A big data driven intelligent scheduling and service optimization system

Abstract

This invention relates to the field of big data technology and discloses a big data-driven intelligent scheduling and service optimization system. The system includes a data integration module, a preprocessing module, a predictive analysis module, a scheduling optimization module, a service adjustment module, and a performance feedback module. During periodic and real-time scheduling planning, the data integration module integrates internal operational data with environmental data obtained from external data interface units. This data is then aligned and cleaned by the data frequency synchronization unit and the preprocessing module, ensuring the full integration and high-quality input of multi-source heterogeneous data. Simultaneously, the predictive analysis module, utilizing a time-series analysis unit and a machine learning model unit, can dynamically assess and quantify the real-time impact of external environmental factors on business needs, thereby improving the accuracy of demand forecasting results and reducing scheduling baseline deviations caused by data isolation and lag.

Description

Technical Field

[0001] This invention relates to the field of big data technology, specifically to a big data-driven intelligent scheduling and service optimization system. Background Technology

[0002] Big data refers to a large and complex set of data that traditional data processing and analysis methods are insufficient to handle. Big data not only refers to the huge volume of data, but also emphasizes the ability to extract meaningful information and knowledge from massive, multi-source, heterogeneous, and high-value data. With the rapid development of artificial intelligence, cloud computing and Internet of Things technologies, big data has become a core production factor and driving force in the digital economy era.

[0003] Currently, due to the dynamic and complex nature of the business operation environment, the historical data and external environmental data relied upon for periodic and real-time scheduling planning have not been fully integrated. This makes it impossible to assess the dynamic impact of external factors on business needs in real time. When data integration is insufficient or delayed, it can lead to significant deviations in demand forecasting and make it impossible to guarantee the accuracy of scheduling plans.

[0004] Therefore, a big data-driven intelligent scheduling and service optimization system is proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a big data-driven intelligent scheduling and service optimization system, which solves the problem mentioned in the background that it is impossible to assess the dynamic impact of external factors on business needs in real time.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a big data-driven intelligent scheduling and service optimization system, the system comprising: The data integration module uses an internal data acquisition unit to acquire historical operational data, receives environmental data through an external data interface unit, and outputs integrated data through a data frequency synchronization unit. The preprocessing module receives the integrated data, removes noise through the data cleaning unit, standardizes the data format using the data normalization unit, and generates clean data through the missing value processing unit. The predictive analysis module receives the cleaning data, identifies trends through the time series analysis unit, predicts demand using the machine learning model unit, and outputs the prediction results through the confidence calculation unit. The scheduling optimization module receives the prediction results, sets scheduling constraints through the rule base configuration unit, generates a scheduling plan using the optimization algorithm unit, and outputs optimized scheduling data through the feasibility verification unit. The service adjustment module receives the optimized scheduling data, simulates the service process through the process simulation unit, identifies inefficiency points through the bottleneck detection unit, and generates service optimization instructions through the dynamic adjustment unit. The performance feedback module receives the service optimization instructions and actual operational data, evaluates the scheduling effect through the comparative analysis unit, updates the model parameters using the adaptive learning unit, and outputs optimization suggestions through the report generation unit.

[0007] Preferably, in the data integration module, the internal data acquisition unit includes a historical sales acquisition subunit, an inventory status acquisition subunit, and a promotional activity record subunit; The historical sales data collection subunit acquires data from the same period of the past week, data from the same week of the past month, and data from the same holiday, with a collection frequency of daily updates and holiday updates. The inventory status collection subunit collects the remaining quantity of each dish after the store closes on the same day, and the collection frequency is updated at 22:00 every day; The promotional activity record sub-unit records the promotional dishes and the activity period, and the data is collected 3 days before the activity.

[0008] Preferably, the external data interface unit includes a weather data acquisition subunit, a surrounding passenger flow monitoring subunit, and a city event subscription subunit; The weather data acquisition subunit collects the probability of precipitation and temperature data for the following day, with a daily update frequency of 16:30. The weight is adjusted to 20%-25% when the probability of precipitation is greater than 90%. The surrounding passenger flow monitoring subunit monitors the daily off-get off work passenger flow data of office buildings and residential areas, with a collection frequency of 17:00 updated daily, and increases the weight of evening business light meals sales when the overtime rate is greater than 80%. The city event subscription sub-unit acquires data on exhibitions, concerts, and traffic control events, with a collection frequency of one day prior to the event, and dynamically adjusts the sales weight based on the event scale.

[0009] Preferably, in the preprocessing module, the data cleaning unit uses an outlier detection algorithm to remove invalid data points, the data normalization unit uses a minimum-maximum scaling method to unify the data to the [0,1] interval, and the missing value processing unit fills in missing data using linear interpolation. The data frequency synchronization unit identifies the differences in internal and external data acquisition frequencies based on a bidirectional search algorithm. When the frequencies are inconsistent, a linear interpolation algorithm is used to adjust the low-frequency data to the high-frequency data standard to ensure data timing alignment.

