Communication optimization method of intelligent electric charge settlement system

Abstract

The invention discloses a communication optimization method of an intelligent electric charge settlement system. The method comprises the following steps: monitoring parameters of an environment where an intelligent electric meter is located in real time; calculating an environment comprehensive index based on the environment parameters; mapping the environment comprehensive index into an environment factor; when the environment factor is lower than a preset threshold value, adopting a preset baud rate, otherwise, dynamically generating an optimal baud rate based on the environment factor, a communication protocol theory optimal baud rate and a real-time signal-to-noise ratio; and controlling data transmission of the intelligent electric meter at the optimal baud rate.

Description

Technical Field

[0001] The present invention relates to the technical field of combining an intelligent electricity bill settlement system with environment-adaptive communication optimization, and in particular to a communication optimization method for an intelligent electricity bill settlement system. Background Art

[0002] With the rapid adoption of smart grids, electricity bill settlement systems are undergoing a transition from manual operations to automated management. The traditional model, which relies on manual meter reading, is showing signs of fatigue. Meter readers must regularly record data on-site, which is time-consuming and labor-intensive, and prone to errors and omissions due to negligence. This creates significant efficiency bottlenecks, especially in remote areas or large residential areas. Long meter reading cycles also result in data lags, making it difficult for users to keep up to date on their electricity usage and easily leading to payment disputes. Manual calculations are also fraught with problems. Faced with increasingly complex tiered electricity pricing and time-of-use pricing rules, manual calculations are not only slow but also prone to errors due to misunderstandings of policies, making it difficult to ensure accurate billing.

[0003] While existing automation technologies attempt to alleviate these issues, they still have significant limitations. For example, in remote meter reading systems, early solutions based on power line carrier communications were susceptible to grid noise, lacked transmission stability, and required manual data logging. Some systems use fixed communication parameters, leading to frequent communication interruptions in outdoor environments with high or low temperatures or strong electromagnetic interference, resulting in the loss of critical electricity usage data. Furthermore, most electricity bill processing software has relatively simple functions. For example, some solutions focus solely on optimizing user credit ratings but fail to achieve a closed loop of data collection, rule matching, and real-time calculations. Such systems respond slowly to changes in electricity price policies, require manual synchronization after updates, and are difficult to expand to support new billing dimensions such as new energy subsidies.

[0004] Current smart grid scenarios place higher demands on electricity bill settlement. On the one hand, massive numbers of smart meters generate millions of data points per second, making traditional batch processing models incapable of meeting real-time settlement requirements. On the other hand, the marketization of electricity is driving flexible mechanisms such as time-of-use pricing and dynamic peak pricing, requiring the system to adapt to new rules on a minute-by-minute basis. More critically, outdoor meters face long-standing challenges in harsh environments, such as low temperatures in northern winters, high humidity in southern summers, and electromagnetic interference near substations. These factors directly impact data transmission stability. According to tests, in outdoor scenarios with temperature fluctuations exceeding 20°C, nearly 30% of meters experience periodic communication failures. Improving communication robustness in complex environments has become a common technical pain point in the industry.

[0005] Overall, the industry urgently needs a solution that integrates high-precision data collection, dynamic rule engines, and environmentally adaptive communications to break through traditional efficiency bottlenecks and meet the multi-dimensional challenges of the smart grid era. Summary of the Invention

[0006] To address the shortcomings of existing technologies, such as coarse environmental perception, simplistic communication parameter adjustment, and insufficient adaptability to extreme scenarios, the present invention provides an environmentally adaptive communication optimization method and system for an intelligent electricity bill settlement system. This method achieves breakthroughs in both communication stability and efficiency through the following innovative designs: Refined environmental perception mechanism A pioneering composite environmental quantification model: Through dynamic weight allocation and adjustment coefficient optimization, it accurately quantifies the nonlinear impact of temperature and humidity deviations from baseline values, addressing the perception distortion problem of traditional linear models under sudden environmental changes. The temperature and humidity adjustment coefficients are limited to optimized ranges of 0.01 to 0.05 and 0.02 to 0.07, respectively, ensuring adaptability to high and low temperature scenarios.

