A distribution box data acquisition automation control system

Abstract

This invention discloses an automated control system for data acquisition from distribution boxes, belonging to the field of distribution box control technology. The system categorizes distribution boxes based on their information, resulting in several distribution box classifications. It then dynamically adjusts the classification of each distribution box within a given category, resulting in dynamic classifications. Furthermore, it performs collaborative verification on each distribution box within a dynamic classification, obtaining collaborative verification results. Real-time data acquisition and prediction are performed on each distribution box, obtaining the predicted acquisition data and its reliability for each box, and assigning corresponding reliability tags to the predicted acquisition data. The system analyzes the acquired data or predicted acquisition data to obtain fault analysis results, generates corresponding control signals based on the fault analysis results, and performs control processing based on the control information. Finally, the system performs real-time data acquisition from the distribution boxes, obtaining the acquired data; performs real-time transmission analysis to obtain transmission information, and manages the transmission of the acquired data based on the transmission information.

Description

Technical Field

[0001] This invention belongs to the field of distribution box control technology, specifically an automated control system for distribution box data acquisition. Background Technology

[0002] In the field of power supply and distribution, distribution boxes, as critical infrastructure, undertake core functions such as power distribution, circuit protection, and equipment control. The stability and reliability of their operation directly affect the safety and efficiency of the entire power system. Traditional distribution box management mainly relies on regular manual inspections and manual data recording. This method not only consumes a lot of manpower and resources but is also limited by the inspection cycle and personnel experience, making it difficult to capture subtle changes in equipment operation and potential faults in real time. For example, problems such as aging wiring, poor contact, or overload operation within the distribution box are often difficult to detect in time before they cause obvious faults (such as short circuits or fires), leading to delayed maintenance. This can not only cause equipment damage but also power outages, disrupting production and daily life, and even threatening personnel safety. With the rapid development of the Internet of Things, sensor technology, and communication technology, the power industry's demand for intelligent and automated distribution box management is becoming increasingly urgent.

[0003] In order to solve the above problems, the present invention provides an automated control system for data acquisition of distribution boxes. Summary of the Invention

[0004] To address the problems of the above solutions, this invention provides an automated control system for data acquisition in distribution boxes.

[0005] The objective of this invention can be achieved through the following technical solutions: An automated control system for data acquisition from a distribution box includes a platform and an equipment. The platform includes a device analysis module, a transmission analysis module, a platform analysis module, and a prediction module; The equipment analysis module is used to analyze each distribution box, obtain information about each distribution box, classify each distribution box according to the information, and obtain several distribution box classifications. The distribution boxes within each category are dynamically classified and adjusted to obtain dynamic classifications; collaborative verification is performed on each distribution box within the dynamic classification to obtain collaborative verification results; and corresponding processing is carried out based on the collaborative verification results.

[0006] Furthermore, the distribution boxes are categorized based on their information, including: Obtain the various power distribution backgrounds of each distribution box at its current location, summarize the power distribution backgrounds corresponding to each distribution box, and obtain the background set of each distribution box. Group the distribution boxes with the same background set into one category to obtain several initial categories; obtain the historical power distribution data of each distribution box under the corresponding power distribution background; extract the power distribution result data of the distribution box under the power distribution background based on the historical power distribution data; summarize the power distribution result data of the distribution box under each power distribution background to obtain the result set data of the distribution box. The distribution boxes within the initial classification are evaluated based on the data from each result set to obtain the classification evaluation results between the distribution boxes. The classification evaluation results include results of the same category and results of different categories. All distribution boxes that have the same classification evaluation result within the initial classification are grouped into one category to obtain the classification of each distribution box.