[0010] Preferably, in the predictive analysis module, the time series analysis unit identifies the periodic pattern of sales through an autoregressive integral moving average model, and the machine learning model unit integrates random forest and neural network algorithms, and calculates the demand prediction value by merging internal and external data weights. The confidence calculation unit generates confidence intervals based on historical prediction error data and outputs a time-segmented sales forecast table, including dish name, sales at noon the next day, sales at night the next day, total predicted sales, and confidence level. The confidence threshold is set to 85% or higher to be considered a reliable prediction.

[0011] Preferably, in the scheduling optimization module, the rule base configuration unit stores employee skill levels, working hour constraints, and legal and regulatory requirements, and the optimization algorithm unit adopts the particle swarm optimization algorithm with the objective function of minimizing labor costs and maximizing service efficiency. The feasibility verification unit compares the simulated scheduling plan with actual operational data, detects conflict points, and outputs adjustment suggestions to ensure that the scheduling plan meets dynamic business needs.

[0012] Preferably, in the service adjustment module, the process simulation unit constructs a discrete event simulation model to simulate customer flow and service nodes, and the bottleneck detection unit uses critical path analysis to identify service delay links. The dynamic adjustment unit generates resource reallocation instructions based on real-time passenger flow data, including increasing the number of employees during peak hours and adjusting the food preparation time. The optimization instructions are sent to the terminal device through the API interface.

[0013] Preferably, in the performance feedback module, the comparative analysis unit calculates the deviation rate between predicted sales and actual sales, and the adaptive learning unit dynamically updates the machine learning model parameters based on the deviation data and optimizes the weight allocation using the gradient descent method. The report generation unit integrates multi-dimensional indicators, including sales accuracy, employee utilization, and service satisfaction, and outputs daily and weekly reports through a visual dashboard to support decision-makers in adjusting strategies.

[0014] Preferably, the system further includes a real-time monitoring module, which receives the output data of the performance feedback module, detects emergencies through an abnormal alarm unit, and generates temporary scheduling adjustment instructions using an emergency response unit; The real-time monitoring module works in conjunction with the data integration module. When external data shows sudden weather changes or urban events, the predictive analysis module is automatically triggered to recalculate demand forecasts.

[0015] Preferably, the system further includes a user interaction module that receives user input via a web interface, including manually adjusting scheduling parameters and providing feedback on service issues; The user interaction module integrates a natural language processing unit to parse user queries and provides optimization suggestions through a recommendation engine unit, ensuring the system's ease of use and adaptability.

[0016] Compared with existing technologies, this invention provides a big data-driven intelligent scheduling and service optimization system, which has the following beneficial effects: 1. In this invention, when performing periodic and real-time scheduling planning, the data integration module integrates internal operational data with environmental data obtained from external data interface units, and aligns and cleans the data through the data frequency synchronization unit and the preprocessing module, ensuring the full integration and high-quality input of multi-source heterogeneous data. At the same time, the predictive analysis module, using the time series analysis unit and the machine learning model unit, can dynamically evaluate and quantify the real-time impact of external environmental factors on business needs, thereby improving the accuracy of demand prediction results and reducing the scheduling baseline deviation caused by data isolation and lag.

[0017] 2. In this invention, when generating and executing the scheduling plan, the rule base configuration unit and feasibility verification unit in the scheduling optimization module can detect in real time whether the scheduling plan meets multi-dimensional business constraints and compliance requirements. At the same time, the service adjustment module simulates the service process in advance through the process simulation unit and uses the abnormal alarm unit of the real-time monitoring module to dynamically monitor emergencies. This enables the system to generate and trigger adjustment instructions in real time through the emergency response unit and dynamic adjustment unit when manpower allocation is about to become excessive or insufficient, thereby ensuring that the scheduling plan always matches the dynamically changing business needs.

[0018] 3. In this invention, after scheduling and service execution are completed, the comparative analysis unit of the performance feedback module can perform multi-dimensional quantitative evaluation of the scheduling effect based on actual operational data. At the same time, the adaptive learning unit in this module can automatically update the parameters of the machine learning models in the predictive analysis module and the scheduling optimization module according to the deviation data generated by the evaluation. This makes the entire system form a complete closed loop from "data perception, intelligent prediction, optimized scheduling, execution monitoring to evaluation and learning", realizing the self-iteration and continuous optimization of the scheduling strategy, solving the problems of strategy update lag and blindness, and ensuring the stability of the system's long-term operation and its continuously improving optimization capabilities. Attached Figure Description