[0007] Dynamic generation of baud rate based on multi-parameter coupling An innovative signal-to-noise ratio compression and environmental factor attenuation model integrates environmental factors, signal-to-noise ratio, and network utilization parameters. This model optimizes stability in weak signal scenarios through square root compression of the signal-to-noise ratio, while also combining exponential attenuation of environmental factors to mitigate the effects of harsh environments. Field tests show that this mechanism reduces packet loss by 81.7% and increases transmission speed by 63.9% in high-temperature and high-humidity environments (40°C, 85% RH).

[0008] Interference and channel dual closed-loop coordinated control Interference classification response: Identifies pulse-type or white noise interference based on frequency domain energy spectra, triggering differentiated strategies: pulse interference switches to spread spectrum communication (increasing bandwidth by 50%), while white noise interference enables forward error correction coding (with an error correction rate exceeding 50%). Fast channel switching: Combining the environmental stability index with real-time channel parameter scores, it completes channel switching within 120 milliseconds (traditional solutions require 500 milliseconds), resolving baud rate mismatch issues during the switching process.

[0009] The present invention specifically adopts the following technical solutions: A communication optimization method for an electricity fee intelligent settlement system comprises the following steps: Real-time monitoring of the environmental parameters of smart meters; Calculating an environmental comprehensive index based on the environmental parameters; Mapping the comprehensive environmental index into environmental factors; When the environmental factor is lower than a preset threshold, a preset baud rate is used; otherwise, an optimal baud rate is dynamically generated based on the environmental factor, the theoretical optimal baud rate of the communication protocol, and the real-time signal-to-noise ratio; The smart meter data transmission is controlled at the optimal baud rate.

[0010] Furthermore, the environmental parameters include temperature and humidity, and the environmental comprehensive index is calculated using the following formula:

[0011] Where V represents the comprehensive environmental index; n represents the number of environmental data collection times in historical environmental monitoring records; x i represents the weight value corresponding to the temperature data in the i-th environmental data collection; y i represents the weight value corresponding to the humidity data in the i-th environmental data collection; α represents the adjustment coefficient of temperature data; b represents the adjustment coefficient of humidity data; T i represents the temperature data in the i-th environmental data collection; W i represents the humidity data in the i-th environmental data collection; T e and W e They respectively represent the temperature and humidity values ​​corresponding to the ideal optimal environment where the smart meter is located.

[0012] Furthermore, the environmental factors are converted by Sigmoid function:

[0013] Among them, σ represents the environmental factor; s represents the environmental factor adjustment coefficient; V th Indicates the preset comprehensive index threshold.

[0014] Furthermore, the adjustment coefficient a The value of a is 0.01 to 0.05, the value of b is 0.02 to 0.07; the value of the environmental factor adjustment coefficient s is 0.3 to 0.7.

[0015] Furthermore, the optimal baud rate calculation formula is:

[0016] Among them, B opt Indicates the optimal baud rate; R max represents the theoretical optimal baud rate; K represents the signal-to-noise ratio of the communication network where the smart meter is located; K y It represents the minimum signal-to-noise ratio required to meet the communication quality requirements; σ represents the environmental factor; λ represents the communication utilization rate of the current communication network.

[0017] Furthermore, the determination of the preset threshold includes: When the environmental factor is lower than the preset threshold, the preset baud rate is called; When the environmental factor is not lower than the preset threshold, the optimal baud rate calculation is started.

[0018] Furthermore, after the step of real-time monitoring of the environmental parameters of the smart meter, the step further includes: Identify the type of electromagnetic interference; If it is pulse interference, switch to spread spectrum communication mode; if it is white noise interference, enable forward error correction coding.

[0019] Furthermore, when the interference level exceeds a threshold, recalibration of the environmental factor threshold is triggered according to the interference level.

[0020] Furthermore, before determining whether the environmental factor is lower than a preset threshold, the following steps are also included: Calculate the comprehensive channel score; If the current channel score is lower than the threshold, the baud rate is adjusted after switching to the optimal channel.

[0021] And, an intelligent electricity bill settlement system, characterized by comprising a communication optimization module for executing the method described above.

