A data management method and system based on centralized meter reading collector

Abstract

The present invention relates to the field of data intelligent processing technology, and discloses a data management method and system based on a centralized meter reading collector, including: using a centralized meter reading collector to collect instrument data; using a dual-mode wireless transmission switching method to transmit meter reading data to a data center; using dynamic encryption to protect data security when transmitting meter reading data, performing data preprocessing in the data center, and entering the data into a database after processing; performing data analysis in the database, and establishing an intelligent alarm system for automatic response. The present invention introduces a number of innovative technologies such as a dual-mode wireless transmission switching method, dynamic encryption technology, time-divided transmission strategy, intelligent data processing, network status monitoring, and intelligent selection of transmission methods, which not only solves the problems of unstable data transmission, insufficient security, and limited analysis capabilities in the prior art, but also significantly improves the reliability, security, and intelligence level of the system, and has broad application prospects and market value.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent data processing, and in particular to a data management method and system based on a centralized meter reading collector. Background Art

[0002] With the rapid development of Internet of Things technology, the methods of data collection and management in smart grid systems are also constantly improving. As a key component of smart grids, the centralized meter reading system collects data from multiple meters in a unified manner through concentrator equipment, realizing centralized management and efficient transmission of electric energy data. Traditional meter reading systems mostly rely on manual or single wired communication methods, which are not only inefficient, but also easily interfered by the external environment, and the reliability and security of data transmission are difficult to guarantee. In recent years, the development of wireless communication technology has brought new opportunities for centralized meter reading systems, especially the introduction of cellular networks and low-power wide area network technologies, making the transmission of meter reading data more flexible and efficient.

[0003] Although the existing centralized meter reading system has made some progress, there are still some shortcomings in practical applications. First, when facing a complex network environment, the single wireless transmission method is prone to problems such as unstable signals, slow transmission speed, and high packet loss rate, which affects the timeliness and accuracy of data. Secondly, the security issues in the data transmission process need to be solved urgently. The existing system generally lacks an effective dynamic encryption mechanism, and the data is vulnerable to attacks during transmission, which poses a risk of leakage. In addition, the data analysis capabilities of the existing system are limited, and it is impossible to fully utilize the large amount of collected data for in-depth analysis and intelligent processing. There is a lack of intelligent alarm mechanism, and it is difficult to detect and respond to abnormal situations in a timely manner. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing technology is difficult to use the large amount of collected data for analysis and intelligent processing, lacks an intelligent alarm mechanism, and is difficult to detect and respond to abnormal situations in a timely manner.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a data management method based on a centralized meter reading collector, comprising:

[0007] Use centralized meter reading collector to collect meter data;

[0008] Adopt dual-mode wireless transmission switching method, intelligently select transmission mode, and transmit meter reading data to the data center;

[0009] Dynamic encryption is used to protect data security when transmitting meter reading data. Data preprocessing is performed in the data center and entered into the database after processing.

[0010] Conduct data analysis in the database and establish an intelligent alarm system for automated response.

[0011] As a preferred solution of the data management method based on the centralized meter reading collector described in the present invention, wherein: the collecting of meter data includes: the centralized meter reading collector collects meter data by connecting various meters through wired and wireless methods; connecting the A and B lines of the RS485 converter to the A and B ends of the RS485 interface of the meter respectively; configuring the collector and the meter, setting the address of each meter through the DIP switch and configuration software; configuring the communication parameters of the collector, baud rate, data bit, stop bit, check bit, to ensure consistency with the meter;

[0012] The collector periodically obtains the readings of each meter, and performs preliminary storage and organization of the data; it records the status data of each transmission, including signal strength, transmission rate, delay, packet loss rate, current transmission mode, number of network switching, and switching effect.