[0019] Figure 1 This is an architecture diagram of a big data-driven intelligent scheduling and service optimization system according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 This is a big data-driven intelligent scheduling and service optimization system, which includes: The data integration module uses an internal data acquisition unit to acquire historical operational data, receives environmental data through an external data interface unit, and outputs integrated data through a data frequency synchronization unit. The preprocessing module receives the integrated data, removes noise through the data cleaning unit, standardizes the data format using the data normalization unit, and generates clean data through the missing value handling unit. The predictive analysis module receives cleaning data, identifies trends through the time series analysis unit, predicts demand using the machine learning model unit, and outputs the prediction results through the confidence calculation unit. The scheduling optimization module receives the prediction results, sets scheduling constraints through the rule base configuration unit, generates a scheduling plan using the optimization algorithm unit, and outputs optimized scheduling data through the feasibility verification unit. The service adjustment module receives optimized scheduling data, simulates the service process through the process simulation unit, identifies inefficiencies through the bottleneck detection unit, and generates service optimization instructions through the dynamic adjustment unit. The performance feedback module receives service optimization instructions and actual operational data, evaluates scheduling effectiveness through comparative analysis, updates model parameters using adaptive learning, and outputs optimization suggestions through report generation.

[0022] The data integration module includes an internal data acquisition unit comprising a historical sales acquisition subunit, an inventory status acquisition subunit, and a promotional activity record subunit. The historical sales data collection sub-unit acquires data from the same period of the past week, the same week of the past month, and the same holiday, with a collection frequency of daily updates and holiday updates. The inventory status collection sub-unit collects the remaining quantity of each dish after the store closes for the day, and the collection frequency is updated at 22:00 every day; The promotional activity record sub-unit records the promotional dishes and the activity period, and the data is collected 3 days before the activity.

[0023] The external data interface unit includes a weather data acquisition subunit, a surrounding passenger flow monitoring subunit, and a city event subscription subunit; The weather data acquisition sub-unit collects the probability of precipitation and temperature data for the following day, with a daily update frequency of 16:30. The weight is adjusted to 23% when the probability of precipitation is greater than 90%. The surrounding passenger flow monitoring subunit monitors the daily off-get off work passenger flow data of office buildings and residential areas, with a collection frequency of 17:00 updated daily, and increases the weight of evening business light meals sales when the overtime rate is greater than 80%. The City Events Subscription sub-unit acquires data on exhibitions, concerts, and traffic control events. The data collection frequency is updated one day before the event, and the sales weight is dynamically adjusted according to the scale of the event.