[0022] Compared with the prior art, the present invention and its preferred embodiments have at least the following beneficial effects: Improve communication stability in complex environments Through a unique environmental quantification model and multi-parameter coupling mechanism, the robustness of smart meter communications is significantly enhanced in harsh conditions such as drastic temperature and humidity fluctuations and electromagnetic interference. A core innovation—dynamic baud rate adjustment driven by environmental factors—optimizes data transmission reliability from the bottom up, avoiding the frequent interruptions that plague traditional fixed-parameter models in extreme scenarios.

[0023] Optimize the real-time and accuracy of billing processes The environmentally adaptive communication module works closely with the electricity billing system to ensure efficient collection and transmission of electricity usage data, mitigating billing discrepancies caused by data loss or delays. Combined with a dynamic electricity pricing policy engine, it enables minute-by-minute response to complex rules such as tiered electricity pricing and surcharges, enhancing the automation level of the entire process.

[0024] Enhance system scalability and adaptability Interference classification response mechanisms (such as pulse interference switching to spread spectrum communication and white noise-enabled error correction coding) and fast channel switching design form a dual closed-loop control link, supporting rapid adaptation to new interference scenarios in the future. The modular architecture allows for flexible expansion of billing dimensions (such as green power subsidies) to meet the needs of continuous rule iteration in the evolving power market.

[0025] Reduce operation and maintenance costs and manual dependence Adaptive optimization of communication parameters reduces the need for manual intervention, while improved channel switching efficiency significantly reduces maintenance costs. The fully automated design covers data collection, billing, and reconciliation, eliminating the redundant manual meter reading and accounting required in traditional models, aligning with the trend toward unmanned O&M in smart grids.

[0026] The above effects form a technical closed loop: environmental perception ensures reliable communication → reliable communication supports accurate data → accurate data drives efficient billing → efficient system adapts to future development, systematically solving the core pain points of electricity bill settlement in the power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments: Figure 1 This is a module diagram of an electricity bill data processing system in an intelligent electricity bill settlement according to an embodiment of the present invention; Figure 2 This is a flow chart of a method for processing electricity bill data in an intelligent electricity bill settlement according to an embodiment of the present invention; Figure 3 This is a workflow diagram of the communication optimization module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. Based on these detailed descriptions, those skilled in the art will be able to clearly understand the present application and implement the present application. Without violating the principles of the present application, the features of different embodiments may be combined to obtain new implementations, or certain features of certain embodiments may be substituted to obtain other preferred implementations.

[0029] To make the features and advantages of the present invention more clearly understood, the following embodiments are specifically described in detail with reference to the accompanying drawings. The embodiment of the present invention provides an electricity bill data processing system in the intelligent electricity bill settlement, such as Figure 1 As shown, it includes a data acquisition module, an electricity price policy module, an electricity fee calculation module, an account management module and a reconciliation generation module. The data acquisition module can read data from the smart meter in real time or at a fixed time, ensuring the timeliness and accuracy of the data. The preliminary verification mechanism further improves the reliability of the data and reduces the calculation deviation caused by data errors. The electricity price policy module provides a flexible user interface to facilitate administrators to enter, modify and delete electricity price policy information, so that the system can adapt to different electricity price policies and market changes. At the same time, the function of regularly updating electricity price policies ensures the timeliness and compliance of the system. The electricity fee calculation module can automatically read the user's electricity consumption information and electricity price policy information from the database, perform accurate calculations according to different time periods and tiered electricity price segments, and automatically add additional fees, which greatly improves the calculation efficiency and accuracy and reduces the burden of manual calculations.

[0030] Specifically: The data acquisition module is used to communicate with the smart meter, read the meter data from the smart meter data acquisition device in real time or at a fixed time, collect the user's electricity usage information, including the amount of electricity used and the time of electricity use, perform preliminary verification on the collected data and store it in the database; The electricity price policy module provides a user interface that allows administrators to enter, modify, and delete electricity price policy information. The electricity price policy information includes electricity prices for different user types and different electricity usage periods, tiered electricity price segments, and additional fees. The electricity price policy information is stored in the database and queried by the electricity fee calculation module. The electricity price policy is also updated regularly. The electricity fee calculation module is used to read the user's electricity usage information and electricity price policy information from the database, apply the electricity price policy, calculate the electricity fee according to different time periods and tiered electricity prices, and add additional fees. The additional fees include the basic electricity fee and the power factor adjustment electricity fee, and the total electricity fee payable by the user is summarized; The account management module is used to provide a user account database, store user account balance information, and perform deduction or recharge operations based on electricity fee calculation results; The reconciliation generation module is used to organize electricity bill calculation results, account balance changes, electricity usage information data, format the output data, and generate a user-readable electricity bill statement. The electricity bill statement includes user information, billing cycle, electricity usage, electricity bill details, amount payable, account balance changes, and provides statement query and download functions.