[0007] Furthermore, each distribution box within the initial category is evaluated based on the data from each result set, including: Label the distribution background corresponding to the initial classification as i, i = 1, 2, ..., n, where n is the number of distribution backgrounds corresponding to the initial classification; label the distribution result data in the result set data as p. i The result set data is then U = {p1, p2, p3, ..., p...} n}; Establish an outcome evaluation model, the expression of which is: ; In the formula: (U c U r ) represents the input data, U c and U r These represent the result sets of data from the two distribution boxes being evaluated; U c ⇔U r This indicates that the result sets of data from the two distribution boxes are considered equivalent, which is equivalent to p. 1c ⇔p 1r p 2c ⇔p 2r p 3c ⇔p 3r ... p nc ⇔p nr The output data is the result evaluation value PG (U). c U r The result evaluation value is 1 or 0; The result set data of the two corresponding distribution boxes in the initial classification is integrated into the input data and input into the result evaluation model for analysis to obtain the result evaluation value between the corresponding distribution boxes. When the evaluation value of the result is 1, the classification evaluation result is that the result belongs to the same category; When the evaluation value of the result is 0, the classification evaluation result is that the result is not in the same category.

[0008] Furthermore, the distribution boxes within each category are dynamically categorized and adjusted, including: The power distribution background of each power distribution box within the power distribution box category is determined in real time. Power distribution boxes with the same power distribution background within the power distribution box category are grouped together and marked as dynamic classification.

[0009] The prediction module is used to perform real-time data acquisition and prediction for each distribution box, obtain the acquisition and prediction data and credibility of each distribution box, mark the acquisition and prediction data with corresponding credibility tags, and send the acquisition and prediction data to the platform analysis module.

[0010] The platform analysis module is used to analyze the received collected data or collected prediction data, obtain corresponding fault analysis results, generate corresponding control signals based on the fault analysis results, and send the control signals to the control module on the device side.

[0011] The transmission analysis module is used to perform real-time transmission analysis, obtain various analysis requirements, identify the data analysis range corresponding to each analysis requirement, and the data analysis range is composed of various range data items; perform real-time transmission analysis on each analysis requirement to obtain the real-time transmission time of each range data item; generate transmission information based on the real-time transmission time of each range data item, and send the transmission information to the transmission module on the device side.

[0012] Furthermore, real-time transmission and analysis are performed on various analytical requirements, including: Real-time acquisition of the timeliness requirements of various analysis needs, identification of the range data items corresponding to each data analysis scope; dynamic generation of the spare time axis for each range data item based on the timeliness requirements; Based on the available time axis of each data item in each range, real-time transmission analysis is performed with the highest transmission economy as the benchmark to obtain the real-time transmission time of each data item in each range.

[0013] Furthermore, the timeliness requirements for corresponding analysis needs are adjusted based on the reliability of the collected and predicted data.

[0014] Furthermore, the impact prediction data of each range of data items is estimated in real time, and the data transmission of each range of data items is analyzed in real time based on the impact prediction data to determine whether the data transmission meets the requirements and obtain the transmission prediction results; the idle time axis is adjusted based on the transmission impact results.

[0015] Furthermore, transmission information is generated based on the real-time transmission time of each range of data items, including: Identify each real-time transmission time and integrate the data items that reach the real-time transmission time range into a data transmission range; Identify the data collection range corresponding to the collected data, and subtract the data transmission range from the data collection range to obtain the data storage range; Transmission information is generated based on the data transmission range and data storage range.

[0016] The device includes a data acquisition module, a transmission module, and a control module; The acquisition module is used to acquire data from the distribution box in real time and obtain the acquired data.

[0017] The transmission module is used to manage the transmission of collected data according to the received transmission information, identify the data transmission range and data storage range in real time according to the transmission information, summarize the collected data corresponding to the current time and the stored collected data according to the data transmission range and send them to the platform analysis module on the platform side, and store the collected data corresponding to the data storage range.

[0018] The control module is used to perform control processing based on the received control information.