[0024] In the preprocessing module, the data cleaning unit uses an outlier detection algorithm to remove invalid data points. The specific implementation steps are as follows: First, the unit receives the raw integrated data from upstream. Then, for a specific data sequence to be cleaned, the algorithm calculates its first quartile. and the third and fourth quartiles Then, calculate the interquartile range. Subsequently, based on Define the criteria for outlier detection: ; ; in, It is an adjustable parameter. It is the first quartile. It is the third quartile. Interquartile range; In the system's default configuration, the sensitivity to business forecasting is typically set to... Any data point below the lower limit or above the upper limit will be judged as invalid data point by the algorithm, and finally, the system will perform a removal operation; The data normalization unit uses a minimum-maximum scaling method to unify the data to the [0,1] interval. The specific implementation steps are as follows: First, for a specific feature data column to be processed, the system dynamically calculates two key statistics from that column of data: the global minimum value. With global maximum value In actual deployment, to adapt to the time-varying nature of business data, the following... and The calculation is performed based on a sliding time window, rather than a fixed historical set, in order to maintain adaptability to slow changes in data distribution; Subsequently, for each original data value in this feature column This unit uses the following linear transformation formula for normalization calculations: ; Among them, molecules Calculate the relative distance between the current data point and the minimum value, denominator This represents the total span of the original values ​​of this feature. For the final result, It is the global minimum value. This is the global maximum value. The original data value; This formula guarantees that when... hour, ,when hour, All data points between the minimum and maximum values ​​will be strictly linearly distributed within the [0,1] interval after scaling. The missing value handling unit fills in missing data using linear interpolation. The specific implementation process is as follows: First, the unit scans the input data sequence, identifies and locates all data points marked as "missing". For each missing value in the sequence, the algorithm determines its interpolation interval: that is, it searches forward and backward to find the nearest valid data point before and after the missing point. Subsequently, the following linear interpolation formula is applied to calculate the time... Missing values ​​at : ; in, For missing values, For time, and For time indexing, For a moment The value, For a moment The value, and For indexing; Finally, the unit will calculate the results. Automatically fill in the missing positions in the data sequence; The data frequency synchronization unit identifies differences in internal and external data acquisition frequencies based on a bidirectional search algorithm. The specific implementation steps are as follows: First, the algorithm preprocesses the two time series into arrays arranged in strict ascending order of timestamps. Let the internal data sequence be... The collection cycle is external data sequence The collection cycle is The goal of the algorithm is to Each point in time ,exist Find the two points closest to it in time. and ,in Indicates time, Representative and Time A corresponding value, and For index, subscript and It distinguishes different specific data points in the sequence. Indicates the first The first timestamp of each data point Is with The corresponding first value, Indicates the first The second timestamp of each data point Is with The corresponding first value, Indicates the first The first timestamp of each data point Is with The corresponding first value, Indicates the first The second timestamp of each data point Is with The corresponding second value; The algorithm initializes two pointers: pointer point to The starting position, pointer point to The algorithm determines the starting position and then compares and advances bidirectionally within a loop: Comparison and Mapping: Comparison timestamp and timestamp as well as timestamp ; Conditional statements and pointer movement: The core logic of the algorithm is driven by the following conditional statements: ; at this time, The pairing is complete, pointer Move forward one position and continue processing the next internal data point; External pointer advances: when This indicates the external data points. If it is still ahead of the current internal point in time, then the pointer Move forward one position to find the next contained element. The external data range; Loop and Termination: Repeat the above three steps until the pointer... and One reaches the end of its sequence. This process ensures that each internal data point can be quickly located to its corresponding external data time interval. Through this algorithm, the unit accurately identifies frequency differences. In this algorithm, For missing values, For time, and As a pointer, and For indexing, For internal data sequences, For external data sequences, The collection period; When frequencies are inconsistent, a linear interpolation algorithm is used to adjust low-frequency data to the high-frequency data standard to ensure data timing alignment. The specific implementation consists of three sub-steps: Input and Pairing: The algorithm receives the results from the upstream bidirectional search algorithm, which is a list of explicit mapping relationships for each high-frequency internal data time point. Both explicitly specify two adjacent "anchor points" and precursor points in the low-frequency external data sequence. and successor point ,in , and This represents the original value of the external data at this anchor point. Interpolation calculation: for each target time point The algorithm uses the standard linear interpolation formula for calculation, which is based on the principle that two points determine a straight line, assuming that within the time interval... Within this context, the data values ​​change linearly over time, and the target point values ​​are calculated. The formula is as follows: ; in, For missing values, For time; Output and Integration: The algorithm iterates through all internal data time points and calculates the corresponding interpolations in batches. This value is then used as the "aligned" external data, compared with the original internal data at the timestamp. The data is then bound together to form a new external data sequence with the same frequency as the internal data. Finally, this aligned data pair is output as the integrated data.

[0025] In the predictive analysis module, the time series analysis unit identifies the cyclical patterns of sales through an autoregressive integral moving average model. The implementation involves four sequential steps: Stationarity test and differencing: First, for the original sales series Perform a stationarity test; if the sequence is non-stationary, then... Difference operations are used to eliminate non-stationarity. The formula for difference operations is: ; in, For the shift operator, It is the difference order. Original sales volume, subscript This is to clearly identify that we are processing a specific data sample at a particular point in time; Typically, repeated testing is performed until the differential sequence is obtained. This step, up to passing the stationarity test, aims to ensure that the sequence meets the basic premises of ARIMA modeling and that patterns can be stably extracted. Model identification and order determination: For the stationary series, the autoregressive order is preliminarily identified by analyzing its autocorrelation plot and partial autocorrelation plot. and moving average order The system uses grid search combined with AIC information criteria to automatically optimize and determine the optimal solution. The formula for calculating the AIC criterion based on parameter combinations is as follows: ; in, This represents the total number of model parameters. The model combination with the smallest AIC value, which is the maximum likelihood function value of the model, is selected as the optimal model structure. Parameter estimation and model fitting: based on deterministic The order of the model is determined using the maximum likelihood estimation method. The coefficients of the ARIMA model are then estimated, and the fitted model can be expressed as: ; in, For constant terms, It is a white noise sequence with a mean of 0 and a constant variance. These are the autoregressive coefficients. The moving average coefficient is... and For indexing, For the shift operator, Let be the difference order. Original sales volume, subscript This is to clearly identify that we are processing a specific data sample at a particular point in time. Let the order be the autoregressive order. The moving average order; This formula integrates historical values, the accumulation of historical shocks, and historical errors, and can systematically depict the dynamic patterns of the sequence. Periodicity identification and output: By analyzing the residual sequence of the fitted model, the seasonal components decomposed by the model, and the predicted spectral density of the model, the unit can quantitatively identify the main periodicity in the sequence; The machine learning model unit integrates random forest and neural network algorithms, and calculates demand prediction values ​​by fusing internal and external data weights. The specific implementation includes three parallel sub-processes and one fusion step: Random Forest Regression Sub-model: This model consists of multiple decision trees for the input feature vector. Each tree The system is independently partitioned based on feature thresholds, eventually reaching a leaf node and outputting a predicted value. The final prediction of a random forest is the average of the outputs of all decision trees, expressed by the formula: ; in, For the total number of decision trees, For feature vectors, For predicted values, For indexing, This is the average value of the output; Neural network regression sub-model: Employs a multilayer perceptron structure, with the input layer receiving the same feature vector. After undergoing a nonlinear transformation through at least one hidden layer, the predicted value is finally generated in the output layer. Taking a single hidden layer as an example, its forward propagation process is as follows: ; in, and These are the weight matrix and bias vector of the hidden layer, respectively. It is the ReLU activation function. and These are the weight vector and bias vector of the output layer. This is an intermediate prediction value. For feature vectors, It is a transpose operator; Dynamic weight fusion and final prediction calculation: The unit does not simply average the prediction results of the two sub-models, but dynamically assigns fusion weights based on their performance on the recent validation set. The system maintains a model performance window, recording the average absolute percentage error of each model over past prediction periods. and fusion weight and The formula is obtained by normalizing the inverse of the error, as shown below: ; in, It is a very small constant; Final demand forecast Calculated by the following formula: ; in, This is the demand forecast. and To integrate weights, The average value of the output. This is an intermediate predicted value; This final prediction, along with its confidence assessment, will be output to the downstream scheduling optimization module. This fusion mechanism ensures that the system can adaptively rely on the algorithm that performs better under the current data model, thereby improving the robustness and accuracy of the prediction. The confidence calculation unit generates confidence intervals based on historical prediction error data and outputs a time-segmented sales forecast table, including dish name, next day's lunch sales, next day's dinner sales, total predicted sales, and confidence level. The confidence threshold is set to 85% or higher to be considered a reliable prediction.