[0031] Furthermore, the data acquisition module includes: The communication unit is used to set communication parameters with the smart meter, establish a connection with the smart meter, send data request commands to the smart meter according to predetermined data reading rules, wait for and receive data packets returned by the smart meter, parse the received data packets, and extract power consumption and power consumption time information; The data verification storage unit is used to check the integrity of the received data packet, verify whether the data is within the normal range based on the reasonable range of the meter data, build a database table structure, and store electricity usage information. The electricity usage information includes user ID, meter number, electricity usage time, electricity consumption, and record the timestamp of data storage.

[0032] The communication unit adopts the communication optimization module designed in the present invention, and its basic working mechanism is as follows: Figure 3 As shown, including: Environmental data extraction module, used to extract temperature data and humidity data of the environment where the smart meter is located from historical environmental monitoring records; The environment comprehensive index acquisition module is used to obtain the environment comprehensive index based on the temperature data and humidity data of the environment in which the smart meter is located. The environment comprehensive index is obtained by the following formula:

[0033] Where V represents the comprehensive environmental index; n represents the number of environmental data collection times in historical environmental monitoring records; x i represents the weight value corresponding to the temperature data in the i-th environmental data collection; y i represents the weight value corresponding to the humidity data in the i-th environmental data collection; α represents the adjustment coefficient of temperature data, ranging from 0.01 to 0.05; b represents the adjustment coefficient of humidity data, ranging from 0.02 to 0.07; T i represents the temperature data in the i-th environmental data collection; W i represents the humidity data in the i-th environmental data collection; T e and W e They respectively represent the temperature and humidity values ​​corresponding to the ideal optimal environment where the smart meter is located; The environmental factor acquisition module is used to use the environmental factor corresponding to the environment in which the smart meter is located using the environmental comprehensive index. The environmental factor is obtained by the following formula:

[0034] Among them, σ represents the environmental factor; s represents the environmental factor adjustment coefficient, and the value range of the environmental factor adjustment coefficient is 0.3-0.7; V represents the comprehensive environmental index; V th Indicates the preset comprehensive index threshold; A comparison module, for comparing the environmental factor with a preset environmental factor threshold; A first baud rate parameter setting module is used to retrieve a preset baud rate value from a database to set the communication baud rate parameter of the smart meter when the environmental factor is lower than a preset environmental factor threshold; The second baud rate parameter setting module is used to set the communication baud rate parameter of the smart meter using the environmental factor when the environmental factor is not lower than a preset environmental factor threshold.

[0035] The technical effect of the above technical solution is that by extracting temperature and humidity data from the smart meter's environment and combining this data to calculate a comprehensive environmental index, the smart meter can more accurately perceive its operating environment. This accurate perception helps the smart meter adjust its operating state based on actual conditions to better adapt to environmental changes, thereby improving its operational stability and reliability. Based on the calculated environmental factors, the smart meter can dynamically adjust its communication baud rate parameters. When the environmental factor is below a preset threshold, the smart meter retrieves a preset baud rate value from a database and sets it to ensure stable communication performance even under adverse environmental conditions. When the environmental factor is above the preset threshold, the communication baud rate parameter is directly set based on the environmental factor to further optimize communication performance. By dynamically adjusting the communication baud rate parameters, the smart meter can maintain optimal communication efficiency under varying environmental conditions. This helps reduce communication errors and data loss, and improves the reliability and stability of data transmission. This technical solution gives the smart meter stronger adaptability, enabling it to automatically adjust to varying environmental conditions and communication requirements. This helps extend the lifespan of the smart meter, reduce maintenance costs, and improve the overall system efficiency.

[0036] In summary, the above technical solution improves the communication efficiency and stability of smart meters and enhances their adaptive capabilities by accurately sensing the environment in which they are located and dynamically adjusting their communication baud rate parameters, thereby providing users with more reliable and efficient electricity monitoring and management services.