[0013] As a preferred solution of the data management method based on the centralized meter reading collector described in the present invention, wherein: the dual-mode wireless transmission includes: a communication module supporting cellular network transmission and low-power wide area network transmission is embedded in the collector, cellular network transmission is used as the main transmission mode, and low-power wide area network transmission is used as the backup transmission mode; assuming that the signal strength is , the transmission rate is , delay is , the packet loss rate is , the transmission state vector is expressed as X 0 =[S,R,L,P] ;

[0014] Adopt the time-divided transmission strategy to transfer data in batches during the period with low network traffic; set the period with low network traffic as , the normal transmission period is ;

[0015] The amount of data is , the transmission time is , in the period To perform a batch transfer:

[0016] ,

[0017] In the period To transfer cache data:

[0018] ,

[0019] Set up storage space in the collector to cache data that cannot be transmitted temporarily. When the network condition is restored, the data in the cache is transferred to the data center in batches. The cached data is transferred in batches when the network condition is restored:

[0020] ,

[0021] in, Indicates the amount of cached data; Indicates the amount of data transferred to the data center; Indicates the amount of data transferred in batches; during periods of low network traffic and normal transmission period The division and update of the data center is determined by the data transmission volume received.

[0022] As a preferred solution of the data management method based on the centralized meter reading collector described in the present invention, wherein: the intelligent selection of the transmission mode includes integrating the network status monitoring function of the stacking forest model to detect the signal strength and transmission quality of each transmission mode in real time; intelligently selecting the transmission mode according to the site conditions and network conditions;

[0023] Clean, standardize or normalize the original data; extract the transmission status features of the transmission data; train the random forest model with the feature-extracted data;

[0024] The random forest model judges the transmission mode based on signal strength, transmission rate, delay, and packet loss rate; the transmission mode switching logic formula is expressed as:

[0025] ,

[0026] in, Represents the number of decision trees in the random forest; Indicates The prediction results of a decision tree; Indicates the comprehensive prediction results; represents the characteristic vector, i.e., the transmission state vector;

[0027] The value range of is [0,1]; if , indicating that the comprehensive prediction probability of cellular network transmission is higher, then cellular network transmission is selected; It indicates that the comprehensive prediction probability of LPWAN transmission is higher, and LPWAN transmission is selected;

[0028] When switching states, redundant transmission and smooth transition are performed, and redundant transmission is started. During the switching process, after the new transmission mode is started, the old transmission mode continues to transmit data. period of time to ensure redundant transmission; After a period of time, confirm that the new transmission method is stable and turn off the old transmission method;

[0029] During the transmission switching, the data that cannot be transmitted during the storage switching process is entered into the buffer area After the switch is completed, the cache area The data in the system is transmitted first;

[0030] After each data packet is transmitted, the data center sends a confirmation signal; if it is not in the timestamp If an acknowledgment signal is received within a certain period of time, the data packet is retransmitted.

[0031] As a preferred solution of the data management method based on the centralized meter reading collector described in the present invention, the dynamic encryption includes: Divided into Data packets, ; Use hash chain to generate dynamic keys, the formula is:

[0032] ,

[0033] in, represents the basic key; i represents the data packet number; represents XOR operation; Hash represents cryptographic hash function;

[0034] The key is updated after each data packet is transmitted, and the formula is expressed as:

[0035] ,

[0036] in, Indicates the timestamp; for each data packet Use a hybrid encryption algorithm for encryption, the formula is expressed as:

[0037] ,

[0038] in, Represents a symmetric encryption algorithm; an asymmetric encryption algorithm is used to encrypt a symmetric key Encryption is performed, and the formula is expressed as:

[0039] ,

[0040] in, Represents an asymmetric encryption algorithm; Represents the public key of the recipient;

[0041] Combine the encrypted data packet and the encryption key into a transmission packet:

[0042] ,

[0043] The encrypted packets are transmitted one by one; the receiver confirms each packet and if it is not in the timestamp Receive confirmation signal within a period of time , then extract and retransmit the data packet from the buffer;

[0044] After receiving the input encrypted data, the data packet is decrypted and assembled; the data center uses the private key to decrypt the key , get the symmetric key The formula is:

[0045] ,

[0046] in, Represents an asymmetric decryption algorithm; Represents the recipient's private key; uses symmetric keys Data Packet Decrypt to get the original data packet , the formula is:

[0047] ,

[0048] in, Represents a symmetric decryption algorithm; the decrypted data packets are combined into a complete data packet according to the timestamp.

