Big data-based environmental sanitation operation energy consumption management method and system

Abstract

The invention discloses an environmental sanitation operation energy consumption management method and system based on big data, and relates to the technical field of intelligent environmental sanitation, and the method comprises the steps: collecting and preprocessing the operation data of environmental sanitation operation, and calculating the energy consumption of a battery based on the preprocessed operation data; constructing an objective function according to the energy consumption of the battery to calculate a Pareto optimal solution set, executing the Pareto optimal solution set, and optimizing the objective function; and constructing a visual interface to display and optimize the objective function, and storing, collecting and analyzing the generated environmental sanitation operation operation data. According to the method, the operation data of environmental sanitation operation are collected and preprocessed, and the energy consumption of the battery is calculated based on the preprocessed operation data; constructing an objective function according to the energy consumption of the battery to calculate a Pareto optimal solution set, executing the Pareto optimal solution set, and optimizing the objective function; the energy waste and the operation cost are reduced, the accuracy and the utilization efficiency of energy consumption management are improved, and the flexibility and the adaptability of operation management are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent environmental sanitation, and particularly to a method and system for energy consumption management of environmental sanitation operations based on big data. Background Art

[0002] With the acceleration of the globalization process, the level of urbanization has been continuously improving. Urban environmental sanitation operations have become an important part of urban management. Environmental sanitation operations not only concern the cleanliness of the city, but also involve energy consumption, environmental impact, and the effective utilization of resources. In current environmental sanitation operations, traditional operation methods usually rely on manual scheduling and empirical route planning, lacking effective monitoring and optimization of energy consumption, resulting in waste of resources and low operation efficiency. With the rapid development of information technology, especially the wide application of big data, the Internet of Things, and artificial intelligence, the energy efficiency management of environmental sanitation operations has gradually developed towards intelligence and dataization. By installing sensors and building a data collection and analysis platform, the energy consumption status of environmental sanitation vehicles can be monitored in real time, and dynamic scheduling and optimization can be carried out, thereby effectively improving operation efficiency and energy utilization rate.

[0003] Existing methods and systems for energy consumption management of environmental sanitation operations based on big data still have deficiencies. Existing technologies often lack the dynamic optimization ability for multi-factor impacts in complex environments, and it is difficult to fully consider the impacts of factors such as weather changes and road conditions on environmental sanitation operations, resulting in insufficient accuracy and flexibility of energy consumption management. Some existing environmental sanitation energy consumption optimization algorithms mostly adopt single-objective optimization methods, ignoring the multi-objective coordination requirements existing in actual operations. Summary of the Invention

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

[0005] Therefore, the present invention provides a method and system for energy consumption management of environmental sanitation operations based on big data to solve the problems that existing technologies often lack the dynamic optimization ability for multi-factor impacts in complex environments, it is difficult to fully consider the impacts of factors such as weather changes and road conditions on environmental sanitation operations, resulting in insufficient accuracy and flexibility of energy consumption management, and some existing environmental sanitation energy consumption optimization algorithms mostly adopt single-objective optimization methods, ignoring the multi-objective coordination requirements existing in actual operations.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for energy consumption management of environmental sanitation operations based on big data, which includes collecting operation data of environmental sanitation operations and performing preprocessing, calculating battery energy consumption based on the preprocessed operation data; constructing an objective function according to the battery energy consumption to calculate the Pareto optimal solution set, executing the Pareto optimal solution set and optimizing the objective function; constructing a visual interface to display the optimized objective function, and storing the operation data of environmental sanitation operations generated by collection and analysis.

[0008] As a preferred solution of the method for energy consumption management of environmental sanitation operations based on big data according to the present invention, wherein: the collecting operation data of environmental sanitation operations and performing preprocessing refers to using intelligent sensors to collect operation data of environmental sanitation operations and performing preprocessing, including:

[0009] The intelligent sensors include a voltage sensor, a current sensor, a GPS sensor, an electronic level, a load sensor, a lidar, and a temperature sensor;

[0010] The operation data includes voltage, current, vehicle driving speed, road surface slope angle, vehicle mass, garbage accumulation height, garbage heap area, and temperature;

[0011] The preprocessing includes time-aligning the operation data using a timestamp alignment method, identifying and deleting outliers using the quartile method, filling in missing values using the mean filling method, and performing standardization processing on the filled operation data.

