Intelligent integrated design method for box-type substation based on cloud data

Abstract

The invention relates to the technical field of data processing, in particular to a box-type substation intelligent integrated design method based on cloud data, and the method comprises the steps: carrying out the integrated storage of historical system diagram data, component data, cabinet type data and heat dissipation scheme data; through a machine learning algorithm, the relevance between the classified and sorted data is learned, and the machine learning algorithm is applied to the prediction model; training the prediction model, and outputting corresponding component data, cabinet type data and a heat dissipation scheme according to newly input system diagram data; in the data transmission process, encryption processing is carried out, and a transport layer security protocol is adopted for encryption processing. According to the method, the design process is simplified, the design accuracy and speed are improved, the matching and heat dissipation scheme of each component is optimized, the problem of parameter complex relevance is solved through systematic data analysis, meanwhile, the safety of data in the transmission and storage process can be ensured, and the reliability of the system is improved. Therefore, the design efficiency and quality of the box-type substation are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent integrated design method for box-type substations based on cloud data. Background Art

[0002] A box-type substation is an integrated and efficient power equipment, which is widely used in places such as urban power supply, industrial parks, and commercial centers. It integrates a transformer, high-voltage distribution devices, low-voltage distribution devices, and auxiliary equipment in a sealed box, and has the advantages of small floor area, quick installation, and convenient movement. However, the design and configuration of box-type substations need to consider multiple factors, such as equipment heat dissipation, size matching, ventilation conditions, etc. These factors directly affect the operation efficiency and safety of the substation.

[0003] According to the current technological development, by introducing artificial intelligence technology, the design efficiency and reliability of box-type substations can be significantly improved. For example, by using a large amount of historical system diagram design data, component heat dissipation data, component size data, box-type substation cabinet type data, and heat dissipation solution data stored in the cloud, the correlation between these data can be learned and analyzed specifically through machine learning algorithms. Thus, when generating new single data, the most suitable other data solutions can be automatically output, which not only speeds up the design process but also improves the design accuracy, ultimately ensuring good matching of each component in actual use, and finally enhancing the overall performance and safety of the substation.

[0004] However, currently, there is no mature technical solution for the above content. The design of many box-type substations still relies on traditional manual experience and static data, resulting in a cumbersome and error-prone design process. Designers need to manually search for and match component information, and it is difficult to quickly and accurately select a suitable design solution. In addition, due to the lack of systematic analysis of a large amount of historical data, the design process often cannot fully consider the complex correlations between various parameters, easily leading to problems such as poor heat dissipation, space waste, or equipment mismatch, ultimately affecting the operation stability and maintenance cost of the substation.

[0005] How to provide a specific solution by introducing cloud data and intelligent integrated design to effectively overcome the above limitations, thereby improving the design efficiency and quality, has become a technical problem that needs to be solved currently. Summary of the Invention

[0006] The present invention provides an intelligent integrated design method for box-type substations based on cloud data, which can effectively solve the problems in the background art.

[0007] To achieve the above object, the technical solution adopted by the present invention is:

[0008] Intelligent integrated design method for box-type substation based on cloud data, including:

[0009] Integrated storage of historical grouped system diagram data, component data, cabinet type data, and heat dissipation scheme data to form a cloud data warehouse;

[0010] Classify and structurally organize the data stored in the cloud data warehouse in sequence;

[0011] Through machine learning algorithms, learn the correlation between the classified and organized data, and apply the machine learning algorithms to the prediction model;

[0012] Train the prediction model, and through the trained prediction model, according to the newly input system diagram data, output the corresponding component data, cabinet type data, and heat dissipation scheme.