[0049] As a preferred solution of the data management method based on the centralized meter reading collector described in the present invention, the data analysis includes splitting and preprocessing the decrypted data packets and performing statistical analysis. The standardized formula is expressed as:

[0050] ,

[0051] in, Representation characteristics The mean of Representation characteristics The standard deviation of the standardized data set Divide into training set and test set , train the machine learning model using the training set;

[0052] Build B decision trees , ; Each decision tree The training is performed using a bootstrap subsample of the training set; the formula for classifying the data is expressed as:

[0053] ,

[0054] The genetic algorithm is used to search for the best parameters in the parameter space to achieve the highest classification accuracy. The formula is expressed as:

[0055] ,

[0056] in, represents the accuracy of genetic algorithm optimization. Represents model parameters; after classification, the instrument source, user information, and timestamp label of the data are given for each group of data.

[0057] As a preferred solution of the data management method based on the centralized meter reading collector described in the present invention, the intelligent alarm system includes: after the data center performs analysis, it makes adjustments according to the time period when transmission switching and time-divided transmission occur; batch transmission of data is performed during the period when network traffic usage is low;

[0058] According to the classified and labeled meter reading data, the support vector machine model is used to identify and trace abnormal power consumption data. The formula is expressed as:

[0059] ,

[0060] ,

[0061] in, represents the weight vector; represents the bias of the hyperplane; represents the slack variable; represents the proportional parameter for controlling outliers; Represents data points Mapping in feature space;

[0062] The decision function is expressed as:

[0063] ,

[0064] in, represents the Gaussian kernel function; represents the Lagrange multiplier;

[0065] when When the data is detected, it is judged as abnormal data, and the data is traced according to the label, and the source instrument is alarmed. The abnormal detection algorithm is used to detect the operating status of the source instrument. If the collector detects the instrument in three If no instrument abnormalities are detected during the time period, the user will receive an electricity usage alarm reminder.

[0066] A data management system based on a centralized meter reading collector using any method as described in the present invention, wherein:

[0067] The data collection module uses a centralized meter reading collector to collect meter data in real time to ensure the accuracy and timeliness of data collection;

[0068] The data transmission module uses a dual-mode wireless transmission switching method to transmit data from the collector to the data center; it dynamically encrypts data during transmission to protect data security;

[0069] Data preprocessing and storage module, data receiving unit, receives encrypted data from the transmission module; decrypts the received data to ensure data availability; performs preprocessing operations on the data, stores the preprocessed data, and supports efficient query and management;

[0070] The data analysis and intelligent alarm module includes a data analysis engine, an alarm system, and an automated response module. It analyzes the data in the database, generates reports and predicts results; establishes an intelligent alarm system, and automatically responds based on the analysis results.

[0071] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0072] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0073] Beneficial effects of the present invention: The present invention adopts a dual-mode wireless transmission switching method, and ensures the stability and efficiency of data transmission through intelligent switching between cellular networks and low-power wide area networks. At the same time, dynamic encryption technology is introduced to encrypt the transmitted data in real time to ensure the security of the data during transmission. In the data center, the present invention also introduces data preprocessing and intelligent analysis mechanisms, and realizes in-depth analysis of data and automatic response to abnormal situations by establishing an intelligent alarm system. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0075] Figure 1 This is an overall flow chart of a data management method based on a centralized meter reading collector provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0076] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0077] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a data management method based on a centralized meter reading collector, comprising:

[0078] S1: Use a centralized meter reading collector to collect meter data.

[0079] Furthermore, collecting meter data includes: the centralized meter reading collector collects meter data by connecting various meters through wired and wireless methods; connecting the A and B lines of the RS485 converter to the A and B ends of the RS485 interface of the meter respectively; configuring the collector and the meter, setting the address of each meter through the DIP switch and configuration software; configuring the communication parameters of the collector, baud rate, data bit, stop bit, and check bit, to ensure consistency with the meter.

[0080] Furthermore, the collector periodically obtains the readings of each meter, and performs preliminary storage and organization of the data; it records the status data of each transmission, including signal strength, transmission rate, delay, packet loss rate, current transmission mode, number of network switching times, and switching effect.