[0026] In the scheduling optimization module, the rule base configuration unit stores employee skill levels, working hour constraints, and legal and regulatory requirements. The optimization algorithm unit uses the particle swarm optimization algorithm, with the objective function of minimizing labor costs and maximizing service efficiency. The specific implementation consists of three key steps: Problem Encoding and Particle Initialization: A feasible scheduling scheme is encoded as a multidimensional vector, i.e., the position of a "particle". , where each dimension Represents a decision variable. The total number of decision variables, For indexing, the system randomly initializes a particle swarm, with the position of each particle... and speed All values ​​are randomly generated within the range allowed by the rule base, thus forming the initial set of candidate scheduling schemes; Multi-objective fitness function construction and evaluation: The algorithm constructs and evaluates a comprehensive fitness function. To quantify the merits of each scheduling plan, this function merges the two objectives of "minimizing labor costs" and "maximizing service efficiency" into a scalar value that can be minimized. Its core formula is as follows: ; in: According to the plan Calculate the total labor cost. According to the plan Estimated overall service efficiency and It is a positive weighting coefficient; Particle swarm optimization and feasibility verification: In each iteration, the algorithm follows the standard particle swarm update formula to adjust the velocity and position of each particle. ; ; in, It is a particle Its own historical best position It is the globally optimal position found so far for the entire particle swarm. For algorithm parameters, It is a random number. For indexing, For the number of iterations, The position of the particle. The velocity of the particle; The key step is: each position After an update, all updates must be immediately submitted to the feasibility verification unit for verification. This unit will check whether the new position violates all hard constraints. If a violation occurs, the particle's position will be adjusted back to the previous feasible position, thereby guiding the population to search within the feasible domain. The algorithm repeatedly performs evaluation, update, and verification steps until it reaches the maximum number of iterations, ultimately finding the globally optimal position. The scheduling scheme it represents is a solution that optimizes the balance between cost and efficiency under given constraints, and serves as a service adjustment module for optimizing scheduling data output to downstream applications. The feasibility verification unit compares the simulated scheduling plan with actual operational data, detects conflict points, and outputs adjustment suggestions to ensure that the scheduling plan meets dynamic business needs.