[0012] As a preferred solution of the method for energy consumption management of environmental sanitation operations based on big data according to the present invention, wherein: the calculating battery energy consumption based on the preprocessed operation data refers to calculating the garbage accumulation volume at time t using the integration method according to the garbage accumulation height and garbage heap area in the preprocessed operation data , the formula is:

[0013] ,

[0014] where is the total area of the garbage accumulation area, is the garbage accumulation height, is the area element of the integration region;

[0015] According to the garbage accumulation volume at time t, calculate the garbage heap density at time t;

[0016] According to the vehicle mass, vehicle driving speed, road surface slope angle, and garbage heap density in the preprocessed operation data, calculate the load power demand at time t;

[0017] Calculate the battery power demand at time t based on the neutral current in the pre - processed operating data and the discharge percentage ;

[0018] Add the load power demand at time t and the battery power demand to calculate the total power demand ;

[0019] Calculate the mean value of the temperature using the moving average method to set the reference temperature ;

[0020] Obtain the random initial attenuation coefficient , the initial battery efficiency and the activation energy from the technical documents of the battery manufacturer

[0021] Calculate the temperature influence factor at time t using the Arrhenius model based on the temperature in the pre - processed operating data ;

[0022] Calculate the battery start - up time to the final attenuation coefficient at time t according to the initial attenuation coefficient and the temperature influence factor , the formula is:

[0023] ,

[0024] where is the discharge percentage at time t;

[0025] Calculate the final battery efficiency parameter at time t using the battery efficiency calculation model according to the final attenuation coefficient at time t ; ;

[0026] Calculate the battery energy consumption at time t according to the total power demand at time t and the final battery efficiency parameter ; .

[0027] As a preferred solution of the energy consumption management method for environmental sanitation operations based on big data according to the present invention, wherein: the construction of the objective function according to the battery energy consumption to calculate the Pareto optimal solution set means calculating the task load L according to the garbage density at time t and the total area of the garbage stacking area ;

[0028] According to the vehicle driving speed at time t Calculate the task execution time for the task load L ;

[0029] Set the cross - effect adjustment coefficient using linear interpolation method ;

[0030] According to the cross - effect adjustment coefficient , the battery energy consumption at time t and the task execution time , construct the objective function based on the cross - effect , the formula is:

[0031] ,

[0032] Randomly generate the initial population, where an individual represents a combination of task execution time and battery energy consumption;

[0033] Bring the individual into the objective function F for evaluation and calculate the fitness value of the individual;

[0034] Use the tournament selection method to randomly select individuals for comparison and select the individual with a higher fitness as the parent to participate in the crossover operation;

[0035] Use single - point crossover to exchange the genes of the parent individuals to generate new offspring individuals,

[0036] After crossover, randomly select individuals to perform mutation using single - point mutation to generate new individuals;

[0037] Perform Pareto sorting on the individuals, identify the dominance relationship, sort according to the non - dominance relationship, and select the Pareto optimal solutions;

[0038] Calculate the crowding degree of each solution and select the solution with a larger crowding degree to enter the next generation;

[0039] Use the empirical rule method to set the maximum number of iterations. When the maximum number of iterations is reached, stop the iteration and output the Pareto optimal solution set, including the optimized task execution time and battery energy consumption.

[0040] As a preferred solution of the energy consumption management method for environmental sanitation operations based on big data according to the present invention, wherein: executing the Pareto optimal solution set and optimizing the objective function means using a dynamic scheduling algorithm to convert the Pareto optimal solution set into scheduling instructions, transmitting them to the control platform through the API interface, and the control platform receives the scheduling instructions, executes them, and collects the execution operation data;

[0041] Calculate the difference between the execution operation data and the optimal solution set;

[0042] Use statistical methods to set a judgment threshold, compare the differences with the judgment threshold, and adjust the cross-effect adjustment coefficient of the objective function for the differences greater than or equal to the judgment threshold , and stop adjusting until the difference is less than the judgment threshold, and continue to monitor the execution of the operation data.