[0037] Furthermore, the second baud rate parameter setting module includes: Environmental factor retrieval module, used to retrieve the currently obtained environmental factors; The communication protocol retrieval module is used to retrieve the communication protocol corresponding to the current communication of the smart meter; Theoretical optimal baud rate acquisition module, used to obtain the theoretical optimal baud rate corresponding to the communication protocol according to the communication protocol; A signal-to-noise ratio acquisition module is used to acquire the signal-to-noise ratio corresponding to the communication network where the smart meter is located; The optimal baud rate setting module is used to set the optimal baud rate using environmental factors, the theoretical optimal baud rate, and the signal-to-noise ratio corresponding to the communication network where the smart meter is located. The optimal baud rate is obtained by the following formula:

[0038] Among them, B opt Indicates the optimal baud rate; R max represents the theoretical optimal baud rate; K represents the signal-to-noise ratio of the communication network where the smart meter is located; K yIt represents the minimum signal-to-noise ratio required to meet the communication quality requirements; σ represents the environmental factor; λ represents the communication utilization rate of the current communication network.

[0039] The technical effect of the above technical solution is that, through the environmental factor retrieval module, the system can perceive changes in the smart meter's environment in real time and use this environmental factor as a key parameter for adjusting the communication baud rate. This helps the smart meter maintain optimal communication performance under different environmental conditions, improving its adaptability to the environment. The communication protocol retrieval module allows the system to obtain the theoretical optimal baud rate corresponding to the communication protocol currently used by the smart meter. This step ensures that the baud rate adjustment matches the requirements of the communication protocol, thereby optimizing communication efficiency and quality. The signal-to-noise ratio retrieval module allows the system to consider the signal-to-noise ratio of the communication network in which the smart meter is located. The signal-to-noise ratio is an important indicator of communication quality. By incorporating it into the optimal baud rate calculation, the system can more accurately adjust the baud rate to ensure the best communication performance under the existing communication conditions. The optimal baud rate setting module calculates the optimal baud rate using a given formula by comprehensively considering multiple factors, including environmental factors, the theoretical optimal baud rate, the signal-to-noise ratio, and the communication network's communication utilization. This comprehensive decision-making mechanism ensures that baud rate adjustments comply with both communication protocol requirements and the actual communication environment and conditions, thereby optimizing both communication efficiency and quality. By dynamically adjusting to the optimal baud rate, smart meters can maintain efficient communication performance under varying environmental conditions. This helps reduce communication delays and data loss, improves the reliability and stability of data transmission, and ultimately enhances the operational efficiency and safety of the entire power system. By automatically adjusting the baud rate to adapt to environmental changes and communication needs, this technical solution reduces the need for manual intervention. This helps reduce maintenance costs and improves the system's automation and intelligence.

[0040] In summary, the above technical solution dynamically adjusts the smart meter's communication baud rate by comprehensively considering multiple factors, achieving dual optimization of communication efficiency and quality. This technical solution not only improves the smart meter's environmental adaptability and communication performance, but also helps reduce maintenance costs and improve the operational efficiency and safety of the entire power system.

[0041] The technical solution of the present invention is further explained with respect to the design experimental framework: Table 1 Test scenario control group Experimental group Core variables Observation indicators High temperature and high humidity environment Fixed baud rate (9600bps) Dynamic baud rate adjustment Temperature (35°C / 40°C / 45°C) Communication delay, packet loss rate, transmission rate Low temperature and low humidity environment Fixed baud rate (9600bps) Dynamic baud rate adjustment Humidity (10%RH / 20%RH / 30%RH) Communication stability, bit error rate Complex electromagnetic environment Fixed baud rate (9600bps) Dynamic baud rate adjustment Signal-to-noise ratio (10dB / 15dB / 20dB) Data integrity and retransmission times Table 2 High temperature and high humidity environment test data (temperature 40°C, humidity 85% RH): Test scenario control group Experimental group Optimization rate Average communication delay 120ms 45ms 62.5% Packet loss rate 8.2% 1.5% 81.7% Effective transmission rate 7.2kbps 11.8kbps 63.9% Table 3 Experimental data in low temperature and low humidity environment (temperature -10°C, humidity 15% RH): Test scenario control group Experimental group Optimization rate Communication stability Occasional interruptions Continuous stability - Bit Error Rate <![CDATA[3.5×10 -4 ]]> <![CDATA[8.2×10 -6 ]]> 97.7% Table 4 Experimental data in complex electromagnetic environment (SNR 15dB): Test scenario control group Experimental group Optimization rate Data integrity 92% 99.2% 7.8% Number of retransmissions 5 times / minute 1 time / minute 80% As shown in Tables 1-4, the dynamic baud rate adjustment mechanism significantly improves communication performance in extreme environments. In particular, the packet loss rate is reduced by 81.7% in high-temperature and high-humidity scenarios, and the bit error rate is close to zero in low-temperature environments. In electromagnetic interference scenarios, baud rate optimization combined with signal-to-noise ratio improves data integrity to over 99%, verifying the effectiveness of multi-factor linkage adjustment.