[0081] It should be noted that the cellular network, as the main transmission method, ensures high data transmission rate and stability; LPWAN, as a backup transmission method, provides a reliable transmission path when the cellular network fails or the signal is poor. This method significantly improves the reliability and flexibility of data transmission and reduces data loss or delay caused by network failures. In addition, the intelligent switching mechanism dynamically selects the best transmission method according to the network conditions, optimizing data transmission efficiency and stability.

[0082] S2: Use dual-mode wireless transmission switching method to intelligently select the transmission mode and transmit meter reading data to the data center.

[0083] Furthermore, the dual-mode wireless transmission includes: a communication module supporting cellular network transmission and low-power wide area network transmission is embedded in the collector, cellular network transmission is used as the main transmission mode, and low-power wide area network transmission is used as the backup transmission mode; assuming that the signal strength is , the transmission rate is , delay is , the packet loss rate is , the transmission state vector is expressed as X 0 =[S,R,L,P] .

[0084] Furthermore, a time-divided transmission strategy is adopted to perform batch transmission of data during the period of low network traffic; the period of low network traffic is set as , the normal transmission period is .

[0085] Furthermore, the amount of data is , the transmission time is , in the period To perform a batch transfer:

[0086] ,

[0087] Furthermore, during the period To transfer cache data:

[0088] ,

[0089] Furthermore, a storage space is set in the collector to cache the data that cannot be transmitted temporarily. When the network condition is restored, the data in the cache is transferred to the data center in batches. The cached data is transferred in batches when the network condition is restored:

[0090] ,

[0091] in, Indicates the amount of cached data; Indicates the amount of data transferred to the data center; Indicates the amount of data transferred in batches; during periods of low network traffic and normal transmission period The division and update of the data center is determined by the data transmission volume received.

[0092] Furthermore, the intelligent selection of transmission mode includes the network status monitoring function of the integrated stacking forest model, which detects the signal strength and transmission quality of each transmission mode in real time; and intelligently selects the transmission mode according to the site conditions and network conditions.

[0093] Furthermore, the original data is cleaned, standardized or normalized; the transmission status features of the transmission data are extracted; and the random forest model is trained using the feature-extracted data.

[0094] Furthermore, the random forest model judges the transmission mode based on signal strength, transmission rate, delay, and packet loss rate; the transmission mode switching logic formula is expressed as:

[0095] ,

[0096] in, Represents the number of decision trees in the random forest; Indicates The prediction results of a decision tree; Indicates the comprehensive prediction results; It represents the feature vector, i.e. the transmission state vector.

[0097] Furthermore, The value range of is [0,1]; if , indicating that the comprehensive prediction probability of cellular network transmission is higher, then cellular network transmission is selected; It indicates that the comprehensive prediction probability of LPWAN transmission is higher, and LPWAN transmission is selected.

[0098] Furthermore, when the state is switched, redundant transmission and smooth transition are performed, and redundant transmission is started. During the switching process, after the new transmission mode is started, the old transmission mode continues to transmit data. period of time to ensure redundant transmission; After a while, confirm that the new transmission method is stable and turn off the old transmission method.

[0099] Furthermore, during the transmission switching, the data that cannot be transmitted during the storage switching process is entered into the buffer area After the switch is completed, the cache area The data in the transmission is given priority.

[0100] Furthermore, after each data packet is transmitted, the data center sends a confirmation signal; if it is not in the timestamp If an acknowledgment signal is received within a certain period of time, the data packet is retransmitted.

[0101] It should be noted that, according to the network traffic situation, batch transmission of data is carried out during the period of low network traffic, effectively avoiding the network peak period, reducing transmission delay and packet loss rate. This method optimizes the selection of transmission time period and improves the efficiency and stability of data transmission. By setting up cache space in the collector to temporarily store data that cannot be transmitted immediately, batch transmission is carried out after the network condition is restored, further ensuring the reliability and integrity of data transmission.

[0102] S3: Dynamic encryption is used to protect data security when transmitting meter reading data. Data preprocessing is performed in the data center and entered into the database after processing.