[0043] As a preferred solution of the energy consumption management method for environmental sanitation operations based on big data according to the present invention, wherein: the construction of the visualization interface to display the optimization objective function means using the front-end framework Vue.js to construct the visualization interface to display the optimization objective function and the Pareto optimal solution set;

[0044] Allow users who have passed real-name verification to view.

[0045] As a preferred solution of the energy consumption management method for environmental sanitation operations based on big data according to the present invention, wherein: the storage of the operation data of environmental sanitation operations generated by collection and analysis means storing the collected operation data and the Pareto optimal solution set generated by analysis in the central database. The central database is sorted according to the time sequence and marked with corresponding tags, and at the same time, the collected operation data and the Pareto optimal solution set generated by analysis are backed up to the cloud, and the integrity of the backup data is detected regularly.

[0046] In a second aspect, the present invention provides an energy consumption management system for environmental sanitation operations based on big data, including,

[0047] A collection and calculation module for collecting the operation data of environmental sanitation operations and performing preprocessing, and calculating the battery energy consumption based on the preprocessed operation data;

[0048] A construction and optimization module for constructing an objective function according to the battery energy consumption to calculate the Pareto optimal solution set, and executing the Pareto optimal solution set and optimizing the objective function;

[0049] A visualization and storage module for constructing a visualization interface to display the optimization objective function and storing the operation data of environmental sanitation operations generated by collection and analysis.

[0050] In a third aspect, the present invention provides a computer device, including a memory and a processor, wherein: when the computer program stored in the memory is executed by the processor, any step of the energy consumption management method for environmental sanitation operations based on big data as described in the first aspect of the present invention is implemented.

[0051] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program is executed by the processor, any step of the energy consumption management method for environmental sanitation operations based on big data as described in the first aspect of the present invention is implemented.

[0052] The beneficial effects of the present invention are as follows: By collecting and preprocessing the operation data of sanitation operations, calculating the battery energy consumption based on the preprocessed operation data, constructing an objective function according to the battery energy consumption to calculate the Pareto optimal solution set, executing the Pareto optimal solution set and optimizing the objective function, the present invention reduces energy waste and operating costs, improves the accuracy and utilization efficiency of energy consumption management, and enhances the flexibility and adaptability of operation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0054] Figure 1 It is a flowchart of the energy consumption management method for sanitation operations based on big data in Embodiment 1.

[0055] Figure 2 It is a schematic diagram of the energy consumption management system for sanitation operations based on big data in Embodiment 1. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0057] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0058] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that excludes other embodiments.

[0059] Embodiment 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides an energy consumption management method for sanitation operations based on big data, including the following steps:

[0060] S1. Collect the operation data of sanitation operations and perform preprocessing, and calculate the battery energy consumption based on the preprocessed operation data;

[0061] Specifically, collecting and preprocessing the operation data of environmental sanitation operations means using intelligent sensors to collect and preprocess the operation data of environmental sanitation operations, including:

[0062] The intelligent sensors include voltage sensors, current sensors, GPS sensors, electronic level gauges, load sensors, lidar, and temperature sensors;

[0063] The operation data includes voltage, current, vehicle driving speed, road surface slope angle, vehicle mass, garbage accumulation height, garbage heap area, and temperature;

[0064] The preprocessing includes time-aligning the operation data using the timestamp alignment method, identifying and removing outliers using the quartile method, filling in missing values using the mean filling method, and standardizing the filled operation data.

[0065] By using intelligent sensors to collect various operation data in environmental sanitation operations in real time, it can comprehensively and accurately reflect all possible factors affecting energy consumption during the environmental sanitation operation process, providing multi-dimensional and reliable basic data for subsequent energy consumption calculation and optimization. Through the timestamp alignment method, the data collected by all sensors can be accurately aligned according to time, eliminating the data misalignment problem caused by time deviation, enabling all data to be synchronously analyzed at the same time point. In environmental sanitation operations, sensors may have data anomalies due to different environmental factors (such as extreme weather, equipment failures, etc.). If not removed, it may seriously affect the accuracy of subsequent analysis. It avoids data inconsistency or analysis interruption caused by missing values. For multiple data sources in environmental sanitation operations, standardization processing can effectively eliminate the magnitude differences between different data, ensure that the data has the same weight when input into the model, and prevent some data from having too much or too little influence on the results. It not only enhances the comparability of the data but also improves the calculation efficiency and accuracy of subsequent algorithms, especially when conducting large-scale data analysis, it has significant advantages.

