Business management method based on one-stop inspection platform

Abstract

This application relates to the field of business management and discloses a business management method based on a one-stop inspection platform: by collecting order demand and external environment data, establishing a dynamic system model of order demand, performing Kalman filter prediction, and constructing an optimization model based on optimal control theory and variational methods, ultimately achieving optimization of resource scheduling strategies, handling demand fluctuations and uncertainties, and dynamically adjusting resource allocation using a multi-level feedback mechanism; the present invention also provides a business management system based on the one-stop inspection platform: a data acquisition module, a nonlinear dynamic model module, a demand forecasting module, a resource scheduling optimization module, a stochastic control module, and a feedback control module. The present invention combines a nonlinear dynamic system model with Kalman filtering technology to achieve accurate order demand prediction and dynamic adjustment; optimizes resource scheduling based on optimal control theory, introduces a stochastic control model to deal with uncertainty, and uses a multi-level feedback mechanism to ensure rapid response and adjustment.

Description

Technical Field

[0001] The present invention relates to the field of business management, and in particular to a business management method based on a one-stop inspection platform. Background Art

[0002] In the existing resource scheduling and demand forecasting fields, many platforms use static, rule-based scheduling methods. This approach typically relies on historical data and forecasting models, but is often insensitive to rapidly changing market demand. For example, when demand fluctuates significantly, the system is unable to adjust resource allocation in real time, resulting in inefficient scheduling. Consequently, even the initial scheduling plan may become ineffective by the time it is implemented and unable to adapt to market changes.

[0003] In addition, existing technologies generally adopt a centralized processing approach, where all decisions are executed by a single system. This approach is not only prone to bottlenecks, but also often cannot respond quickly to complex situations due to the lack of a flexible feedback mechanism. In fact, centralized processing often ignores the impact of certain details in the system, especially in multi-level and multi-dimensional scheduling, where the lag in information flow will exacerbate resource allocation errors.

[0004] Finally, the existing system's approach to uncertainty also has certain limitations. Although some traditional algorithms have introduced probabilistic models to deal with the randomness of demand, most of them assume that market changes are stable and do not take into account uncertainties in complex and dynamic environments. Scheduling models constructed in this way are often unable to adapt to rapidly changing environments and are difficult to meet optimization goals in actual operations, resulting in a significant reduction in the system's scheduling effect. Therefore, the present invention proposes a business management method based on a one-stop inspection platform to address the shortcomings of the existing technology. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a business management method based on a one-stop inspection platform. This method addresses the existing problems of slow resource scheduling, bottlenecks caused by centralized decision-making, and insufficient handling of dynamic uncertainty. By introducing a more flexible distributed decision-making mechanism and dynamic adjustment capabilities, this invention can respond to market demand fluctuations in real time, optimize resource allocation, and improve the efficiency and accuracy of business management.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A business management method based on a one-stop inspection platform includes the following steps:

[0007] Collecting platform order demand data and external environment data, the order demand data includes order quantity, order type, and product specification information, and the external environment data includes market fluctuations and seasonal changes;

[0008] Establishing a dynamic system model of order demand based on a nonlinear dynamic system model, wherein the nonlinear dynamic system model is used to describe the change of order demand over time and the influence of external disturbance factors;

[0009] Performing Kalman filtering on the order demand data and updating the predicted value of the order demand in real time through recursive calculation;

[0010] Based on the predicted order demand, an optimization model for resource scheduling is constructed using optimal control theory, wherein the optimization model includes an objective function, and the objective function is used to minimize the total cost of resource scheduling and meet business constraints;

[0011] The optimal control model is solved using a calculus of variations to obtain an optimal resource scheduling strategy, and resources are allocated based on the strategy. Order demand fluctuations and uncertainties in resource scheduling are handled using a stochastic control model, which is optimized based on an objective function that minimizes expectations.

[0012] Build a multi-level feedback control mechanism to dynamically adjust resource scheduling strategies based on real-time data and demand forecast results.

[0013] Preferably, the state equation of the nonlinear dynamic system model is:

[0014]

[0015] Among them, x(t) represents the state variable of the system, which is usually the quantity that needs to be tracked in the system; u(t) represents the external input variable, which is usually the external factor that affects the system; w(t) represents the system noise, which reflects the randomness and uncertainty that the model cannot capture; f(x(t),u(t),t) is the dynamic function of the system, which represents the law of evolution of the system state over time, which depends on the current state x(t), the control input u(t) and the time t.

[0016] Preferably, the step of Kalman filtering processing includes:

[0017] Prediction step: predicting order demand through historical data and system models;

[0018] In the update step, the predicted value is corrected according to the real-time observation data, the Kalman gain is updated and the state estimation is optimized.