[0042] Furthermore, as a preferred solution of this embodiment, the data acquisition module further includes: The interference adaptive adjustment unit is used to collect the time domain waveform of the electromagnetic signal strength in the communication frequency band of the smart meter in real time through the wideband sensor integrated in the communication unit, generate a frequency domain energy distribution spectrum, and extract interference characteristic parameters including center frequency, bandwidth, and power spectrum density from the interference signal; Establish a preset interference feature library, dynamically match the interference feature parameters extracted in real time with the feature library, calculate the similarity and determine the current interference type; When the interference type is determined to be narrowband continuous wave interference, an interference level signal is sent to the comparison module, triggering the dynamic correction mechanism of the environmental factor threshold. The corresponding communication protocol adaptation strategy is called according to the interference type. If it is pulse interference, it automatically switches to spread spectrum communication mode to improve anti-interference capability by increasing the transmission bandwidth. If it is white noise interference, the forward error correction coding mechanism is activated to perform coding redundancy processing on the transmitted data based on the Reed-Solomon algorithm. The channel dynamic optimization unit is used to periodically scan the list of available channels within the communication range of the smart meter to obtain the real-time received signal strength indication and channel occupancy rate of each channel; Retrieve the current environment temperature and humidity data from the environmental data extraction module and calculate the environmental stability index of each channel based on historical data; Setting scoring weights for the real-time received signal strength indicator, channel occupancy rate, and environmental stability index, calculating a comprehensive quality score for each channel based on the real-time received signal strength indicator, channel occupancy rate, and environmental stability index, and generating a channel priority list based on the comprehensive quality score; When the comprehensive quality score of the current channel is lower than the preset threshold, a pre-switching signal is sent to the second baud rate parameter setting module to suspend the current baud rate adjustment operation; the idle channel with the highest comprehensive quality score is selected according to the priority list, and the protocol parameters adapted to the channel are requested to the communication protocol calling module to perform channel switching; The technical effects of this solution include: real-time monitoring of electromagnetic interference across the entire frequency band, capturing transient interference signals of pulse-type interference, improving the accuracy of interference type identification in complex electromagnetic environments (such as near substations), and implementing differentiated protocol adjustments for different interference types. For example, in pulse-type interference scenarios, bit error rates are reduced by switching to spread-spectrum communication mode. In white noise interference environments, over 50% of burst errors are automatically corrected. Channel selection accuracy is improved in scenarios with drastic temperature and humidity fluctuations (such as outdoor meters with a day-night temperature difference greater than 20°C), reducing frequent channel switching caused by environmental factors. A pre-switching coordination mechanism with the baud rate setting module resolves the baud rate mismatch issue in traditional channel switching, reducing switching time from an average of 500ms to 120ms and improving switching success rates. The adaptive interference adjustment unit and the dynamic channel optimization unit form a dual closed-loop control system, enabling fully automated collaborative optimization of communication parameters in complex electromagnetic environments. The linkage between the interference feature parameter extraction and the baud rate adjustment module builds a feedforward control link; while the interaction between the channel quality assessment and the protocol adaptation module forms feedback control, improving the system response speed.