[0066] Furthermore, calculating the battery energy consumption based on the preprocessed operation data means calculating the garbage accumulation volume at time t using the integration method according to the garbage accumulation height and garbage heap area in the preprocessed operation data , the formula is:

[0067] ,

[0068] where is the total area of the garbage accumulation area, is the garbage accumulation height, is the area element of the integration region;

[0069] Directly estimating the volume of garbage accumulation using simple geometric shapes will overlook the differences in the accumulation height of garbage at different locations. The integration method, by summing up each small area element over the entire region, can accurately calculate the actual volume of garbage accumulation, avoiding the errors caused by over-simplification, reflecting the temporal changes and spatial distribution of garbage accumulation. Other methods (such as the average method, linear interpolation, etc.) cannot provide such high-precision calculations. The simple summation method or the average height method within the region will be overly simplistic and may underestimate or overestimate the volume of garbage accumulation, leading to mistakes in management decisions. The integration method, by considering the actual height distribution within each region, can provide a more accurate volume estimate. This method avoids the errors of underestimation or overestimation that may occur in traditional methods. The integration method can comprehensively consider the height differences of all points within the region, thus obtaining a more real and accurate volume of garbage accumulation.

[0070] According to the volume of garbage accumulation at time t , calculate the garbage density at time t , the formula is:

[0071] ,

[0072] According to the vehicle mass, vehicle driving speed, road surface slope angle, and garbage density in the preprocessed operation data, calculate the load power demand at time t , the formula is:

[0073] ,

[0074] where is the vehicle mass at time t, is the vehicle driving speed at time t, is the road surface slope angle;

[0075] According to the voltage and current in the preprocessed operation data, calculate the battery power demand and the discharge percentage , the formula is:

[0076] ,

[0077] ,

[0078] where is the voltage at time t, is the current at time t, is the output current at time t, is the energy consumption time interval, is the total battery capacity;

[0079] The load power demand at time t and the battery power demand Add them up to calculate the total power demand ;

[0080] Use the moving average method to calculate the mean value of the temperature and set the reference temperature ;

[0081] Obtain the random initial attenuation coefficient , the initial battery efficiency and the activation energy from the technical documents of the battery manufacturer;

[0082] According to the temperature in the pre - processed operation data, use the Arrhenius model to calculate the temperature influence factor at time t , and the formula is:

[0083] ,

[0084] where is the temperature at time t, R is the gas constant, representing the ratio of the energy of each mole of ideal gas to the temperature, 8.314 J / (mol·K), which is used to describe the influence of temperature on battery performance;

[0085] According to the initial attenuation coefficient and the temperature influence factor , calculate the final attenuation coefficient at time t , and the formula is:

[0086] ,

[0087] where is the discharge percentage at time t;

[0088] The attenuation of the battery is mainly closely related to its depth of discharge. Traditional attenuation models often assume that the attenuation of the battery is uniform or only depends on the battery usage time, ignoring the significant influence of the depth of discharge on battery attenuation. Introducing the discharge percentage can dynamically adjust the attenuation coefficient to match the actual usage situation of the battery. The battery is usually affected by the external environmental temperature. Introducing a dynamic temperature influence factor provides a more accurate attenuation prediction and avoids the deviation of battery performance estimation caused by temperature changes. Traditional battery attenuation models often ignore environmental changes and differences in operating conditions and use a fixed attenuation coefficient to estimate battery attenuation. By introducing the attenuation coefficient calculation, this method can consider the actual working state of the battery in real - time, provide a more accurate attenuation model, adapt to environmental temperature changes in real - time, greatly improve the accuracy and practical applicability of the model, adjust the attenuation rate in real - time, thereby improving the accuracy of prediction, and simulate the attenuation process of the battery under different working conditions, thus avoiding over - simplifying the estimation of battery health;

[0089] According to the final decay coefficient at time t , use the battery efficiency calculation model to calculate the final battery efficiency parameter at time t , and the formula is:

[0090] ,

[0091] where is the battery startup time;

[0092] According to the total power demand at time t and the final battery efficiency parameter , calculate the battery energy consumption at time t , and the formula is:

[0093] ,

[0094] where is the battery discharge time interval.