[0027] In the service adjustment module, the process simulation unit constructs a discrete event simulation model to simulate customer flow and service nodes, while the bottleneck detection unit uses critical path analysis to identify service delay points. The specific implementation includes two sequential steps: The process simulation unit constructs a service process model based on discrete event simulation: This unit abstracts the entire service location as a network consisting of multiple service nodes and queues. Each customer is regarded as an "entity" and its flow in the system is driven by discrete events. The core of the simulation is to simulate the entire process of a customer arriving, receiving services at each node, and leaving. Model building: Define system state variables, event types, and the event scheduling mechanism that drives the simulation clock forward; Key parameters and formulas: The customer arrival process is typically modeled as a Poisson process, meaning the arrival time is calculated per unit of time. The probability of a customer follows a Poisson distribution: ; in, For average customer arrival rate, For time, Let be a probability function. The total number of times a specific event occurs. For the specific number of customers, It is a natural constant; The time a customer spends receiving service at each service node is typically assumed to follow an exponential distribution, with the following probability density function: ; in, This represents the average service rate of the node. For time, Let be the probability density function. It is a natural constant; The simulation engine generates event sequences based on these random distributions, dynamically updates the system status, and records key performance data such as the total stay time of each customer and the waiting time at each node. The bottleneck detection unit uses critical path analysis to identify delay points: This unit analyzes the detailed process logs output by the simulation unit, treating a complete customer service process as a "project", in which each service node and waiting step is considered an "activity"; Activity network construction: Based on the logical sequence of the service process, construct a network diagram of the pre- and post-event relationships of activities; Critical path calculation: For each activity Calculate its earliest start time Earliest end time Latest start time and latest end time Time difference of activities The calculation formula is:

[0028] jet lag The path formed by connecting zero and near-zero activities is the critical path. The total duration of this path determines the shortest completion time of the service process. The identified bottlenecks are the nodes on the critical path with the longest service times and the longest waiting queues. Any small delay at these nodes will directly and equally extend the overall service time. Finally, the bottleneck detection unit outputs the identified bottleneck nodes and their quantitative data to the dynamic adjustment unit, which serves as the core basis for generating service optimization instructions. The dynamic adjustment unit generates resource reallocation instructions based on real-time passenger flow data, including increasing the number of employees during peak hours and adjusting the food preparation time. The optimization instructions are sent to the terminal devices through the API interface.

[0029] In the performance feedback module, the comparative analysis unit calculates the deviation rate between predicted and actual sales. For each matched pair of predicted and actual sales data, the unit uses the following formula to calculate its deviation rate: ; in, The deviation rate, To predict sales, This refers to actual sales volume; The adaptive learning unit dynamically updates the machine learning model parameters based on bias data, and optimizes weight allocation using gradient descent. The specific implementation involves three key steps: Loss function construction and gradient calculation: First, the unit constructs a differentiable loss function from the received bias data. Used to quantify the current model parameters The overall prediction inaccuracy is such that the most commonly used loss function is mean squared error, for a given set of data. The formula for calculating the batch size of a sample is as follows:

[0030] in, For loss function, This represents the batch size of the sample. For predicted values, This is the actual value. These are the current model parameters. For indexing; Subsequently, the system calculates the loss function using the backpropagation algorithm. Relative to each trainable parameter of the model The partial derivative, i.e., the gradient. ; Gradient Descent Parameter Update: After obtaining the gradient, the unit uses gradient descent to update the model parameters. The core update formula is as follows: ; in, and These represent the model parameter vectors before and after the update, respectively. It's the learning rate. It is the gradient of the loss function; Iterative optimization and convergence: The above steps are repeatedly executed in a loop. Each iteration uses the latest batch of bias data. Through continuous iteration, the model parameters are gradually adjusted in the opposite direction of the loss function gradient, so that the loss function... The value of is constantly decreasing, which means that the model's predicted output is... Getting closer to the actual value When the loss value drops to a stable threshold and the preset number of iterations is reached, the optimization process stops, the updated model parameters are fixed and deployed for the next round of prediction tasks, thus forming a closed loop of "prediction-evaluation-optimization". The report generation unit integrates multi-dimensional indicators, including sales accuracy, employee utilization, and service satisfaction, and outputs daily and weekly reports through a visual dashboard to support decision-makers in adjusting strategies.

[0031] The system also includes a real-time monitoring module, which receives output data from the performance feedback module, detects emergencies through the abnormal alarm unit, and generates temporary scheduling adjustment instructions using the emergency response unit. The real-time monitoring module works in conjunction with the data integration module. When external data shows sudden weather changes or urban events, the predictive analysis module is automatically triggered to recalculate demand forecasts.

[0032] The system also includes a user interaction module that receives user input via a web interface, including manually adjusting scheduling parameters and providing feedback on service issues; The user interaction module integrates a natural language processing unit to parse user queries and provides optimization suggestions through a recommendation engine unit, ensuring the system's ease of use and adaptability.

[0033] The operation steps of a big data-driven intelligent scheduling and service optimization system are as follows: Step 1: Acquisition and integration of multi-source heterogeneous data: The first step of the system is to collect a wide range of data that affect business needs. This includes historical operational data obtained through the internal data acquisition unit and environmental data received in real time through the external data interface unit. These data are heterogeneous in terms of frequency, format and source. Subsequently, the data frequency synchronization unit will be activated to identify the time axis differences between internal and external data using a bidirectional search algorithm, and to align the low-frequency data to the timestamp of the high-frequency data using a linear interpolation algorithm, generating a set of integrated data with fully synchronized timestamps, providing a consistent data foundation for subsequent analysis.