[0013] Furthermore, realize the transmission of the grouped system diagram data, component data, cabinet type data, and heat dissipation scheme data to the cloud data warehouse through a data upload tool. The transmission process includes:

[0014] Initiate a connection request to the cloud data warehouse through the data upload tool, and start the Transport Layer Security (TLS) protocol handshake process. The connection request includes at least the TLS protocol version, supported encryption algorithms, and random numbers;

[0015] The cloud data warehouse responds to the connection request, selects the supported TLS protocol version and encryption algorithm, and sends a message with a digital certificate attached;

[0016] Verify the digital certificate through the data upload tool, and generate a pre-master key after verification, encrypt it with the public key of the cloud data warehouse, and send it to the cloud data warehouse;

[0017] Generate a session key using the pre-master key and random numbers. The session key is used for encrypting and decrypting data;

[0018] The data upload tool and the cloud data warehouse send a set message to complete the handshake process.

[0019] Furthermore, before the grouped system diagram data, component data, cabinet type data, and heat dissipation scheme data are transmitted to the cloud data warehouse, confirm the box-type substation corresponding to each group of data, and provide a unique identifier for the box-type substation; add the unique identifier to the random numbers.

[0020] Furthermore, determine the random numbers, including:

[0021] Randomly extract a part of the unique identifier as the first part of the random numbers;

[0022] Collect the dynamic characteristic data of the network during data transmission, and convert it into a byte sequence as the second part of the random number;

[0023] Use the timestamp during data transmission as the third part of the random number.

[0024] Further, collect the dynamic characteristic data of the network during data transmission and convert it into a byte sequence, including:

[0025] Determine the types of network dynamic characteristic data to be collected, and collect real-time data for each type;

[0026] Convert the collected network dynamic characteristic data into a byte representation of a set length;

[0027] Combine the byte representations of all types of network dynamic characteristic data into a continuous byte sequence;

[0028] Perform a hash process on the combined byte sequence, and use the converted byte sequence as part of the random number.

[0029] Further, the machine learning algorithm is the XGBoost algorithm, which integrates several decision trees. The prediction model applied by the algorithm is:

[0030]

[0031] Among them, are the component data, cabinet type data, and heat dissipation solution to be predicted;

[0032] f k is the k-th decision tree of the XGBoost algorithm;

[0033] K is the total number of decision trees;

[0034] x i is the feature vector corresponding to the newly input system diagram data.

[0035] Further, the calculation formula for the total number K of decision trees is:

[0036] K = N / η · C

[0037] Among them, N is the number of features in the feature vector corresponding to the newly input system diagram data;

[0038] η is the set learning rate;

[0039] C is an adjustment constant used to adjust the ratio of the calculation formula.

[0040] Further, the calculation formula for the adjustment constant C is:

[0041] C = aN + b

[0042] Wherein, a and b are fitting coefficients, obtained by fitting the relationship between C and N through the linear regression method.

[0043] Furthermore, classifying the data stored in the cloud data warehouse includes: determining the classification criteria for the stored data, and adding classification labels to each data item according to the classification criteria.

[0044] Furthermore, the structured arrangement after classifying the data stored in the cloud data warehouse includes:

[0045] Defining a data model according to the characteristics and classification results of the classified data;

[0046] Designing a database structure according to the data model, and creating corresponding tables and relationships;

[0047] Performing data cleaning and transformation to achieve the matching of the data with the tables and relationships in the database structure.

[0048] Through the technical solution of the present invention, the following technical effects can be achieved:

[0049] The intelligent integrated design method of the box-type substation based on cloud data in the present invention effectively overcomes the limitations in the current technology, simplifies the design process, improves the design accuracy and speed, optimizes the matching and heat dissipation solutions of each component, and solves the problem of complex parameter correlation through systematic data analysis. At the same time, it can ensure the security of data during transmission and storage, thereby comprehensively improving the design efficiency and quality of the box-type substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 It is a schematic flow chart of the intelligent integrated design method of the box-type substation based on cloud data;

[0052] Figure 2 It is a schematic flow chart of the transmission process;

[0053] Figure 3 It is a schematic flow chart of determining a random number. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention belongs. The terms used in the description of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0056] As Figure 1 shown, a smart integrated design method for a box-type substation based on cloud data includes:

[0057] S1: Integrate and store historical grouped system diagram data, component data, cabinet type data, and heat dissipation solution data to form a cloud data warehouse;