[0095] Calculating the garbage accumulation volume through the integration method helps to evaluate the traction force required in the operation and the energy consumption during the cleaning process, avoid the errors that may occur in the traditional approximate calculation method, improve the accuracy of energy consumption prediction, and further optimize the operation scheduling and resource allocation. By accurately calculating the garbage heap density, it is possible to more accurately predict the load power required during the cleaning process, providing a more scientific decision-making basis for the energy management of sanitation operations. By comprehensively considering various factors, the power required for the cleaning task can be accurately calculated. The optimized power demand calculation model helps to improve the operation efficiency, reduce unnecessary energy waste, and helps the scheduling system to adjust the task priority and vehicle configuration in real time, thereby enhancing the overall operation efficiency. Accurate calculation of the battery power demand can help to monitor the battery usage status in real time, predict the remaining battery energy, and perform intelligent management of battery charging and discharging, extend the battery life, reduce the charging frequency, and improve the energy utilization rate of sanitation operations. By applying the Arrhenius model, the present invention can quantify the influence of temperature on the battery decay coefficient, optimize the battery usage strategy, early warn the battery life, and avoid excessive energy consumption caused by temperature changes, not only improving the accuracy of energy management, but also helping users to more effectively schedule sanitation operations, ensuring that tasks are completed on time and the energy consumption is within a reasonable range.

[0096] S2. Construct an objective function based on the battery energy consumption to calculate the Pareto optimal solution set, execute the Pareto optimal solution set, and optimize the objective function;

[0097] Specifically, constructing an objective function based on the battery energy consumption to calculate the Pareto optimal solution set means according to the garbage heap density at time t and the total area of the garbage accumulation area , calculate the task load L, with the formula:

[0098] ,

[0099] According to the vehicle driving speed at time t and the task load L, calculate the task execution time , with the formula:

[0100] ,

[0101] Use the linear interpolation method to set the cross-effect adjustment coefficient ;

[0102] According to the cross-effect adjustment coefficient , the battery energy consumption at time t and the task execution time , construct the objective function based on the cross-effect , with the formula:

[0103] ,

[0104] The cross-effect adjustment coefficient is introduced to reflect the interaction between the task execution time and the battery energy consumption, which is an important factor that cannot be ignored in multi-objective optimization. It reflects the mutual influence between the task execution time and the battery energy consumption under specific operating conditions. There is a close coupling between the battery energy consumption and the task execution time. Traditional objective functions usually optimize these two aspects separately, but this separate treatment will lead to incomplete optimization results. The attenuation characteristic enables the objective function to adapt to the reality that the influence of the task execution time on the energy consumption gradually weakens when facing high energy consumption. Many optimization methods optimize the task execution time and the battery energy consumption separately without fully considering the cross-effect between them, adopting static optimization methods and ignoring the dynamic changes of the task execution time and the battery state during the actual execution process. The introduction of the exponential decay model can simulate the more complex relationship between the energy consumption and the task execution time, especially the decreasing effect of the energy consumption under high load conditions, improving the accuracy and practical applicability of the optimization;

[0105] Randomly generate the initial population, and the individual represents the combination of the task execution time and the battery energy consumption;

[0106] Bring the individual into the objective function F for evaluation and calculate the fitness value of the individual;

[0107] Use the tournament selection method to randomly select individuals for comparison and select the individuals with higher fitness as the parents to participate in the crossover operation;

[0108] Use single-point crossover to exchange the genes of the parent individuals to generate new offspring individuals,

[0109] After crossover, individuals are randomly selected and mutated using single-point mutation to generate new individuals;

[0110] The individuals are sorted by Pareto to identify the dominance relationship, and sorted according to the non-dominance relationship to select the Pareto optimal solutions;

[0111] Calculate the crowding degree of each solution, and select the solutions with a larger crowding degree to enter the next generation;

[0112] Use the empirical rule method to set the maximum number of iterations. When the maximum number of iterations is reached, stop the iteration and output the Pareto optimal solution set, including the optimized task execution time and battery energy consumption.