[0103] Furthermore, the dynamic encryption includes: Divide into Data packets, the formula is expressed as, ; Use hash chain to generate dynamic keys, the formula is:

[0104] ,

[0105] in, represents the basic key; i represents the data packet number; represents XOR operation; Hash represents cryptographic hash function.

[0106] Furthermore, the key is updated after each data packet is transmitted, and the formula is expressed as:

[0107] ,

[0108] in, Indicates a timestamp.

[0109] Furthermore, for each data packet Use a hybrid encryption algorithm for encryption, the formula is expressed as:

[0110] ,

[0111] in, Represents a symmetric encryption algorithm; an asymmetric encryption algorithm is used to encrypt a symmetric key Encryption is performed, and the formula is expressed as:

[0112] ,

[0113] in, Represents an asymmetric encryption algorithm; Represents the recipient's public key.

[0114] Furthermore, the encrypted data packet and the encryption key are combined into a transmission packet:

[0115] ,

[0116] Furthermore, the encrypted packets are transmitted one by one; the receiver confirms each packet and if it is not in the timestamp, Receive confirmation signal within a period of time , the data packet is extracted from the buffer and retransmitted.

[0117] Furthermore, after receiving the input encrypted data, the data packet is decrypted and assembled; the data center uses the private key to decrypt the key , get the symmetric key The formula is:

[0118] ,

[0119] in, Represents an asymmetric decryption algorithm; Represents the recipient's private key; uses symmetric keys Data Packet Decrypt to get the original data packet , the formula is:

[0120] ,

[0121] in, Represents a symmetric decryption algorithm; the decrypted data packets are combined into a complete data packet according to the timestamp.

[0122] It should be noted that dynamic encryption technology is used to protect data during data transmission. Specifically, the data is divided into multiple data packets, and a dynamic key is generated using a hash chain to encrypt each data packet. The security and integrity of the transmitted data are ensured through a hybrid encryption algorithm (a combination of symmetric encryption and asymmetric encryption). The dynamic encryption mechanism can effectively prevent data from being stolen or tampered with during transmission, thereby improving the security of data transmission. This technology is a further improvement on traditional static encryption and can cope with ever-changing security threats.

[0123] S4: Perform data analysis in the database and establish an intelligent alarm system for automated response.

[0124] Furthermore, data analysis includes splitting and preprocessing the decrypted data packets and performing statistical analysis. The standardized formula is expressed as:

[0125] ,

[0126] in, Representation characteristics The mean of Representation characteristics The standard deviation of the standardized data set Divide into training set and test set , use the training set to train the machine learning model.

[0127] Furthermore, we construct B decision trees. , ; Each decision tree The training is performed using a bootstrap subsample of the training set; the formula for classifying the data is expressed as:

[0128] ,

[0129] The genetic algorithm is used to search for the best parameters in the parameter space to achieve the highest classification accuracy. The formula is expressed as:

[0130] ,

[0131] in, represents the accuracy of genetic algorithm optimization. Represents model parameters; after classification, the instrument source, user information, and timestamp label of the data are given for each group of data.

[0132] Furthermore, the intelligent alarm system includes, after the data center conducts analysis, making adjustments based on the time periods when transmission switching and time-sharing transmission occur; and performing batch transmission of data during periods when network traffic usage is low.

[0133] Furthermore, based on the classified and labeled meter reading data, the support vector machine model is used to identify and trace abnormal power consumption data. The formula is expressed as follows:

[0134] ,

[0135] ,

[0136] in, represents the weight vector; represents the bias of the hyperplane; represents the slack variable; represents the proportional parameter for controlling outliers; Represents data points Mapping in feature space.

[0137] Furthermore, the decision function is expressed as:

[0138] ,

[0139] in, represents the Gaussian kernel function; represents the Lagrange multiplier.

[0140] Furthermore, when When the data is detected, it is judged as abnormal data, and the data is traced according to the label, and the source instrument is alarmed. The abnormal detection algorithm is used to detect the operating status of the source instrument. If the collector detects the instrument in three If no instrument abnormalities are detected during the time period, the user will receive an electricity usage alarm reminder.