[0058] Specifically, in order to centrally manage the data related to the box-type substation collected, this step provides a unified data management platform by constructing a cloud data warehouse for storing several groups of system diagram data, component data, cabinet type data, and heat dissipation solution data of the box-type substation, facilitating the centralized management of data, improving the reliability of data, and providing a basis for subsequent data analysis and processing. The system diagram data, component data, cabinet type data, and heat dissipation solution data of the same box-type substation are a group of data. Among them, the system diagram data includes main wiring diagrams, protection and control circuit diagrams, equipment layout diagrams, cable routing diagrams, single-line diagrams, etc.; the component data includes model and specifications, rated voltage and current, size and weight, heat dissipation parameters, life and reliability data, etc.; the cabinet type data includes cabinet type categories, size and layout, material and structure, interfaces and connection points, heat dissipation characteristics, installation methods, etc.; the heat dissipation solution data includes heat dissipation technology types, heat dissipation components, heat dissipation effect evaluation, environmental adaptability, cost and energy consumption, etc.

[0059] S2: Classify and structurally organize the data stored in the cloud data warehouse in sequence;

[0060] Based on the above embodiments, classification criteria for various types of data can be formulated to organize and classify the data stored in the cloud data warehouse. The data stored in the cloud data warehouse has its own specific attributes and uses. Through classification, these data can be better managed and utilized, improving the retrievability and operability of the data. For the classification results, the data can be stored in different database tables or columns for structured organization to facilitate management and query. Therefore, this step combines data classification and data structured organization, which can improve data quality and ensure the effectiveness and accuracy of the data in subsequent analysis and machine learning processes.

[0061] S3: Through machine learning algorithms, learn the correlations between the classified and organized data, and apply the machine learning algorithms to the prediction model;

[0062] S4: Train the prediction model. Through the trained prediction model, according to the newly input system diagram data, output the corresponding component data, cabinet type data, and heat dissipation solutions.

[0063] Specifically, for the analyzed and organized data, appropriate machine learning algorithms such as decision trees, random forests, neural networks, etc. can be used to find the internal relationships and patterns between different data. A prediction model is constructed based on the selected machine learning algorithm, and the data stored in the cloud data warehouse can be divided into a training set and a test set. The prediction model is repeatedly trained and verified. During the process, techniques such as cross-validation can be used to evaluate the model performance, and the parameters are continuously adjusted to improve the accuracy of the model. Through sufficient training, the model can have high prediction ability and generalization ability, be able to adapt to different input data, and accurately predict the design solutions corresponding to new data. Through machine learning and the prediction model, the system can learn and optimize autonomously, reducing human intervention, improving the intelligence level of the design, and automatically predicting and outputting the design solutions, improving the design efficiency. At the same time, sufficient training and verification also ensure the prediction accuracy of the model, thereby improving the reliability and rationality of the box-type substation design solution.

[0064] The intelligent integrated design method of the box-type substation based on cloud data in the present invention effectively overcomes the limitations in the current technology, simplifies the design process, improves the accuracy and speed of the design, optimizes the matching of each component and the heat dissipation solution, and solves the problem of complex parameter correlations through systematic data analysis, thereby comprehensively improving the design efficiency and quality of the box-type substation.

[0065] Based on the above embodiments, as Figure 2 shown, through the data upload tool, the transmission of grouped system diagram data, component data, cabinet type data, and heat dissipation solution data to the cloud data warehouse is realized. The transmission process includes:

[0066] A1: Initiate a connection request to the cloud data warehouse through a data upload tool, start the Transport Layer Security (TLS) protocol handshake process. The connection request includes at least the TLS protocol version, supported encryption algorithms, and a random number.

[0067] In this step, data upload tools such as command-line tools, APIs, SDKs, and data synchronization tools can be selected to initiate a connection request and start the TLS protocol handshake process to establish a secure connection, ensuring the encryption and integrity of data during transmission. The TLS protocol version, supported encryption algorithms, and random number are essential elements in the handshake process. The TLS protocol is used to ensure the security of data during transmission. The protocol version and encryption algorithm information in the connection request are used to negotiate the secure communication standards used by both parties. The random number is used for subsequent key generation to ensure the uniqueness and security of each connection communication.