[0019] Compared with the prior art, the beneficial effects of the present invention are: The automated data acquisition control system for distribution boxes provided by this invention brings significant changes and improvements to the power supply and distribution field. It abandons the traditional management method that relies on regular manual inspections and manual data recording, effectively solving the problems of traditional methods that consume a lot of manpower and resources and are difficult to capture subtle changes in equipment operation and potential faults in real time due to limitations in inspection cycles and personnel experience. Leveraging advanced IoT, sensor, and communication technologies, this system can collect various operating data from distribution boxes in real time and accurately. Potential problems such as aging lines, poor contact, or overload operation can be detected in time before they cause obvious faults such as short circuits or fires. This greatly shortens the fault detection time, enabling timely maintenance and avoiding equipment damage caused by delayed maintenance. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a block diagram illustrating the principle of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] like Figure 1 As shown, an automated control system for data acquisition in a power distribution box includes a platform and an equipment. The platform and each device typically use a communication connection.

[0024] The platform includes a device analysis module, a transmission analysis module, a platform analysis module, and a prediction module; The equipment analysis module analyzes the various distribution boxes that need to be managed on the platform, obtaining information such as model, purpose, application scenario, and operating mode. Based on this information, the module categorizes the distribution boxes into several categories. Distribution boxes belonging to the same category can be mutually referenced, meaning that under the same conditions, the influence between two distribution boxes is the same or the error is within acceptable limits. For example, under certain background conditions, the current, voltage, and temperature parameters of two distribution boxes are the same or the error is within acceptable limits. The module also dynamically adjusts the classification of distribution boxes within each category, resulting in dynamic classifications. Because differences in power distribution backgrounds mean that not all distribution boxes within a category are in the same category in real time, dynamic adjustments are needed based on the power distribution background, resulting in a single distribution box category being further divided into multiple dynamic classifications. Collaborative verification is performed on each distribution box within the dynamic classification to obtain the collaborative verification results. That is, when all distribution boxes are functioning normally, the power distribution data of each distribution box within the dynamic classification should be considered identical. If they cannot be considered identical, an anomaly is identified. This anomalies can be investigated to determine if they are caused by equipment malfunctions, leading to abnormal data analysis results. Examples include malfunctions in the data acquisition equipment or the analysis process. Specific data acquisition and judgment are based on the actual power distribution data of each distribution box. Appropriate processing is then performed based on the collaborative verification results. For example, if the collaborative verification is normal, no processing is performed; if the collaborative verification is abnormal, an early warning is issued.

[0025] In one embodiment, distribution boxes are classified according to their information, based on existing classification algorithms, clustering algorithms, etc. For example, classification criteria are constructed from four dimensions: equipment characteristics, operating environment, data quality, and management strategy, to ensure that the distribution boxes are highly consistent in key features. Equipment consistency requires that the hardware configurations of distribution boxes be homogeneous. This includes ensuring that the models, specifications, and installation locations of core components such as sensors, circuit breakers, and communication modules are completely identical or belong to the same product series, with performance parameter errors not exceeding 5%. At the same time, the software and control logic must be compatible, and the firmware version and control algorithm (such as reactive power compensation strategy and overload protection threshold) must be the same or be compatible upgrade versions to avoid data distribution deviations due to logic differences. The aging of the equipment must be similar, with the difference in operating years not exceeding 20% ​​of the expected lifespan of the distribution box.

[0026] Operating environment similarity emphasizes consistency in geographical and climatic conditions. The installation altitude, temperature range, and humidity level of the distribution box should be close to those of similar distribution boxes. Electromagnetic interference levels should also be matched. If the distribution box is located in a strong electromagnetic field environment (such as near a substation), similar distribution boxes should have similar interference records to ensure that the model can adapt to noise data. Spatial layout and load density should be similar. For example, the power supply radius and load type distribution of the distribution box (such as the proportion of industrial motors and commercial lighting) should be consistent with those of similar distribution boxes to avoid differences in voltage drop and harmonic distribution due to differences in topology.

[0027] Data quality and reliability requirements stipulate that the historical data of the distribution box must be complete and free from systematic deviations. The data collection frequency (e.g., once every 15 minutes) must be consistent with that of similar distribution boxes. The proportion of missing data should not exceed 5%, and missing periods should be filled in by interpolation or data from adjacent periods. Outliers must be handled reasonably. For example, erroneous readings caused by sensor failures should be removed by threshold filtering or statistical methods to avoid contaminating the training set. The data distribution must cover the target scenario. If the distribution box experiences seasonal load fluctuations (e.g., a surge in air conditioning load in summer), the historical data of the distribution box must include the complete annual cycle, and the proportion of data from each season should not deviate from the target scenario by more than 10%.