[0043] Furthermore, as a preferred solution of this embodiment, the electricity price policy module includes: The policy entry editing unit is used to build a user interface. The user interface contains input boxes, selection boxes, and buttons for various fields of electricity price policy information. The administrator enters electricity price policy information through the user interface. The system verifies the legitimacy and integrity of the input data and stores the verified electricity price policy information in the database. An interface is provided for the administrator to select specific electricity price policy items for modification and deletion operations. The system updates the corresponding records in the database based on the operations. The policy query and display unit is used to provide a query interface, allowing the retrieval of electricity price policy information based on conditions such as user type and electricity consumption period, and displaying the retrieved electricity price policy information in the form of tables and charts; The policy update maintenance unit is used to set up scheduled tasks or reminder mechanisms, record the update history of electricity price policies, and inform administrators and relevant users through system notifications and emails after the electricity price policies are updated.

[0044] Furthermore, as a preferred solution of this embodiment, the electricity fee calculation module includes: A data reading unit is used to retrieve the electricity usage information of a specified user and the currently effective electricity price policy information from the database through a database query interface, pre-process the read electricity usage information, and integrate the pre-processed electricity usage information with the corresponding electricity price policy information in preparation for electricity fee calculation; The electricity fee calculation unit is used to determine the electricity price rate corresponding to each electricity usage period according to the time period division rules in the electricity usage time and electricity price policy. Calculate the electricity fee. According to the additional fee items specified in the electricity price policy, calculate and add the additional fees to the total electricity fee to obtain the total electricity fee that the user should pay.

[0045] Furthermore, when the electricity fee calculation unit calculates the electricity fee, if the electricity price policy includes a tiered electricity price, the electricity fee for each segment is calculated according to the user's total electricity consumption and the tiered electricity price segmentation rules, and the total electricity fee for the tiered electricity price portion is obtained by summing up.

[0046] Furthermore, as a preferred solution of this embodiment, the account management module includes: The account information maintenance unit is used for users to create accounts and record their basic information and initial account balance in the user account database, store the user account information in the database, and update the account information according to user needs or system rules; The deduction and recharge operation unit is used to perform deduction operations according to the electricity fee calculation results, receive the user's recharge request and perform recharge operations, and update the account balance.

[0047] A method for processing electricity bill data in intelligent electricity bill settlement obtained by the above system of this embodiment is as follows: Figure 2 As shown, the following steps are included: Step 1: Establish a connection with the smart meter through the communication unit of the data acquisition module, read the meter data in real time or periodically, and the data verification storage unit checks the integrity of the data packet and the rationality of the data, and then stores the valid data in the database and records the timestamp of the data storage.

[0048] Step 2: The electricity price policy module enters, modifies and deletes electricity price policy information, updates electricity price policy regularly or according to system reminders, and informs administrators and relevant users through the notification mechanism.

[0049] Step 3: The data reading unit of the electricity fee calculation module retrieves the user's electricity usage information and the currently valid electricity price policy information from the database, calculates the electricity fee for each time period based on the electricity usage time and the time period of the electricity price policy, and takes into account the tiered electricity price and additional fees, and finally summarizes the total electricity fee that the user should pay.

[0050] Step 4: The reconciliation generation module organizes the electricity bill calculation results, account balance changes and electricity usage information data to generate a user-readable electricity bill statement.

[0051] Compared with the prior art, the electricity bill data processing system and method for the above-mentioned intelligent electricity bill settlement provided by this embodiment have the following advantages and positive effects: the present invention ensures the timeliness and accuracy of the data by reading data from the smart meter in real time or on a scheduled basis. The preliminary verification mechanism further improves the reliability of the data and reduces the calculation deviation caused by data errors. The electricity price policy module provides a flexible user interface to facilitate administrators to enter, modify and delete electricity price policy information, so that the system can adapt to different electricity price policies and market changes. At the same time, the function of regularly updating the electricity price policy ensures the timeliness and compliance of the system. It automatically reads the user's electricity usage information and electricity price policy information from the database, performs accurate calculations according to different time periods and tiered electricity price segments, and automatically adds additional fees, greatly improving the calculation efficiency and accuracy, reducing the burden of manual calculations, and providing a user account database to facilitate users to view their account balances and transaction records at any time. At the same time, the reconciliation generation module can automatically generate electricity bill statements, detailing the user's electricity usage, electricity bill details, and account balance changes, improving user convenience and system transparency. Real-time, accurate electricity bill calculation and transparent bill display can enhance user trust and satisfaction with electricity bill settlement.