[0068] A2: The cloud data warehouse responds to the connection request, selects a supported TLS protocol version and encryption algorithm, and sends a message with an attached digital certificate.

[0069] After receiving the connection request initiated by the data upload tool, the cloud data warehouse can parse the request content, select the optimal version and encryption algorithm supported by both parties to ensure the smooth progress of communication. Sending a message with an attached digital certificate can be used to verify the identity of the cloud data warehouse, avoid connecting to a forged server, and prevent man-in-the-middle attacks. The digital certificate contains a public key and other authentication information, which is used to encrypt sensitive data in subsequent communications.

[0070] A3: Verify the digital certificate through the data upload tool, and after verification, generate a pre-master key, encrypt it with the public key of the cloud data warehouse, and send it to the cloud data warehouse.

[0071] After receiving the digital certificate sent by the cloud data warehouse, the data upload tool can verify it to ensure the authenticity and validity of the digital certificate. Verifying the digital certificate ensures that the identity of the cloud data warehouse is true and trustworthy, preventing forgery. After successful verification, a pre-master key is generated. This is a temporary key used for subsequent generation of session keys. Then, the pre-master key can be encrypted with the public key and sent to the cloud data warehouse to ensure the security of the key exchange process.

[0072] A4: Generate a session key using the pre-master key and the random number. The session key is used for encrypting and decrypting data.

[0073] The pre-master key and the random number can generate a symmetrically encrypted session key through a key generation algorithm, such as a pseudorandom function, ensuring that the key for each communication is unique. The session key can be used for subsequent data encryption and decryption to ensure the confidentiality and integrity of data transmission.

[0074] A5: The data upload tool and the cloud data warehouse send setup messages to complete the handshake process.

[0075] Specifically, the two parties confirm the successful completion of the handshake process by exchanging setup messages. The setup messages can include a handshake message digest to ensure that the data transmitted during the entire handshake process has not been tampered with. After the handshake is completed, the two parties can use the session key for secure data transmission. The entire transmission process is carried out through the Transport Layer Security (TLS) protocol to ensure the security of data transmission. Through digital certificate verification and encryption key exchange, data leakage and man-in-the-middle attacks are prevented; the verification and confirmation mechanisms included in each step ensure the reliability of the connection between the data upload tool and the cloud data warehouse, and the integrity and confidentiality of the data during the transmission process are guaranteed. Although the TLS protocol handshake process is complex, it runs quickly in modern computing environments to ensure the efficiency of the data transmission process.

[0076] Furthermore, before the grouped system diagram data, component data, cabinet type data, and heat dissipation solution data are transmitted to the cloud data warehouse, confirm the box-type substations corresponding to each group of data and provide a unique identifier for the box-type substations; add the unique identifier to the random number.

[0077] In this embodiment, the integrity and accuracy of each group of data can be checked and verified to ensure that the data can be correctly mapped to the corresponding box-type substations, and a universally unique identifier or other unique coding method can be used to generate a unique identifier for each box-type substation. During the TLS protocol handshake process, the unique identifier of the box-type substation can be embedded in the random number to ensure that the data source can be identified during the handshake process, and certain format processing can be performed to ensure the validity of the random number.

[0078] Furthermore, as Figure 3 shown, determining the random number includes:

[0079] Randomly extract a part of the unique identifier as the first part of the random number;

[0080] As a preference of the above embodiment, during the random number generation process, several characters, numbers, or specific digits can be randomly selected from a specific position of the unique identifier as the first part of the random number. Since the identifier of each box-type substation is unique, extracting a part from the unique identifier as a part of the random number can increase the uniqueness of the generated random number.