[0028] The coordination of management strategies emphasizes the uniformity of maintenance cycles and operating procedures. The inspection frequency and maintenance records (such as the number of circuit breaker opening and closing times and sensor calibration cycles) of distribution boxes should be similar to those of similar distribution boxes to avoid differences in equipment reliability due to maintenance differences. Control strategies must be consistent. If the distribution box uses dynamic voltage regulation (such as DVR devices), similar distribution boxes must have the same control logic to ensure that the model can learn the optimization strategy. Energy management objectives must be matched. For example, if the distribution box and similar distribution boxes both serve energy-saving optimization scenarios (such as peak-valley electricity price arbitrage), their historical data indicators such as power factor and load factor should reflect similar management requirements.

[0029] Intelligent models can also be built based on machine learning and deep learning algorithms to determine whether two distribution cabinets meet the classification criteria.

[0030] In one embodiment, the distribution boxes are classified according to their information, including: The system acquires various power distribution backgrounds that each distribution box may have at its current location, such as environmental and power backgrounds. This data is used to analyze the differences in power distribution result data between different distribution boxes under the same background, and is summarized to form a background set corresponding to each distribution box. If the power distribution backgrounds are the same or similar, it means that the input conditions for the distribution boxes are the same or similar. Whether a distribution box belongs to the same category is determined based on whether its output results (power distribution result data) are the same or similar. Output results, such as output voltage, output current, power, electrical energy, equipment temperature, humidity, and switch status, are included in the power distribution background as input data that affects the output results. In other words, the power distribution background (input data) can be determined based on data that influences the output results. Specifically, the platform provider sets the power distribution background and the corresponding data items for the distribution box result data according to user needs.

[0031] Group the distribution boxes with the same background set into one category to obtain several initial categories; obtain the historical power distribution data of each distribution box under the corresponding power distribution background; extract the power distribution result data of the distribution box under the power distribution background based on the historical power distribution data; summarize the power distribution result data of the distribution box under each power distribution background to obtain the result set data of the distribution box. Each distribution box in the initial classification is evaluated based on the data of each result set. The evaluation determines whether they meet the result classification criteria and obtains the classification evaluation results, which include results of the same category and results of different categories. All distribution boxes that have the same classification evaluation result within the initial classification are grouped into one category to obtain the classification of each distribution box.

[0032] In one embodiment, each distribution box within the initial category is evaluated based on the data of each result set. The evaluation assesses whether the distribution result data of the distribution boxes are the same or the error is within the allowable range under the same distribution background. When all distribution backgrounds are the same or the error is within the allowable range, the classification evaluation result is that the results are of the same category; otherwise, the classification evaluation result is that the results are of different categories. Based on the above evaluation method, various existing technologies can be used for evaluation, such as machine learning, deep learning algorithms, etc.

[0033] In one embodiment, the evaluation of each distribution box within the initial category is performed based on the data from each result set, including: Label the distribution background corresponding to the initial classification as i, i = 1, 2, ..., n, where n is the number of distribution backgrounds corresponding to the initial classification; label the distribution result data in the result set data as p. i The result set data is then U = {p1, p2, p3, ..., p...} n}; Establish an outcome evaluation model, the expression of which is: ; In the formula: (U c U r ) represents the input data, U c and U r These represent the result sets of data from the two distribution boxes being evaluated; U c ⇔U r This indicates that the result sets of data from the two distribution boxes are considered equivalent, which is equivalent to p. 1c ⇔p 1r p 2c ⇔p 2r p 3c ⇔p 3r ... p nc ⇔p nr Training can be performed using a labeled training set based on relevant historical data, or the permissible similarity between data points can be directly set to determine whether they are considered equivalent; the output data is the result evaluation value PG(U). c U r The result evaluation value is 1 or 0; The result set data of the two corresponding distribution boxes in the initial classification is integrated into the input data and input into the result evaluation model for analysis to obtain the result evaluation value between the corresponding distribution boxes. When the evaluation value of the result is 1, the classification evaluation result is that the result belongs to the same category; When the evaluation value of the result is 0, the classification evaluation result is that the result is not in the same category.