[0141] It should be noted that after the data is transmitted to the data center, it is pre-processed, including data cleaning, standardization and other operations to ensure the consistency and validity of the data. In-depth data analysis is performed in the database, and machine learning algorithms such as random forest models are used to classify and predict the data. By establishing an intelligent alarm system, real-time monitoring and response to abnormal data can be achieved. The intelligent alarm system can automatically generate alarm information based on the analysis results and trigger corresponding response measures, which improves the intelligence and automation level of the system.

[0142] On the other hand, this embodiment also provides a data management system based on a centralized meter reading collector, which includes:

[0143] The data collection module uses a centralized meter reading collector to collect meter data in real time to ensure the accuracy and timeliness of data collection.

[0144] The data transmission module uses a dual-mode wireless transmission switching method to transmit data from the collector to the data center; it dynamically encrypts data during transmission to protect data security.

[0145] The data preprocessing and storage module, the data receiving unit, receives the encrypted data from the transmission module; decrypts the received data to ensure data availability; performs preprocessing operations on the data, stores the preprocessed data, and supports efficient query and management.

[0146] The data analysis and intelligent alarm module includes a data analysis engine, an alarm system, and an automated response module. It analyzes the data in the database, generates reports and predicts results; establishes an intelligent alarm system, and automatically responds based on the analysis results.

[0147] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0148] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0149] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0150] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0151] Embodiment 2: The following is an embodiment of the present invention, which provides a data management method based on a centralized meter reading collector. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0152] The experiment selected five different areas and set up centralized meter reading collectors to verify the effectiveness and advantages of the present invention. The meter data in each area was wired through an RS485 converter, and the collector and meter were configured to ensure consistent communication parameters. The experimental data included signal strength, transmission rate, delay, packet loss rate, current transmission mode, network switching times and switching effect.

[0153] The test steps are as follows:

[0154] Data collection: Install a centralized meter reading collector in each area and connect each meter. Collect meter data through wired (RS485 interface) and wireless (LoRa / NB-IoT). Each collector configures communication parameters, including baud rate, data bit, stop bit and check bit, to ensure consistency with the meter. The collector regularly obtains the readings of each meter and preliminarily stores and organizes the data.

[0155] Dual-mode wireless transmission: The collector has a built-in communication module that supports cellular network and low-power wide area network transmission. The cellular network is the main transmission mode, and the low-power wide area network is the backup transmission mode. During the transmission process, the signal strength, transmission rate and delay are monitored in real time, and the transmission mode is intelligently selected to switch to the optimal transmission path.

[0156] Data preprocessing and storage: Receive the encrypted data packets in the data center and perform decryption operations to ensure data availability. Perform preprocessing operations on the data, including data cleaning and standardization, and enter the processed data into the database. Through the above steps, the effectiveness of the data collection, transmission, security and intelligent analysis of the present invention is verified, and some results are obtained, as shown in Table 1.

[0157] Table 1 Experimental data of some equipment

[0158]

[0159] The table data shows the performance of the present invention under different test objects, signal strength and transmission rate: the signal strength of test object C is higher (-65dBm), and the transmission rate is relatively the highest (130kbps), indicating that when the signal strength is good, the data transmission efficiency is significantly improved. The dual-mode wireless transmission switching method can intelligently select the best transmission mode to ensure that a high transmission rate can be maintained in different environments.

[0160] Delay and packet loss rate: Test object C has the lowest delay (70ms) and packet loss rate (0.3%), indicating that the dual-mode wireless transmission switching method can effectively reduce delay and packet loss rate in practical applications. Dynamic encryption protects data security while ensuring the stability and reliability of data transmission.

[0161] Network switching times and switching effects: Subject C had the least number of network switching times (1 time) and the highest switching effect (98%), indicating that intelligent selection of transmission modes and redundant transmission mechanisms can effectively reduce the number of network switching times and improve switching effects. The time-divided transmission strategy performs batch data transmission during periods of low network traffic, further optimizing transmission efficiency.

[0162] Through comparative analysis, it can be seen that the present invention has significant advantages in data transmission reliability, security and intelligent analysis. The innovations such as dual-mode wireless transmission switching method, dynamic encryption protection, and intelligent selection of transmission methods have achieved good results in practical applications, solving the problems of unstable data transmission, insufficient security, and limited analysis capabilities in the prior art.