[0081] Collect the dynamic characteristic data of the network during data transmission and convert it into a byte sequence as the second part of the random number;

[0082] Specifically, system functions or third-party libraries can be used to obtain network dynamic feature data, which includes the dynamic attributes of the network during data transmission. These features change over time and with the network state. Therefore, converting the collected dynamic feature data into a byte sequence as the second part of the random number increases the dynamism of the random number.

[0083] Use the timestamp during data transmission as the third part of the random number.

[0084] The timestamp marks the specific time when the random number is generated or collected, which is crucial for recording and identifying the chronological order of data. As time goes by, the random numbers generated at different time points will be different. Therefore, using the timestamp during data transmission as part of the random number can increase the timeliness of the random number. At the same time, timeliness can ensure that the generated random number is associated with a specific time point, ensuring the uniqueness of each generation of the random number in terms of time.

[0085] Take an example of generating a random number. Suppose the unique identifier of a box-type substation is ABC123, and extract a part ABC1 of the unique identifier as the first part of the random number; the current network dynamic feature data is network latency 100ms, bandwidth 100Mbps, and packet loss rate 0.1%. Convert the network dynamic feature data into a byte sequence b'100ms_100Mbps_0.1%' as the second part of the random number; the time when the random number is generated is 12:25:02 on June 25, 2021, which is converted into a timestamp 1735728002 as the third part of the random number. Integrating the above three parts together, the final random number can be generated as follows:

[0086] Random number = ABC1 + b'100ms_100Mbps_0.1%' + 1735728002;

[0087] Where b is a byte sequence object.

[0088] In practical applications, the unique identifier, network dynamic feature data, and timestamp are all independent and unrelated information, and the combination order of them will not affect the nature of each part itself or the integrity of the information. For example, the timestamp can also be used as the starting point first, then the network dynamic feature data is added, and finally the part of the unique identifier is added. Therefore, the order of the three parts is allowed to be flexibly adjusted, and various order forms are within the protection scope of the present invention.

[0089] Furthermore, collecting the network dynamic feature data during data transmission and converting it into a byte sequence includes:

[0090] B1: Determine the types of network dynamic feature data to be collected, and collect real-time data for each type;

[0091] B2: Convert the collected network dynamic feature data into a byte representation of a set length.

[0092] B3: Combine the byte representations of all types of network dynamic feature data into a continuous byte sequence.

[0093] B4: Perform a hash process on the combined byte sequence and use the converted byte sequence as part of a random number.

[0094] Specifically, it is possible to determine the types of network dynamic feature data to be collected, including but not limited to network latency, bandwidth utilization, packet loss rate, packet transmission delay, etc. Each type of network dynamic feature data collected needs to be converted into a fixed-length byte representation. This conversion usually involves encoding the data into a byte sequence according to specific rules to ensure the consistency and reliability of the data during transmission and processing; combine the byte representations of all collected network dynamic feature data into a continuous byte sequence, so as to form a complete data block containing all the required information and prepare for subsequent hash processing; perform a hash process on the combined byte sequence. The hash process can be implemented through an encryption hash function to map the complex byte sequence to a fixed-length hash value. The result of the hash process is an irreversible fixed-length data digest that can uniquely represent the input data block; use the result of the hash process as part of a random number, which can ensure the uniqueness and security of the generated random number, while retaining the important features of the original data and is suitable for application scenarios such as authentication, security verification, and data integrity check.

[0095] Furthermore, the machine learning algorithm is the XGBoost algorithm, which integrates several decision trees. The prediction model applied by the algorithm is:

[0096]

[0097] Wherein, is the component data, cabinet type data, and heat dissipation solution to be predicted;

[0098] f k is the k-th decision tree of the XGBoost algorithm;

[0099] K is the total number of decision trees;

[0100] x i is the feature vector corresponding to the newly input system diagram data.

