) The event sequence table is classified, summarized and recorded according to business type to obtain the collection and execution record.

[0044] Specifically, scoring indicators are extracted from the power collection feature library. Scoring indicators include data collection success rate (successful collection times / total collection times), data accuracy (effective data volume / total data volume), communication stability (communication success rate), task execution timeliness (actual execution time / planned execution time), etc. These indicators are standardized, and indicators of different dimensions are unified into a score range of 0-100 to form a standardized scoring indicator system. Indicators are classified according to evaluation dimensions, including data quality dimension, communication performance dimension, and task execution dimension, and a multidimensional evaluation table is generated. Execution data is extracted from the configuration operation sequence, including parameters such as task execution time, data collection volume, and communication status, and the execution status (success, failure, timeout, etc.) is marked to form a sequence status table. The execution data in the sequence status table is mapped according to the indicator requirements of the multidimensional evaluation table, such as mapping the acquisition success mark to the data acquisition success rate indicator to obtain a sequence evaluation set.

[0045] Perform statistical analysis on the collected operation status data, calculate various performance indicators, such as the data collection success rate within 15 minutes, the communication stability within 24 hours, etc., and record them in the operation indicator table. Normalize the data in the operation indicator table according to the standards of the multidimensional evaluation table, unify the quantitative scale, and obtain the status evaluation set. Assign weights to the sequence evaluation set and the status evaluation set, set the weight value according to the importance of the indicator, calculate the comprehensive score, and record it in the score result table. Through threshold analysis, the score is divided into different levels, such as excellent (90-100 points), good (80-89 points), general (60-79 points), and poor (below 60 points), forming a health level table.

[0046] Extract key indicators from the health level table and integrate the multi-dimensional evaluation results into terminal configuration health indicators. At the same time, merge the records of the sequence status table and the operation indicator table, add timestamp information, and generate an execution log table. Filter the data in the execution log table, extract key information such as abnormal events and important status changes, and form a key event table. Classify key events according to status type and organize them in chronological order to generate an event sequence table.

[0047] Finally, the event sequence table is classified and summarized according to the business type and organized into a standardized collection execution record. Take the electric energy collection task of a certain area as an example: within a collection cycle (24 hours), the data collection success rate is 98.5% (collection success times 2370 / total collection times 2400), the data accuracy rate is 99.2% (valid data 2351 / total data 2370), and the communication stability is 97.8% (communication success 2345 / total communication times 2400). After standardization of these raw data, the scores are: data collection success rate 98.5 points, data accuracy 99.2 points, and communication stability 97.8 points. Set the weights: data collection success rate 0.4, data accuracy 0.4, communication stability 0.2, and calculate the comprehensive score 98.44 points, corresponding to the health level of "excellent". An abnormal event was found in the execution record: a certain collection data transmission timed out, occurred at 2024-01-15 10:30:25, lasted for 30 seconds, affected 5 collection points, and was restored to normal through retransmission. This information is recorded in the collection execution record for subsequent operation and maintenance analysis and optimization.

[0048] The above describes the intelligent configuration method of the power consumption information collection terminal based on big data in the embodiment of the present application. The following describes the intelligent configuration system of the power consumption information collection terminal based on big data in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the intelligent configuration system of the power consumption information collection terminal based on big data includes: The collection module 201 is used to classify and analyze the collected terminal business data and power consumption function data to obtain a terminal function dependency graph and a power collection feature library; An organization module 202 is used to organize and standardize the meter reading parameter information and the collection task structure in the electric energy collection feature library in a hierarchical manner to obtain a configuration file format specification and a communication grammar rule; The arrangement module 203 is used to visually arrange the terminal function dependency graph and the configuration file format specification and map the communication grammar rules to obtain a collection task management solution and a parameter configuration template; A matching module 204 is used to analyze and match the acquisition task management solution and the parameter configuration template through an acquisition pattern recognition algorithm to obtain a terminal configuration strategy and a terminal acquisition task file; The encryption module 205 is used to perform block encoding and encryption processing on the terminal configuration policy and the terminal acquisition task file according to the acquisition task structure to obtain a configuration operation sequence and an acquisition operation status; The analysis module 206 is used to perform multi-dimensional analysis on the configuration operation sequence and the collection operation status according to the scoring standard of the power collection feature library to obtain the terminal configuration health index and the collection execution record.

[0049] Through the collaboration of the above components, the terminal function dependency graph and the power collection feature library are obtained by classifying and analyzing the collection terminal business data and power function data, realizing the intelligent management of terminal function configuration and avoiding the cumbersome process of traditional parameter issuance one by one. By hierarchically organizing and standardizing the meter reading parameter information and the collection task structure in the power collection feature library, the configuration file format specification and communication grammar rules are obtained, which improves the standardization and maintainability of the configuration file. The terminal function dependency graph and the configuration file format specification are visually arranged and mapped with communication grammar rules to obtain the collection task management plan and parameter configuration template, making the configuration process more intuitive and convenient. The collection pattern recognition algorithm is used to analyze and match the collection task management plan and the parameter configuration template to obtain the terminal configuration strategy and the terminal collection task file, which enhances the intelligence of the configuration process. By block encoding and encryption of the terminal configuration strategy and the terminal collection task file according to the collection task structure, the configuration operation sequence and the collection operation status are obtained, ensuring the security and reliability of the configuration process. The configuration operation sequence and collection operation status are analyzed in multiple dimensions according to the scoring criteria of the power collection feature library, and the terminal configuration health index and collection execution record are obtained, which realizes the scientific evaluation and continuous monitoring of the configuration execution effect. This method significantly improves the configuration efficiency of the power consumption information collection terminal and reduces manual operation errors. At the same time, through the standardized configuration file format and secure transmission mechanism, it ensures the stability of the configuration process and the security of the data.