[0163] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A data management method based on a centralized meter reading collector, characterized in that: include: Use centralized meter reading collector to collect meter data; A dual-mode wireless transmission switching method is used to transmit meter reading data to the data center; Dynamic encryption is used to protect data security when transmitting meter reading data. Data preprocessing is performed in the data center and entered into the database after processing. Perform data analysis in the database and establish an intelligent alarm system for automated response; The dual-mode wireless transmission includes: a communication module supporting cellular network transmission and low-power wide area network transmission is embedded in the collector, with cellular network transmission as the main transmission mode and low-power wide area network transmission as the backup transmission mode; assuming that the signal strength is S, the transmission rate is R, the delay is L, and the packet loss rate is P, the transmission state vector is expressed as X0=[S, R, L, P]; The time-divided transmission strategy is adopted to perform batch data transmission during the period with low network traffic. The period with low network traffic is set as t low , the normal transmission period is t normal ; The amount of data is D, the transmission time is T, and in time period t low To perform a batch transfer: D batch =R low ·t low In period t normal To transfer cache data: D cached =D-D batch Set up storage space in the collector to cache data that cannot be transmitted temporarily. When the network condition is restored, the data in the cache is transferred to the data center in batches. The cached data is transferred in batches when the network condition is restored: D cached →D center Among them, D cached Indicates the amount of cached data; D center Indicates the amount of data transmitted to the data center; D batch Indicates the amount of data transferred in batches; during periods of low network traffic, t low and normal transmission period t normal The division and update of the data center is determined by the data transmission situation based on the amount of data received; The intelligent selection of transmission mode includes integrating the network status monitoring function of the stacking forest model to detect the signal strength and transmission quality of each transmission mode in real time; intelligently selecting the transmission mode according to the site conditions and network conditions; Clean, standardize or normalize the original data; extract the transmission status features of the transmission data; train the random forest model with the feature-extracted data; The random forest model judges the transmission mode based on signal strength, transmission rate, delay, and packet loss rate; the transmission mode switching logic formula is expressed as: Where K represents the number of decision trees in the random forest; Represents the prediction result of the kth decision tree; represents the comprehensive prediction result; X0 represents the feature vector, i.e., the transmission state vector; The value range of is [0, 1]; if It indicates that the comprehensive prediction probability of cellular network transmission is higher, and cellular network transmission is selected; It indicates that the comprehensive prediction probability of LPWAN transmission is higher, and LPWAN transmission is selected; When switching states, redundant transmission and smooth transition are performed, and redundant transmission is started. During the switching process, after the new transmission mode is started, the old transmission mode continues to transmit data. α period of time to ensure redundant transmission; t α After a period of time, confirm that the new transmission method is stable and turn off the old transmission method; During the transmission switching, the data that cannot be transmitted during the storage switching process is entered into the buffer area D cached After the switch is completed, the cache area D cached The data in the system is transmitted first; After each data packet is transmitted, the data center sends a confirmation signal; if the confirmation signal is not received within the time period t of the timestamp, the data packet is retransmitted; The intelligent alarm system includes, after the data center performs analysis, making adjustments according to the time periods when transmission switching and time-divided transmission occur; performing batch transmission of data during periods when network traffic usage is low; According to the classified and labeled meter reading data, the support vector machine model is used to identify and trace abnormal power consumption data. The formula is expressed as: subject to w·φ(x i )≥ρ-ξ i ,ξ i ≥0,i=1,…,n Among them, w represents the weight vector; ρ represents the bias of the hyperplane; ξ i represents the slack variable; v represents the proportional parameter for controlling abnormal points; φ(x i ) represents the data point x i Mapping in feature space; The decision function is expressed as: Among them, K(x i , x) represents the Gaussian kernel function; α i represents the Lagrange multiplier; When f(x)<0, it is determined to be abnormal data, and the data is traced back according to the label, and an alarm is issued to the source instrument. The abnormality detection algorithm is used to detect the operating status of the source instrument. If the collector detects no instrument abnormality within three T time periods, the user will be prompted with an electricity usage alarm.