[0101] Preferably in this embodiment, the XGBoost algorithm is selected to learn and predict the correlations between various data. The XGBoost algorithm models the complex non-linear relationships between data by integrating multiple decision trees. Due to the complex interdependencies and non-linear relationships among various data types in the intelligent integrated design of box-type substations, the XGBoost algorithm can effectively capture and model these complex relationships, thereby improving the accuracy and reliability of predictions. In this solution, the XGBoost algorithm can effectively handle the complex electrical connection relationships and layout requirements between different parts of the system diagram data, thereby optimizing the design and layout of the electrical system to ensure compliance with safety and performance requirements; it can also analyze the correlations between component data and system operating parameters to predict the most suitable component selection and configuration; it can comprehensively consider multiple aspects of cabinet type data to provide the best cabinet type selection suggestions to ensure the safe and efficient operation of the equipment; it can also analyze the efficiency and adaptability of different heat dissipation solutions and their performance under various environmental conditions to provide the optimal heat dissipation solution.

[0102] Selecting the XGBoost algorithm can be based on the importance assessment of each feature during the learning process, which helps identify and understand the most influential features in the data. For the design of box-type substations, this means being able to determine which system diagram data, component data, cabinet type data, and heat dissipation solution data have a key impact on the final prediction model, helping to optimize the design and decision-making process; the XGBoost algorithm also has the ability to handle missing data and outliers, and can improve the robustness and stability of the model through reasonable splitting strategies and handling of missing values.

[0103] Furthermore, the calculation formula for the total number K of decision trees is:

[0104] K = N / η · C

[0105] Where, N is the number of features in the feature vector corresponding to the newly input system diagram data;

[0106] η is the set learning rate;

[0107] C is an adjustment constant used to adjust the ratio of the calculation formula.

[0108] In the formula, N represents the number of features obtained after the feature engineering of the newly input system diagram data. These features can be numerical or categorical data describing system structure, performance, or other relevant factors; the learning rate η is a parameter set when training each decision tree, controlling the influence degree of each tree on the final model prediction. A smaller learning rate usually leads to more decision trees being trained to achieve the ideal model performance; the adjustment constant C is a scaling factor used to adjust the calculation formula. The choice of the adjustment constant directly affects the size of the total number of decision trees K. The adjustment constant C is usually a preset parameter in machine learning and optimization algorithms and can be obtained through methods such as empirical value setting, parameter search (such as grid search, random search, etc.), automatic adjustment (such as evolutionary algorithms, particle swarm optimization, etc.), and interactive optimization to obtain the optimal adjustment constant C.

[0109] Taking the calculation of two different total numbers K as an example, the specific process is as follows:

[0110] Example 1, assume that the number of features N = 50, the learning rate η = 0.1, and the adjustment constant C = 2, then K = N / η · C = 1000.

[0111] Example 2, assume that the number of features N = 80, the learning rate η = 0.05, and the adjustment constant C = 3, then K = N / η · C = 4800.

[0112] From the calculation processes of the above two total numbers K, it can be seen that the increase in the number of features will lead to a significant increase in the total number of decision trees K. A larger adjustment constant and a smaller learning rate usually lead to a smaller step size of parameter update in each iteration, and more decision trees can be trained to capture complex data relationships to improve the accuracy and generalization ability of the model. This calculation formula combines the factors of the number of features, the learning rate, and the adjustment constant, providing an effective way to adjust the number of decision trees for the intelligent integrated design of box-type substations.

[0113] Furthermore, the calculation formula for the adjustment constant C is:

[0114] C = aN + b

[0115] Among them, a and b are fitting coefficients, obtained by fitting the relationship between C and N through the linear regression method.

[0116] This step can achieve intelligent optimization of the number of decision trees K by dynamically adjusting the adjustment constant C. When there is new input data each time, through the dynamic calculation of a and b, the size of C can be adjusted according to different data volumes and feature complexities. Dynamically adjusting C can intelligently adjust the number of decision trees K according to the characteristics and quantity of new data, which can ensure that the model can always effectively capture the features and correlations of the latest data, improving the accuracy and adaptability of prediction; through the dynamic optimization of the adjustment constant C and dynamically adjusting the number of decision trees K, the complexity of the model can be effectively controlled, the utilization of computing resources can be optimized, and the problems of overfitting or underfitting can be avoided; at the same time, by using linear regression or other optimization methods to dynamically adjust C, the process of adjusting the number of decision trees K can be automated. The system can automatically calculate and adjust the most suitable C value and K value according to the changes in real-time data without manual intervention, improving the intelligence and efficiency of the system and making it adaptable to datasets and application scenarios of different scales and complexities.