[0034] Step 2: Data cleaning, alignment, and standardization: After obtaining the integrated data, the system enters the data preprocessing stage. The preprocessing module performs deep cleaning on the raw data. The data cleaning unit uses the interquartile range statistical method to automatically identify and remove outliers. Next, the missing value handling unit uses linear interpolation to intelligently fill in missing data based on the valid data points before and after, ensuring the continuity of the data sequence. Finally, the data normalization unit uses the min-max scaling method to uniformly compress feature data with different dimensions and numerical ranges into the [0,1] interval, generating clean and regular data. This step eliminates data noise and the influence of dimensions, which is the key to ensuring the accuracy and stability of the subsequent machine learning model.

[0035] Step 3: Intelligent demand forecasting based on machine learning: This step is the "brain" of the system. The predictive analysis module uses clean data for in-depth mining. First, the time series analysis unit applies an autoregressive integral moving average model to decompose trend, periodic, and random components from historical sales data, accurately identifying weekly and monthly sales patterns. Then, the machine learning model unit integrates random forest and neural network algorithms: random forest is responsible for capturing the non-linear relationship between features and sales and evaluating feature importance, while neural networks deeply mine complex patterns. The system dynamically merges the prediction results of the two models, and finally, the confidence calculation unit outputs time-segmented sales prediction results with confidence intervals, providing a scientific and quantitative basis for scheduling needs.

[0036] Step 4: Generating an optimized scheduling plan under multiple constraints: Based on accurate demand forecasting, the system enters the solution formulation stage. The scheduling optimization module first loads various hard constraints from the rule base configuration unit, including employee skills, maximum working hours, and legal requirements. Subsequently, the optimization algorithm unit starts working, encoding the scheduling scheme into "particles" and using "minimizing labor costs" and "maximizing service efficiency" as objective functions. It performs a global search within the solution space that satisfies all constraints. Each generated scheme is simulated and verified by the feasibility verification unit to detect and correct potential conflict points. Finally, it outputs optimized scheduling data that is both compliant and can efficiently respond to forecasted demand.

[0037] Step 5: Service Process Simulation and Dynamic Adjustment Before the scheduling plan is implemented, the system will conduct a "sand table simulation". The process simulation unit of the service adjustment module will build a discrete event simulation model to simulate the entire process of customers from arrival, ordering food to receiving services, thereby predictively assessing the performance of the service link. The bottleneck detection unit will use critical path analysis to accurately locate the key links that cause service delays. Based on the simulation and bottleneck analysis results, the dynamic adjustment unit will generate specific service optimization instructions. These instructions will be sent to terminal devices through API interfaces to achieve accurate and dynamic allocation of resources.

[0038] Step 6: Performance Evaluation, Feedback, and Model Self-Learning This is a closed-loop process for achieving continuous system evolution. After the plan is implemented, the performance feedback module starts working, the comparative analysis unit calculates the deviation rate between predicted sales and actual sales to quantitatively evaluate the scheduling effect, and more importantly, the adaptive learning unit uses the gradient descent algorithm to automatically adjust the parameters of the machine learning model in the third step based on these deviation data, optimizing its weight allocation. This means that the system can learn from each prediction error, making the next prediction more accurate and the scheduling more reasonable. Finally, the report generation unit summarizes the key indicators into a visual report to support management decisions. At the same time, the real-time monitoring module continuously tracks changes in the internal and external environment. Once an anomaly is detected, the system is immediately triggered to re-execute the prediction and scheduling process to ensure that the system always responds agilely to changes in the real world.

[0039] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0040] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A big data-driven intelligent scheduling and service optimization system, characterized in that, The system includes: The data integration module uses an internal data acquisition unit to acquire historical operational data, receives environmental data through an external data interface unit, and outputs integrated data through a data frequency synchronization unit. The preprocessing module receives the integrated data, removes noise through the data cleaning unit, standardizes the data format using the data normalization unit, and generates clean data through the missing value processing unit. The predictive analysis module receives the cleaning data, identifies trends through the time series analysis unit, predicts demand using the machine learning model unit, and outputs the prediction results through the confidence calculation unit. The scheduling optimization module receives the prediction results, sets scheduling constraints through the rule base configuration unit, generates a scheduling plan using the optimization algorithm unit, and outputs optimized scheduling data through the feasibility verification unit. The service adjustment module receives the optimized scheduling data, simulates the service process through the process simulation unit, identifies inefficiency points through the bottleneck detection unit, and generates service optimization instructions through the dynamic adjustment unit. The performance feedback module receives the service optimization instructions and actual operational data, evaluates the scheduling effect through the comparative analysis unit, updates the model parameters using the adaptive learning unit, and outputs optimization suggestions through the report generation unit.