[0113] Calculate the task load based on the garbage dump density and stacking area, providing basic data for the subsequent optimization process. By quantifying the task load, it can help achieve precise task scheduling and optimal resource allocation. By reasonably adjusting the mutual influence between different tasks, it can accurately simulate the interaction effects between tasks in actual operations, thus avoiding errors caused by task interference, improving the accuracy of the objective function, making the optimization of task execution time and battery energy consumption more scientific. The objective function models the relationship between task execution time and battery energy consumption, providing a clear direction for the optimization of the genetic algorithm. By performing multi-objective optimization on the objective function, a set of optimal balance solutions between energy consumption and time execution can be found. Use the tournament selection method for individual selection, which can ensure that the excellent individuals in each generation can participate in the crossover operation, while enhancing the diversity of the population and avoiding falling into local optimal solutions. The genetic algorithm can explore the solution space more comprehensively, thus finding the global optimal solution. The calculation of the crowding degree can ensure that the solution set finally output by the genetic algorithm has a high optimization level in terms of time consumption and energy consumption.

[0114] Furthermore, executing the Pareto optimal solution set and optimizing the objective function means using a dynamic scheduling algorithm to convert the Pareto optimal solution set into scheduling instructions, which are transmitted to the control platform through the API interface. The control platform receives the scheduling instructions, executes them, and collects the execution operation data;

[0115] Calculate the difference between the execution operation data and the optimal solution set;

[0116] Use statistical methods to set a judgment threshold, compare the difference with the judgment threshold, and adjust the cross-effect adjustment coefficient of the objective function for the difference greater than or equal to the judgment threshold until the difference is less than the judgment threshold, then stop the adjustment and continue to monitor the execution operation data.

[0117] Through the dynamic scheduling algorithm, the Pareto optimal solution set is converted into scheduling instructions, enabling the optimal allocation and execution of job tasks. The introduction of the dynamic scheduling algorithm improves the flexibility of jobs, allowing the system to quickly adapt and maintain efficient operation in the face of environmental changes. By transmitting scheduling instructions through the API interface, the control platform can receive and execute scheduling tasks in real time, greatly improving the efficiency of scheduling execution, reducing errors and delays in manual operations, and providing a basis for subsequent real-time monitoring and optimization. By detecting differences in a timely manner, the adaptability of the system to environmental changes can be improved, ensuring that tasks are executed as expected. Setting judgment thresholds improves the intelligence level of the system, enabling the adjustment of scheduling instructions to be optimized based on real data rather than relying solely on manual experience, thereby enhancing the system's self-optimization ability and accuracy. The process of adjusting the cross-effect coefficient can continuously optimize task execution strategies, ensuring an optimal balance between time consumption and energy consumption in actual operations. By continuously monitoring execution data, the system can make adaptive adjustments throughout the task execution process, ensuring the long-term efficient and stable operation of the system.

[0118] S3. Build a visual interface to display the optimization objective function and store the operation data of the sanitation operations collected, analyzed.

[0119] Specifically, building a visual interface to display the optimization objective function means using the front-end framework Vue.js to build a visual interface to display the optimization objective function and the Pareto optimal solution set.

[0120] Allow users who have passed real-name verification to view.

[0121] Using front-end frameworks such as Vue.js, the system can present the optimization results to users through a visual interface, making the optimization objective function and the Pareto optimal solution set more intuitive and understandable. This not only improves users' understanding of the optimization process but also facilitates users' real-time monitoring and adjustment of the optimization plan. Users who have passed real-name verification can query and view relevant data, enhancing the transparency and reliability of the system, making the energy consumption management of sanitation operations more refined and intelligent, and providing strong technical support for environmental protection and energy conservation.