[0034] In one embodiment, dynamic classification adjustment of each distribution box within a distribution box category includes: The power distribution background of each power distribution box within the power distribution box category is determined in real time. Power distribution boxes with the same power distribution background within the power distribution box category are grouped together and marked as dynamic classification.

[0035] The prediction module is used to collect and predict data from each distribution box in real time, obtain the collected prediction data and its credibility, mark the collected prediction data with corresponding credibility tags, and send the collected prediction data to the platform analysis module.

[0036] In one embodiment, the data collected by the distribution box in the future is predicted in real time based on existing and historical data, and marked as predicted data. The credibility of the predicted data is marked according to the prediction accuracy of different time periods in the future. A prediction model is built using existing prediction and credibility techniques, and the prediction model is used for prediction. The prediction accuracy of the current predicted data can also be analyzed based on the prediction accuracy under different conditions during training and the prediction accuracy after application, and then the credibility of different predicted data can be marked.

[0037] The platform analysis module is used to analyze the received collected data or collected prediction data, obtain corresponding fault analysis results, generate corresponding control signals based on the fault analysis results, and send the control signals to the control module on the device side.

[0038] In one embodiment, the received collected data or collected prediction data is analyzed based on existing analysis methods, such as intelligent analysis based on large models, AI and other intelligent technologies, to obtain fault analysis results; alternatively, existing mature fault analysis models can be used for analysis.

[0039] In one embodiment, a corresponding control signal is generated based on the fault analysis results, and the corresponding handling measures for different fault conditions are determined according to user needs, such as automatically cutting off the power supply, alarming the management personnel, resetting the switch, etc. The control signal is generated specifically according to the preset management strategy, linkage method, etc.

[0040] The transmission analysis module is used to perform real-time transmission analysis, obtain various analysis requirements, such as various fault diagnosis requirements, monitoring requirements, etc.; identify the data analysis range corresponding to each analysis requirement, that is, what data needs to be collected in order to realize the analysis of the analysis requirement, the data analysis range is composed of various range data items; perform real-time transmission analysis on each analysis requirement to obtain the real-time transmission time of each range data item; generate transmission information based on the real-time transmission time of each range data item, and send the transmission information to the transmission module on the device side.

[0041] In one embodiment, real-time transmission and analysis of various analytical requirements includes: The system acquires the timeliness requirements of each analysis need in real time, identifies the data items corresponding to each data analysis range, and marks them as range data items. Based on the timeliness requirements, it dynamically generates the spare time axis for each range data item. That is, it determines the time elapsed since the last analysis of each analysis need, and then determines the spare time for each analysis need for each range data item in real time based on the timeliness requirements. The spare time axis is generated based on the minimum value of one or more spare times corresponding to the range data item, which is used to intuitively represent the spare time of the range data item, that is, data is sent during the spare time. Based on the available time axis of each data item in each range, real-time transmission analysis is performed with the highest transmission economy as the benchmark to obtain the real-time transmission time of each data item in each range.

[0042] In one embodiment, the timeliness requirements of each analysis need can be obtained in real time. These requirements can be set according to user needs and updated according to different time periods and conditions. That is, the timeliness requirements of each analysis need under different conditions can be preset and matched later. Alternatively, intelligent models can be built based on machine learning, deep learning algorithms, etc. to intelligently determine the timeliness requirements.

[0043] In one embodiment, when the credibility of the collected prediction data is lower than the threshold X1, it indicates that the credibility is not up to standard and real data is needed for analysis. At this time, the timeliness requirement is adjusted, that is, the data is sent immediately.

[0044] In one embodiment, the timeliness requirements of each analysis need are obtained in real time. Based on the above embodiment, the timeliness is adjusted in combination with the credibility of the collected prediction data. That is, the collected prediction data is used for alternative analysis to extend the timeliness requirements corresponding to the data transmission. The timeliness is determined by the prediction credibility being lower than the threshold X1.