[0117] Furthermore, classify the data stored in the cloud data warehouse, including: determining the classification criteria for the stored data and adding classification labels to each data item according to the classification criteria.

[0118] Specifically, to make data more orderly and efficient during storage and retrieval, it can be classified according to data types, uses, or characteristics and classification labels can be added to reduce the time for data search and processing. System diagram data can be classified according to its functional areas, equipment types, or electrical connection relationships to systematically organize and manage system diagram data. For example, system diagram data can be divided into power generation area system diagrams, substation area system diagrams, power distribution area system diagrams, control area system diagrams, and auxiliary equipment area system diagrams according to its functional areas, and classification labels can be added to each category. For example, the wiring diagram of a generator set and its connection to a transformer can be added with the classification label of power generation area system diagram. Component data can be classified according to its type, function, or manufacturer. For example, component data can be divided into switches, cables, capacitors, transformers, and circuit breakers according to its type, and classification labels can be added to each category. For example, a load switch can be added with the classification label of switch-type components. Cabinet type data can be classified according to its use, material, or installation location. For example, cabinet type data can be divided into low-voltage cabinets, medium-voltage cabinets, high-voltage cabinets, and control cabinets according to its use, and classification labels can be added to each category. For example, a low-voltage power distribution cabinet can be added with the classification label of low-voltage cabinet. Heat dissipation solution data can be classified according to radiator type, heat dissipation efficiency, or applicable environment. For example, heat dissipation solution data can be divided into air-cooled, water-cooled, phase change heat dissipation, and hybrid according to its radiator type, and classification labels can be added to each category. For example, air-cooled can be added with the classification label of air-cooled heat dissipation. For different types of data, corresponding classification labels can be added to each data item through determined classification criteria and classification rules, which helps with subsequent data management, analysis, and application, and improves the operability of the data and the intelligence level of the overall system design.

[0119] Furthermore, perform structured sorting on the data stored in the cloud data warehouse, including:

[0120] C1: Define a data model according to the characteristics of the classification data and the classification results;

[0121] Defining a data model is the process of transforming real-world entities and their relationships into data structures. According to the determined characteristics of the classification data, define the corresponding data model. A clear data model defines the data structure, ensuring data consistency and integrity, and can effectively manage the data in the cloud data warehouse.

[0122] C2: Design the database structure according to the data model and create the corresponding tables and relationships;

[0123] Designing the database structure and transforming the data model into tables and relationships in the database can ensure that the data in the cloud data warehouse is stored and managed in an efficient, orderly, and flexible manner. For each data type, corresponding tables are defined according to its characteristics, and necessary fields are created for each table. When designing the database structure, not only independent tables need to be created, but also the relationships between tables need to be defined to ensure data relevance and integrity. These relationships can be one-to-many, many-to-many, or one-to-one relationships. Clear table and relationship design makes database queries more efficient, enabling quick retrieval and processing of relevant data. Through reasonable table design and relationship definition, data storage space can be optimized, redundant data can be reduced, and storage efficiency can be improved. The design of the data structure can also add new tables and fields according to requirements without affecting the integrity of the existing data.

[0124] C3: Perform data cleaning and transformation to achieve the matching of data with the tables and relationships in the database structure.

[0125] Performing data cleaning includes operations such as data deduplication, error correction, and filling missing values. Performing data transformation includes operations such as format conversion, field matching, and data mapping. Data cleaning and transformation can convert the original data into data that conforms to the database structure, eliminate errors and redundant data, ensure data quality, and improve data reliability and consistency.