[0052] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0053] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, performs the above-described method. The storage medium may be any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0054] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0055] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present disclosure. Various changes and improvements may be made to the present disclosure without departing from the spirit and scope of the present disclosure, and such changes and improvements shall fall within the scope of the present disclosure.

[0056] The present invention is not limited to the above-mentioned optimal implementation mode. Anyone can derive other forms of communication optimization methods for electricity fee intelligent settlement systems under the inspiration of the present invention. All equal changes and modifications made within the scope of the patent application of the present invention should fall within the scope of the present invention.

Claims

1. A communication optimization method for an electricity fee intelligent settlement system, characterized in that: The following steps are involved: Real-time monitoring of the environmental parameters of smart meters; Calculating an environmental comprehensive index based on the environmental parameters; Mapping the comprehensive environmental index into environmental factors; When the environmental factor is lower than a preset threshold, a preset baud rate is used; otherwise, an optimal baud rate is dynamically generated based on the environmental factor, the theoretical optimal baud rate of the communication protocol, and the real-time signal-to-noise ratio; The smart meter data transmission is controlled at the optimal baud rate.

2. The communication optimization method of the electricity fee intelligent settlement system according to claim 1, characterized in that: The environmental parameters include temperature and humidity, and the environmental comprehensive index is calculated using the following formula: Where V represents the comprehensive environmental index; n represents the number of environmental data collection times in historical environmental monitoring records; x i represents the weight value corresponding to the temperature data in the i-th environmental data collection; y i represents the weight value corresponding to the humidity data in the i-th environmental data collection; α represents the adjustment coefficient of temperature data; b represents the adjustment coefficient of humidity data; T i represents the temperature data in the i-th environmental data collection; W i represents the humidity data in the i-th environmental data collection; T e and W e They respectively represent the temperature and humidity values ​​corresponding to the ideal optimal environment where the smart meter is located.

3. The communication optimization method of the electricity fee intelligent settlement system according to claim 2, characterized in that: The environmental factors are obtained by Sigmoid function conversion: Among them, σ represents the environmental factor; s represents the environmental factor adjustment coefficient; V th Indicates the preset comprehensive index threshold.

4. The communication optimization method of the electricity fee intelligent settlement system according to claim 3, characterized in that: Adjustment coefficient a The values ​​of a range from 0.01 to 0.05, and b range from 0.02 to 0.07; The environmental factor adjustment coefficient s ranges from 0.3 to 0.

7.

5. The communication optimization method of the electricity fee intelligent settlement system according to claim 1, characterized in that: The optimal baud rate calculation formula is: Among them, B opt Indicates the optimal baud rate; R max represents the theoretical optimal baud rate; K represents the signal-to-noise ratio of the communication network where the smart meter is located; K y It represents the minimum signal-to-noise ratio required to meet the communication quality requirements; σ represents the environmental factor; λ represents the communication utilization rate of the current communication network.

6. The communication optimization method of the electricity fee intelligent settlement system according to claim 1, characterized in that: The determination of the preset threshold includes: When the environmental factor is lower than the preset threshold, the preset baud rate is called; When the environmental factor is not lower than the preset threshold, the optimal baud rate calculation is started.

7. The communication optimization method of the electricity fee intelligent settlement system according to claim 1, characterized in that: After the step of real-time monitoring of the environmental parameters of the smart meter, the method further includes: Identify the type of electromagnetic interference; If it is pulse interference, switch to spread spectrum communication mode; if it is white noise interference, enable forward error correction coding.

8. The communication optimization method of the electricity fee intelligent settlement system according to claim 7, characterized in that: When the interference level exceeds the threshold, recalibration of the environmental factor threshold is triggered according to the interference level.

9. The communication optimization method of the electricity fee intelligent settlement system according to claim 1, characterized in that: Before determining whether the environmental factor is lower than the preset threshold, the following steps are also included: Calculate the comprehensive channel score; If the current channel score is lower than the threshold, the baud rate is adjusted after switching to the optimal channel.

10. An intelligent electricity bill settlement system, characterized in that: The method comprises a communication optimization module for executing the method according to any one of claims 1 to 9.

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