[0045] In one embodiment, the impact prediction data of data transmission is estimated in real time, that is, the environmental data that will affect the data transmission is predicted; based on the impact prediction data, the data transmission of each range of data items is analyzed in real time to see if the requirements are met, that is, whether the adverse environmental impact is within the preset standard, so as to avoid data quality not meeting the requirements. The user sets the standard according to the needs, obtains the transmission prediction results, and marks the time period in which the transmission prediction result shows that the impact exceeds the standard in the spare time axis, indicating that the data will not be transmitted in that time period if there is a choice; that is, the spare time axis is adjusted according to the transmission impact results.

[0046] In one embodiment, real-time transmission analysis is performed based on the available time axis of each range of data items, with the highest transmission economy as the benchmark. The analysis is based on existing technologies to select the most economical transmission method. For example, an intelligent analysis model is established based on machine learning, deep learning, etc., and the training set is set manually for training. Alternatively, other methods can be used to determine the candidate methods, and the candidate methods are prioritized based on economic efficiency to determine the transmission method.

[0047] In one embodiment, real-time transmission analysis of various analytical needs can be performed based on existing methods to obtain the real-time transmission time of data items in various ranges, such as analysis based on intelligent algorithms such as machine learning.

[0048] In one embodiment, transmission information is generated based on the real-time transmission time of each range of data items, including: Identify each real-time transmission time and integrate the data items that reach the real-time transmission time range into a data transmission range; Identify the data collection range corresponding to the collected data, and subtract the data transmission range from the data collection range to obtain the data storage range; Transmission information is generated based on the data transmission range and data storage range.

[0049] The device is located at the power distribution box and includes a data acquisition module, a transmission module, and a control module. The acquisition module is used to acquire data from the distribution box in real time, obtain the acquired data, and acquire data according to the preset acquisition items.

[0050] The transmission module is used to manage the transmission of collected data according to the received transmission information, identify the data transmission range and data storage range in real time according to the transmission information, summarize the collected data corresponding to the current time and the stored collected data according to the data transmission range and send them to the platform analysis module on the platform side, which is the collected data within the data transmission range for transmission analysis; and store the collected data corresponding to the data storage range.

[0051] The control module is used to perform control processing based on the received control information.

[0052] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0053] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An automated control system for data acquisition in a distribution box, characterized in that, Including both platform and device ends; The platform includes a device analysis module, a transmission analysis module, a platform analysis module, and a prediction module; the device includes a data acquisition module, a transmission module, and a control module. The equipment analysis module is used to analyze each distribution box, obtain information about each distribution box, classify each distribution box according to the information, and obtain several distribution box classifications. The distribution boxes within each category are dynamically classified and adjusted to obtain dynamic classifications; collaborative verification is performed on each distribution box within the dynamic classification to obtain collaborative verification results; and corresponding processing is carried out based on the collaborative verification results. The prediction module is used to perform real-time data acquisition and prediction for each distribution box, obtain the acquisition and prediction data and credibility of each distribution box, mark the acquisition and prediction data with corresponding credibility tags, and send the acquisition and prediction data to the platform analysis module. The platform analysis module is used to analyze the received collected data or collected prediction data, obtain corresponding fault analysis results, generate corresponding control signals based on the fault analysis results, and send the control signals to the control module on the device side. The transmission analysis module is used to perform real-time transmission analysis, obtain various analysis requirements, identify the data analysis range corresponding to each analysis requirement, and the data analysis range is composed of various range data items; perform real-time transmission analysis on each analysis requirement to obtain the real-time transmission time of each range data item; generate transmission information based on the real-time transmission time of each range data item, and send the transmission information to the transmission module on the device side. The acquisition module is used to acquire data from the distribution box in real time and obtain the acquired data. The transmission module is used to manage the transmission of collected data according to the received transmission information, identify the data transmission range and data storage range in real time according to the transmission information, summarize the collected data corresponding to the current time and the stored collected data according to the data transmission range and send them to the platform analysis module on the platform side, and store the collected data corresponding to the data storage range. The control module is used to perform control processing based on the received control information.