[0050] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application 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 application.

Claims

1. A method for intelligent configuration of power consumption information collection terminal based on big data, characterized in that: The intelligent configuration method of the power consumption information collection terminal based on big data includes: Classify and analyze the terminal business data and power consumption function data, and obtain the terminal function dependency graph and power collection feature library; The meter reading parameter information and the collection task structure in the electric energy collection feature library are hierarchically organized and standardized to obtain the configuration file format specification and communication grammar rules; Visually arrange the terminal function dependency graph and the configuration file format specification and map the communication grammar rules to obtain a collection task management solution and a parameter configuration template; Analyze and match the acquisition task management solution and the parameter configuration template through an acquisition pattern recognition algorithm to obtain a terminal configuration strategy and a terminal acquisition task file; The terminal configuration strategy and the terminal acquisition task file are block-coded and encrypted according to the acquisition task structure to obtain a configuration operation sequence and an acquisition operation status; The configuration operation sequence and the collection operation status are analyzed in multiple dimensions according to the scoring criteria of the electric energy collection feature library to obtain terminal configuration health indicators and collection execution records.

2. The intelligent configuration method of the power consumption information collection terminal based on big data according to claim 1 is characterized in that: The terminal service data and power consumption function data are classified and analyzed for correlation to obtain a terminal function dependency graph and a power collection feature library, including: Performing data cleaning and standardization on the distributed photovoltaic, orderly power consumption, demand response and low voltage monitoring data in the acquisition terminal business data to obtain a business data set, and performing data type classification and frequency analysis on the business data set to obtain a business feature vector; Performing function item parsing and attribute extraction on the power consumption function data to obtain a function attribute table, and mapping and matching the function attribute table and the service feature vector to obtain a function service association matrix; Performing weight calculation and threshold screening on the functional business association matrix to obtain core functional links, and performing grouping and hierarchical analysis on the core functional links to obtain a functional hierarchical structure; Marking the dependency relationship of each function node in the function hierarchy to obtain a function dependency marking set, and constructing a dependency tree according to the function dependency marking set to obtain the terminal function dependency graph; Feature parameters are extracted and data types are labeled for nodes in the terminal function dependency graph to obtain the electric energy collection feature library.

3. The intelligent configuration method of the power consumption information collection terminal based on big data according to claim 1 is characterized in that: The meter reading parameter information and the collection task structure in the electric energy collection feature library are hierarchically organized and standardized to obtain the configuration file format specification and communication grammar rules, including: The meter reading parameter information in the electric energy acquisition feature library is identified by data type and defined by effective range to obtain a parameter attribute table, and the parameter attribute table is classified and organized and hierarchically divided to obtain a meter reading parameter hierarchical table; Extracting basic elements and analyzing attributes of the acquisition task structure to obtain a task element set, and serializing and arranging the task element set according to the data acquisition process to obtain a task process chain; The meter reading parameter hierarchical table is associated and mapped according to the task type to obtain a parameter mapping relationship set, and the parameter mapping relationship set and the task process chain are integrated to obtain a functional configuration structure; Performing semantic description and format definition on the functional configuration structure to obtain the configuration file format specification, and performing protocol matching on the parameter transmission mode in the configuration file format specification to obtain a protocol mapping table; The communication rule extraction and grammar conversion are performed on the protocol mapping table to obtain the communication grammar rule.

4. The intelligent configuration method of the power consumption information collection terminal based on big data according to claim 1 is characterized in that: The visual arrangement of the terminal function dependency graph and the configuration file format specification and the mapping of the communication grammar rules to obtain the collection task management solution and parameter configuration template include: Performing node distribution analysis and hierarchical relationship extraction on the terminal function dependency graph to obtain a function node hierarchy table, and performing visual layout planning on the function node hierarchy table to obtain a visual layout solution; Performing interface element mapping on the data structure in the configuration file format specification to obtain an interface control set, and spatially arranging the interface control set according to the visualization layout scheme to obtain an interface layout diagram; Performing parameter association analysis and constraint extraction on the communication grammar rules to obtain a parameter constraint set, and performing rule conversion and verification planning on the parameter constraint set to obtain a verification rule table; Binding and mapping the controls in the interface layout diagram and the verification rule table to obtain interaction control rules, and organizing the interaction control rules into task flows to obtain the acquisition task management solution; The configuration items in the acquisition task management solution are subjected to parameter template processing to obtain the parameter configuration template.