[0122] Furthermore, storing the operation data of the sanitation operations collected, analyzed means storing the collected operation data and the Pareto optimal solution set generated by the analysis in the central database. The central database sorts the data in chronological order and marks the corresponding tags. At the same time, the collected operation data and the Pareto optimal solution set generated by the analysis are backed up to the cloud, and the integrity of the backup data is detected regularly.

[0123] Time sorting not only provides a clear timeline for subsequent data analysis but also facilitates the query and comparison of historical data. Through tags, the system can quickly locate specific types of data or events, improving the efficiency and accuracy of data analysis. The cloud storage of data also makes data access more flexible, enabling querying and analysis of data through remote access, enhancing the management efficiency of environmental sanitation operations. Data integrity detection can effectively avoid data distortion caused by storage problems, ensuring the accuracy of analysis results, thereby improving the quality of decision-making and the efficiency of execution. Based on data collection, analysis, and backup, the present invention can achieve dynamic optimization and adjustment in environmental sanitation operations.

[0124] This embodiment also provides a big data-based energy consumption management system for environmental sanitation operations, including:

[0125] A collection and calculation module for collecting the operation data of environmental sanitation operations and performing preprocessing, and calculating battery energy consumption based on the preprocessed operation data;

[0126] A construction and optimization module for constructing an objective function according to the battery energy consumption to calculate the Pareto optimal solution set, executing the Pareto optimal solution set, and optimizing the objective function;

[0127] A visualization and storage module for constructing a visualization interface to display the optimized objective function and storing the operation data of environmental sanitation operations generated by collection and analysis.

[0128] This embodiment also provides a computer device applicable to the situation of the big data-based energy consumption management method for environmental sanitation operations, including: a memory and a processor; the memory is used for storing computer-executable instructions, and the processor is used for executing the computer-executable instructions to implement the big data-based energy consumption management method for environmental sanitation operations proposed in the above embodiment.

[0129] This computer device can be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computer device is used for wired or wireless communication with an external terminal, and the wireless method can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of this computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the shell of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0130] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for energy consumption management of environmental sanitation operations based on big data as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

Claims

1. A method for managing energy consumption of sanitation operations based on big data, characterized in that: include, Collect and pre-process the operation data of sanitation operations, and calculate the battery energy consumption based on the pre-processed operation data; Construct an objective function based on battery energy consumption to calculate the Pareto optimal solution set, execute the Pareto optimal solution set and optimize the objective function; Build a visual interface to display the optimization objective function, and store, collect and analyze the operating data of sanitation operations.

2. The method for managing energy consumption of sanitation operations based on big data according to claim 1, characterized in that: The collecting and preprocessing of the operating data of the sanitation operation refers to using the intelligent sensor to collect and preprocess the operating data of the sanitation operation, including: The smart sensors include voltage sensors, current sensors, GPS sensors, electronic level gauges, load sensors, laser radars and temperature sensors; The operating data include voltage, current, vehicle speed, road slope angle, vehicle mass, garbage pile height, garbage pile area and temperature; The preprocessing includes using a timestamp alignment method to time align the operating data, using a quartile method to identify and delete outliers, using a mean filling method to fill in missing values, and standardizing the filled operating data.

3. The method for managing energy consumption of sanitation operations based on big data according to claim 2, characterized in that: The calculation of battery energy consumption based on the pre-processed operation data refers to calculating the volume of the garbage pile at time t using the integral method according to the garbage pile height and garbage pile area in the pre-processed operation data. , the formula is: ， in is the total area of ​​garbage accumulation area, is the garbage accumulation height, is the area element of the integration region; According to the volume of garbage accumulation at time t , calculate the garbage pile density at time t ; Calculate the load power demand at time t based on the vehicle mass, vehicle speed, road slope angle and garbage pile density in the pre-processed operation data ; Calculate the battery power requirement at time t based on the pre-processed operating data and current neutralization and discharge percentage ; The load power demand at time t and battery power requirements Add together to calculate the total power requirement ; Use the moving average method to calculate the mean temperature and set the reference temperature ; Obtain random initial attenuation coefficients from the battery manufacturer's technical documentation , initial battery efficiency and activation energy ; According to the temperature in the preprocessed running data, the Arrhenius model is used to calculate the temperature influence factor at time t ; According to the initial attenuation coefficient and temperature influence factors , calculate battery startup time The final attenuation coefficient at time t , the formula is: ， in is the discharge percentage at time t; According to the final attenuation coefficient at time t , use the battery efficiency calculation model to calculate the final battery efficiency parameters at time t ; According to the total power demand at time t and final battery efficiency parameters , calculate the battery energy consumption at time t .