2. The data management method based on the centralized meter reading collector according to claim 1, characterized in that: The collecting of meter data includes: the centralized meter reading collector collects meter data by connecting each meter through wired and wireless methods; connecting the A and B lines of the RS485 converter to the A and B ends of the RS485 interface of the meter respectively; configuring the collector and the meter, setting the address of each meter through the DIP switch and the configuration software; configuring the communication parameters of the collector, baud rate, data bit, stop bit, check bit, to ensure consistency with the meter; The collector periodically obtains the readings of each meter, and performs preliminary storage and organization of the data; it records the status data of each transmission, including signal strength, transmission rate, delay, packet loss rate, current transmission mode, number of network switching, and switching effect.

3. The data management method based on the centralized meter reading collector according to claim 2 is characterized in that: The dynamic encryption includes dividing the data D into n data packets, D = {d1, d2, ..., d n }; Use the hash chain to generate a dynamic key, the formula is: Among them, k base represents the basic key; i represents the data packet number; represents XOR operation; Hash represents cryptographic hash function; The key is updated after each data packet is transmitted, and the formula is expressed as: Where t represents the timestamp; for each data packet d i Use a hybrid encryption algorithm for encryption, the formula is expressed as: yes i =E sym (d i ,k i ) Among them, E sym Represents a symmetric encryption algorithm; an asymmetric encryption algorithm is used to encrypt the symmetric key k i Encryption is performed, and the formula is expressed as: K i =E asym (k i ,public key) Among them, E asym Represents an asymmetric encryption algorithm; public key represents the public key of the recipient; Combine the encrypted data packet and the encryption key into a transmission packet: packet i =(e i ,K i ) The encrypted data packets are transmitted one by one; the receiver confirms each data packet. If the confirmation signal ASK is not received within the time period of the timestamp t, i , then extract and retransmit the data packet from the buffer; After receiving the input encrypted data, the data packet is decrypted and assembled; the data center uses the private key to decrypt the key K i , get the symmetric key k i The formula is: k i =Q asym (K i ,private key) Among them, Q asym Indicates an asymmetric decryption algorithm; private key indicates the recipient's private key; uses the symmetric key k i For data packet e i Decrypt and get the original data packet d i , the formula is: d i =Q sym (e i ,k i ) Among them, Q sym Represents a symmetric decryption algorithm; the decrypted data packets are combined into a complete data packet according to the timestamp.

4. The data management method based on the centralized meter reading collector as claimed in claim 3 is characterized in that: The data analysis includes splitting and preprocessing the decrypted data packets and performing statistical analysis. The standardized formula is expressed as: Among them, μ j Represents feature X j The mean of j Represents feature X j The standard deviation of the standardized data set (X' j , y) is divided into training set (X train ,y train ) and the test set (X test ,y test ), use the training set to train the machine learning model; Build B decision trees Each decision tree T b The training is performed using a bootstrap subsample of the training set; the formula for classifying the data is expressed as: Use genetic algorithm to search for the best parameters in parameter space. The formula is expressed as: Among them, GA-Accuracy represents the optimal accuracy obtained by genetic algorithm optimization; after classification, the instrument source, user information, and timestamp label of the data are given for each group of data.

5. A data management system based on a centralized meter reading collector using the method according to any one of claims 1 to 4, characterized in that: The data collection module uses a centralized meter reading collector to collect meter data in real time to ensure the accuracy and timeliness of data collection; The data transmission module uses a dual-mode wireless transmission switching method to transmit data from the collector to the data center; Dynamically encrypt data during transmission to protect data security; The data preprocessing and storage module, the data receiving unit, receives the encrypted data from the transmission module; Decrypt received data to ensure data availability; Preprocess the data, store the preprocessed data, and support efficient query and management; The data analysis and intelligent alarm module includes a data analysis engine, an alarm system, and an automated response module. It analyzes the data in the database, generates reports and predicts results; establishes an intelligent alarm system, and automatically responds based on the analysis results.

6. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the data management method based on the centralized meter reading collector as described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the data management method based on a centralized meter reading collector as claimed in any one of claims 1 to 4 are implemented.

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