5. The intelligent configuration method of the power consumption information collection terminal based on big data according to claim 1 is characterized in that: The acquisition task management scheme and the parameter configuration template are analyzed and matched by the acquisition pattern recognition algorithm to obtain the terminal configuration strategy and the terminal acquisition task file, including: Performing feature decomposition and task extraction on the acquisition task management plan to obtain a task feature set, and performing data annotation and type identification on the task feature set to obtain a task type table; Performing parameter attribute analysis and value interval division on the parameter configuration template to obtain a parameter constraint library, and calculating the correlation between the parameter constraint library and the task type table through an acquisition pattern recognition algorithm to obtain a task parameter matching table; Performing rule integration and condition screening on the task parameter matching table to obtain a configuration rule set, and performing strategy analysis and partition organization on the configuration rule set to obtain the terminal configuration strategy; The task content in the terminal configuration strategy is converted into a data format and organized into a file structure to obtain a basic task file, and the basic task file is supplemented with parameters and integrity checked to obtain the terminal acquisition task file.

6. The intelligent configuration method of the power consumption information collection terminal based on big data according to claim 1 is characterized in that: The terminal configuration strategy and the terminal acquisition task file are subjected to block encoding and encryption processing according to the acquisition task structure to obtain a configuration operation sequence and an acquisition operation status, including: The terminal configuration strategy is divided into task levels and functional groups according to the acquisition task structure to obtain a strategy grouping table, and the strategy grouping table is arranged into task sequences and dependency annotations to obtain a task dependency chain; Calculating the priority and scheduling the timing of each node in the task dependency chain to obtain a timing task table, and performing data encoding format definition and mapping conversion on the timing task table to obtain an encoding rule set; The coding rule set is classified and encoded and format converted according to data types to obtain a coding task set, and the terminal acquisition task file is content parsed and structure split according to the coding task set to obtain a task block table; Performing security level assessment and encryption strategy allocation on each data block in the task block table to obtain an encryption strategy table, and performing key generation and distribution processing on the encryption strategy table to obtain a key distribution table; Encrypting the key distribution table and the task block table and generating check codes to obtain encrypted data blocks, and performing transmission grouping and sequence arrangement on the encrypted data blocks to obtain a transmission sequence table; Setting a state identifier and generating an execution sequence for the transmission sequence table to obtain the configuration operation sequence, and performing data collection monitoring and state recording on the configuration operation sequence to obtain an operation state set; Data updating and state synchronization processing are performed on the operation state set to obtain the acquisition operation state.

7. The intelligent configuration method of the power consumption information collection terminal based on big data according to claim 1 is characterized in that: The multi-dimensional analysis of the configuration operation sequence and the collection operation status according to the scoring criteria of the electric energy collection feature library is performed to obtain the terminal configuration health index and the collection execution record, including: Extracting and standardizing the scoring indexes of the electric energy collection feature library to obtain a scoring index system, and classifying and organizing the scoring index system according to evaluation dimensions to obtain a multi-dimensional evaluation table; Extracting parameters and marking states of execution data in the configuration operation sequence to obtain a sequence state table, and mapping indicators of the sequence state table according to the multidimensional evaluation table to obtain a sequence evaluation set; Performing data statistics and performance calculation on the collected operation status to obtain an operation index table, and performing data normalization processing on the operation index table according to the multidimensional evaluation table to obtain a state evaluation set; Performing weight assignment and score calculation on the sequence evaluation set and the state evaluation set to obtain a score result table, and performing threshold analysis and grade division on the score result table to obtain a health level table; Extracting indicators and integrating data from the health level table to obtain the terminal configuration health indicator, and merging and time-marking the sequence status table and the operation indicator table to obtain an execution log table; Screening the execution log table for data and extracting key information to obtain a key event table, and classifying the key event table by state and organizing the time sequence to obtain an event sequence table; The event sequence table is classified, summarized and recorded according to business types to obtain the collection execution record.

8. An intelligent configuration system for power consumption information collection terminals based on big data, used to implement the intelligent configuration method for power consumption information collection terminals based on big data as described in any one of claims 1 to 7, characterized in that: The intelligent configuration system of the electricity consumption information collection terminal based on big data includes: The collection module is used to classify and analyze the collected terminal business data and power consumption function data, and obtain the terminal function dependency graph and power collection feature library; An organization module, used for hierarchically organizing and standardizing the meter reading parameter information and the collection task structure in the electric energy collection feature library to obtain configuration file format specifications and communication grammar rules; An orchestration module, used to perform visual orchestration on the terminal function dependency graph and the configuration file format specification and map the communication grammar rules to obtain a collection task management solution and a parameter configuration template; A matching module, used to analyze and match the acquisition task management scheme and the parameter configuration template through an acquisition pattern recognition algorithm to obtain a terminal configuration strategy and a terminal acquisition task file; An encryption module, used for performing block encoding and encryption processing on the terminal configuration strategy and the terminal acquisition task file according to the acquisition task structure to obtain a configuration operation sequence and an acquisition operation status; The analysis module is used to perform multi-dimensional analysis on the configuration operation sequence and the collection operation status according to the scoring criteria of the power collection feature library to obtain the terminal configuration health index and the collection execution record.

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