4. The method for managing energy consumption of sanitation operations based on big data according to claim 3, characterized in that: The objective function is constructed according to the battery energy consumption to calculate the Pareto optimal solution set, which refers to the garbage dump density at time t. and the total area of ​​garbage accumulation , calculate the task load L; According to the vehicle speed at time t and task load L, calculate the task execution time ; Use linear interpolation to set the fork effect adjustment factor ; Adjust coefficients for cross effects , battery energy consumption at time t and task execution time , construct the objective function based on the cross effect , the formula is: ， The initial population is randomly generated, and each individual represents a combination of task execution time and battery energy consumption; Bring the individual into the objective function F for evaluation and calculate the individual's fitness value; Use the tournament selection method to randomly select individuals for comparison, and select individuals with higher fitness as parents to participate in the crossover operation; Use single-point crossover to exchange genes of parent individuals to produce new offspring individuals. After crossover, individuals are randomly selected to mutate using single-point mutation to generate new individuals; Perform Pareto sorting on individuals, identify dominating relationships, sort based on non-dominant relationships, and select the Pareto optimal solution; Calculate the congestion of each solution and select the solution with the larger congestion to enter the next generation; The maximum number of iterations is set using the rule of thumb method. When the maximum number of iterations is reached, the iteration is stopped and the Pareto optimal solution set is output, including the optimized task execution time and battery energy consumption.

5. The method for managing energy consumption of sanitation operations based on big data according to claim 4, characterized in that: Executing the Pareto optimal solution set and optimizing the objective function refers to using a dynamic scheduling algorithm to convert the Pareto optimal solution set into a scheduling instruction, transmitting it to the control platform through an API interface, and the control platform receiving the scheduling instruction, executing it, and collecting execution operation data; Calculate the difference between the execution running data and the optimal solution set; Use statistical methods to set the judgment threshold, compare the difference with the judgment threshold, and adjust the cross-effect adjustment coefficient of the objective function for differences greater than or equal to the judgment threshold , until the difference is less than the judgment threshold, stop adjusting and continue monitoring the execution running data.

6. The method for managing energy consumption of sanitation operations based on big data according to claim 5, characterized in that: The constructing of a visualization interface to display the optimization objective function refers to using the front-end framework Vue.js to construct a visualization interface to display the optimization objective function and the Pareto optimal solution set; Users who have passed real-name verification are allowed to view the information.

7. The method for managing energy consumption of sanitation operations based on big data according to claim 6, characterized in that: The storage, collection and analysis of the operating data of sanitation operations refers to storing the collected operating data and the Pareto optimal solution set generated by the analysis in a central database, sorting the central database in chronological order and marking the corresponding tags, and synchronously backing up the collected operating data and the Pareto optimal solution set generated by the analysis in the cloud, and regularly performing integrity checks on the backup data.

8. A sanitation operation energy consumption management system based on big data, based on the sanitation operation energy consumption management method based on big data according to any one of claims 1 to 7, characterized in that: include, A collection and calculation module is used to collect and pre-process the operation data of sanitation operations, and calculate the battery energy consumption based on the pre-processed operation data; Construct an optimization module, which is used to construct an objective function according to battery energy consumption, calculate the Pareto optimal solution set, execute the Pareto optimal solution set and optimize the objective function; The visualization storage module is used to build a visualization interface to display the optimization objective function and store the operating data of sanitation operations generated by collection and analysis.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the sanitation operation energy consumption management method based on big data described in any one of claims 1 to 7 are implemented.

10. 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 method for managing energy consumption of sanitation operations based on big data described in any one of claims 1 to 7 are implemented.

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