[0126] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. The intelligent integrated design method of box-type substation based on cloud data is characterized by: include: Integrate and store historical grouped system diagram data, component data, cabinet data, and cooling solution data to form a cloud data warehouse; Classifying and structuring the data stored in the cloud data warehouse in turn; Using a machine learning algorithm, learning the correlation between the classified and organized data, and applying the machine learning algorithm to a prediction model; The prediction model is trained, and the trained prediction model outputs the corresponding component data, cabinet type data and heat dissipation solution according to the newly input system diagram data.

2. The intelligent integrated design method for box-type substation based on cloud data according to claim 1 is characterized in that: The system diagram data, component data, cabinet data and cooling solution data are transmitted to the cloud data warehouse in groups through a data upload tool. The transmission process includes: Initiate a connection request to the cloud data warehouse through the data upload tool to start the transport layer security protocol handshake process, wherein the connection request at least includes the transport layer security protocol version, supported encryption algorithm and random number; The cloud data warehouse responds to the connection request, selects the supported transport layer security protocol version and encryption algorithm, and sends a message with a digital certificate; The digital certificate is verified by the data upload tool, and after the verification is completed, a pre-master key is generated, which is encrypted by the public key of the cloud data warehouse and sent to the cloud data warehouse; Generate a session key using the pre-master key and a random number, wherein the session key is used to encrypt and decrypt data; The data upload tool and the cloud data warehouse send a setting message to complete the handshake process.

3. The intelligent integrated design method of box-type substation based on cloud data according to claim 2 is characterized in that: Before the grouped system diagram data, component data, cabinet data and cooling solution data are transmitted to the cloud data warehouse, the box-type substation corresponding to each group of data is confirmed, and a unique identifier is provided for the box-type substation; the unique identifier is added to the random number.

4. The intelligent integrated design method for box-type substation based on cloud data according to claim 3 is characterized in that: Determining the random number includes: randomly extracting a part of the unique identifier as a first part of the random number; Collecting dynamic characteristic data of the network during data transmission and converting it into a byte sequence as the second part of the random number; The timestamp of data transmission is used as the third part of the random number.

5. The intelligent integrated design method of box-type substation based on cloud data according to claim 4 is characterized in that: Collect dynamic characteristic data of the network during data transmission and convert it into byte sequences, including: Determine the types of network dynamic feature data that need to be collected, and collect real-time data for each type; Converting the collected network dynamic feature data into a byte representation of a set length; Combine the byte representations of all types of network dynamic feature data into a continuous byte sequence; Hash the combined byte sequence and use the converted byte sequence as part of the random number.

6. The intelligent integrated design method for box-type substation based on cloud data according to any one of claims 1 to 5, characterized in that: The machine learning algorithm is the XGBoost algorithm, which integrates several decision trees. The prediction model used by the algorithm is: in, The component data, cabinet type data and heat dissipation solution to be predicted; f k is the kth decision tree of the XGBoost algorithm; K is the total number of decision trees; x i It is the feature vector corresponding to the newly input system diagram data.

7. The intelligent integrated design method for box-type substation based on cloud data according to claim 6 is characterized in that: The calculation formula for the total number K of the decision trees is: K=N / η·C Wherein, N is the number of features in the feature vector corresponding to the newly input system diagram data; η is the set learning rate; C is a regulating constant used to adjust the ratio of the calculation formula.

8. The intelligent integrated design method for box-type substation based on cloud data according to claim 7 is characterized in that: The calculation formula of the adjustment constant C is: C=aN+b Among them, a and b are fitting coefficients, which are obtained by fitting the relationship between C and N using the linear regression method.

9. The intelligent integrated design method for box-type substation based on cloud data according to any one of claims 1 to 5, characterized in that: Classifying the data stored in the cloud data warehouse includes: determining a classification standard for the stored data, and adding a classification label to each data item according to the classification standard.

10. The intelligent integrated design method for box-type substation based on cloud data according to claim 9 is characterized in that: The data stored in the cloud data warehouse is classified and structured, including: Define the data model based on the characteristics of the classified data and the classification results; Design the database structure according to the data model and create corresponding tables and relationships; Data cleaning and conversion are performed to match the data with the tables and relations in the database structure.

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