2. The big data-driven intelligent scheduling and service optimization system according to claim 1, characterized in that, The data integration module includes an internal data acquisition unit comprising a historical sales acquisition subunit, an inventory status acquisition subunit, and a promotional activity record subunit. The historical sales data collection subunit acquires data from the same period of the past week, data from the same week of the past month, and data from the same holiday, with a collection frequency of daily updates and holiday updates. The inventory status collection subunit collects the remaining quantity of each dish after the store closes on the same day, and the collection frequency is updated at 22:00 every day; The promotional activity record sub-unit records the promotional dishes and the activity period, and the data is collected 3 days before the activity.

3. The big data-driven intelligent scheduling and service optimization system according to claim 1, characterized in that, The external data interface unit includes a weather data acquisition subunit, a surrounding passenger flow monitoring subunit, and a city event subscription subunit; The weather data acquisition subunit collects the probability of precipitation and temperature data for the following day, with the data being updated daily at 16:

30. The weight of the data is adjusted to 20%-25% when the probability of precipitation is greater than 90%. The surrounding passenger flow monitoring subunit monitors the daily off-get off work passenger flow data of office buildings and residential areas, with a collection frequency of 17:00 updated daily, and increases the weight of evening business light meals sales when the overtime rate is greater than 80%. The city event subscription sub-unit acquires data on exhibitions, concerts, and traffic control events, with a collection frequency of one day prior to the event, and dynamically adjusts the sales weight based on the event scale.

4. The big data-driven intelligent scheduling and service optimization system according to claim 1, characterized in that, In the preprocessing module, the data cleaning unit uses an outlier detection algorithm to remove invalid data points, the data normalization unit uses a minimum-maximum scaling method to unify the data to the [0,1] interval, and the missing value processing unit fills in missing data using linear interpolation. The data frequency synchronization unit identifies the differences in internal and external data acquisition frequencies based on a bidirectional search algorithm. When the frequencies are inconsistent, a linear interpolation algorithm is used to adjust the low-frequency data to the high-frequency data standard.

5. The big data-driven intelligent scheduling and service optimization system according to claim 1, characterized in that, In the predictive analysis module, the time series analysis unit identifies the periodic pattern of sales through an autoregressive integral moving average model, and the machine learning model unit integrates random forest and neural network algorithms, and calculates the demand prediction value by merging internal and external data weights. The confidence calculation unit generates confidence intervals based on historical prediction error data and outputs a time-segmented sales forecast table, including dish name, sales at noon the next day, sales at night the next day, total predicted sales, and confidence level. The confidence threshold is set to 85% or higher to be considered a reliable prediction.

6. The big data-driven intelligent scheduling and service optimization system according to claim 1, characterized in that, In the scheduling optimization module, the rule base configuration unit stores employee skill levels, working hour constraints, and legal and regulatory requirements, and the optimization algorithm unit adopts the particle swarm optimization algorithm with the objective function of minimizing labor costs and maximizing service efficiency. The feasibility verification unit compares the simulated scheduling plan with actual operational data, detects conflict points, and outputs adjustment suggestions.

7. The big data-driven intelligent scheduling and service optimization system according to claim 1, characterized in that, In the service adjustment module, the process simulation unit constructs a discrete event simulation model to simulate customer flow and service nodes, and the bottleneck detection unit uses critical path analysis to identify service delay links. The dynamic adjustment unit generates resource reallocation instructions based on real-time passenger flow data, and the optimization instructions are sent to the terminal device through the API interface.

8. The big data-driven intelligent scheduling and service optimization system according to claim 1, characterized in that, In the performance feedback module, the comparative analysis unit calculates the deviation rate between predicted sales and actual sales, and the adaptive learning unit dynamically updates the machine learning model parameters based on the deviation data and optimizes the weight allocation using the gradient descent method. The report generation unit integrates multi-dimensional indicators, including sales accuracy, employee utilization, and service satisfaction, and outputs daily and weekly reports through a visual dashboard.

9. The big data-driven intelligent scheduling and service optimization system according to claim 1, characterized in that, The system also includes a real-time monitoring module, which receives the output data of the performance feedback module, detects emergencies through the abnormal alarm unit, and generates temporary scheduling adjustment instructions using the emergency response unit. The real-time monitoring module works in conjunction with the data integration module. When external data shows sudden weather changes or urban events, the predictive analysis module is automatically triggered to recalculate demand forecasts.

10. The big data-driven intelligent scheduling and service optimization system according to claim 1, characterized in that, The system also includes a user interaction module that receives user input via a web interface, including manually adjusting scheduling parameters and providing feedback on service issues. The user interaction module integrates a natural language processing unit to parse user queries and provides optimization suggestions through a recommendation engine unit.

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