2. The automated control system for data acquisition of a distribution box according to claim 1, characterized in that, The distribution boxes are categorized according to their information, including: Obtain the various power distribution backgrounds of each distribution box at its current location, summarize the power distribution backgrounds corresponding to each distribution box, and obtain the background set of each distribution box. Group the distribution boxes with the same background set into one category to obtain several initial categories; obtain the historical power distribution data of each distribution box under the corresponding power distribution background; extract the power distribution result data of the distribution box under the power distribution background based on the historical power distribution data; summarize the power distribution result data of the distribution box under each power distribution background to obtain the result set data of the distribution box. The distribution boxes within the initial classification are evaluated based on the data from each result set to obtain the classification evaluation results between the distribution boxes. The classification evaluation results include results of the same category and results of different categories. All distribution boxes that have the same classification evaluation result within the initial classification are grouped into one category to obtain the classification of each distribution box.

3. The automated control system for data acquisition of a distribution box according to claim 2, characterized in that, The distribution boxes within the initial category are evaluated based on the data from each result set, including: Mark the power distribution background corresponding to the initial classification as i, i=1, 2, …, n, n is the number of power distribution backgrounds corresponding to the initial classification; mark the power distribution result data in the result set data as p i ; then the result set data is U={p1, p2, p3, …, p n} Establish an outcome evaluation model, the expression of which is: ； In the formula: (U c , U r ) is input data, U c and U r respectively represent the result set data of two power distribution boxes being evaluated; U c ⇔U r represents that the result set data of the two power distribution boxes are considered equivalent, equivalent to p 1c ⇔p 1r , p 2c ⇔p 2r , p 3c ⇔p 3r , …, p nc ⇔p nr ; the output data is the result evaluation value PG(U c , U r ), and the result evaluation value is 1 or 0; The result set data of the two corresponding distribution boxes in the initial classification is integrated into the input data and input into the result evaluation model for analysis to obtain the result evaluation value between the corresponding distribution boxes. When the evaluation value of the result is 1, the classification evaluation result is that the result belongs to the same category; When the evaluation value of the result is 0, the classification evaluation result is that the result is not in the same class.

4. The automated control system for data acquisition of a distribution box according to claim 1, characterized in that, Dynamically adjust the classification of each distribution box within the distribution box category, including: The power distribution background of each power distribution box within the power distribution box category is determined in real time. Power distribution boxes with the same power distribution background within the power distribution box category are grouped together and marked as dynamic classification.

5. The automated control system for data acquisition of a distribution box according to claim 1, characterized in that, Real-time transmission and analysis of various analytical needs, including: Real-time acquisition of the timeliness requirements of various analysis needs, identification of the range data items corresponding to each data analysis scope; dynamic generation of the spare time axis for each range data item based on the timeliness requirements; Based on the available time axis of each data item in each range, real-time transmission analysis is performed with the highest transmission economy as the benchmark to obtain the real-time transmission time of each data item in each range.

6. The automated control system for data acquisition of a distribution box according to claim 5, characterized in that, Adjust the timeliness requirements of the corresponding analysis needs based on the reliability of the collected and predicted data.

7. The automated control system for data acquisition of a distribution box according to claim 5, characterized in that, Real-time prediction of the impact of data items in each range; real-time analysis of the impact prediction data to determine whether the data transmission of each range of data items meets the requirements, and obtaining the transmission prediction results; and adjustment of the idle time axis based on the transmission impact results.

8. The automated control system for data acquisition of a distribution box according to claim 5, characterized in that, Transmission information is generated based on the real-time transmission time of each data item, including: Identify each real-time transmission time and integrate the data items that reach the real-time transmission time range into a data transmission range; Identify the data collection range corresponding to the collected data, and subtract the data transmission range from the data collection range to obtain the data storage range; Transmission information is generated based on the data transmission range and data storage range.

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