[0019] Preferably, the objective function in the optimal control theory is:

[0020]

[0021] Where J(u) is the objective function of the resource scheduling optimization problem, which represents the total integral value of the objective function in the time interval 0≤t≤T; It is a cost function related to resource scheduling, which depends on the system state x(t), the control input u(t) and the time t. This term is usually used to represent the cost of resource scheduling; I(u(t)) is the information entropy of the resource scheduling policy, which is used to measure the uncertainty of resource allocation; λ is the weight coefficient of the information entropy term, which controls the balance between cost and uncertainty in the resource scheduling optimization objective; T represents the time period of the optimization problem, and the integration interval of the objective function is from t = 0 to t = T.

[0022] Preferably, the variational method solving step includes:

[0023] The Lagrangian function is constructed by the Lagrangian multiplier method to transform the resource scheduling problem into an optimization problem;

[0024] Utilize optimization methods to solve the optimal resource scheduling strategy to meet business constraints and minimize total cost.

[0025] Preferably, the objective function in the stochastic control model is:

[0026]

[0027] in, Represents solving the optimal control strategy u, the goal is to minimize the subsequent expected value; represents the expectation operation, which is used to calculate the average value under the influence of random disturbances; J(u) represents the objective function of the resource scheduling optimization problem; It is the cost function of resource scheduling, which depends on the system state x(t), control input u(t) and time t, and represents the scheduling cost at a certain moment; x(t) is the state vector of the system, which usually describes the order demand and resource usage content; u(t) is the scheduling policy, which represents the allocation decision of system resources at time t; T is the time period, which represents the scheduling optimization process from time 0 to time T.

[0028] Preferably, the multi-level feedback control mechanism includes:

[0029] Feedback real-time data into the control system and adjust resource scheduling strategies based on the feedback information;

[0030] Dynamically adjust scheduling strategies based on real-time order demand changes and resource status;

[0031] The real-time data feedback system performs data stream processing through a big data platform, and utilizes Apache Kafka and Apache Flink technologies to collect, transmit and process order data and market environment data.

[0032] Preferably, the real-time data feedback system performs data stream processing through a big data platform, and utilizes Apache Kafka and Apache Flink technologies to collect, transmit and process order data and market environment data.

[0033] The present invention provides a business management system based on a one-stop inspection platform, comprising:

[0034] Data collection module, used to collect platform order demand data and external environment data;

[0035] Nonlinear dynamics model module, used to establish a dynamic system model of order demand;

[0036] Demand forecasting module, used to perform Kalman filtering on order demand and generate demand forecast values;

[0037] Resource scheduling optimization module, used to build resource scheduling optimization model and perform optimization based on demand forecast results;

[0038] Stochastic control module, used to handle uncertainty in order demand and resource scheduling;

[0039] The feedback control module is used to dynamically adjust resource scheduling strategies based on real-time data and demand forecast results.

[0040] Preferably, the resource scheduling optimization module includes:

[0041] Optimal control model solving unit, which optimizes resource scheduling strategies based on the calculus of variations and optimal control theory;

[0042] Multi-level feedback units dynamically adjust scheduling strategies based on real-time demand data.

[0043] The present invention provides a business management method based on a one-stop inspection platform. It has the following beneficial effects:

[0044] 1. The present invention adopts a solution that combines a nonlinear dynamic system model with Kalman filtering technology, which can accurately predict order demand and dynamically adjust it through real-time feedback; it achieves the technical effect of quickly adapting to demand changes in an uncertain market environment; compared with the single static prediction model in the existing technology, the present invention can update the prediction results in real time, ensuring that the platform resource scheduling is more flexible and efficient, and avoiding resource waste and order delays caused by demand prediction errors.

[0045] 2. The present invention constructs a resource scheduling optimization model based on optimal control theory and combines it with the variational method to solve it, ensuring that the resource scheduling cost is minimized while meeting business constraints. Compared with traditional resource scheduling methods, existing technologies often ignore the balance between constraints and costs and cannot fully optimize resource allocation. By optimizing the objective function and introducing information entropy, the present invention can perform efficient scheduling in a more complex scheduling environment and maximize resource utilization.

[0046] 3. The present invention addresses the challenges brought about by order demand fluctuations and external environmental uncertainties by introducing a stochastic control model; by optimizing the objective function of expected minimization, it achieves the technical effect of stable operation in the face of market fluctuations, emergencies and external disturbances; compared with the existing solutions that fail to effectively deal with uncertain factors, the present invention, through the introduction of stochastic control, can optimize resource allocation under uncertain conditions, thereby improving the system's response capability and stability.

[0047] 4. The present invention adopts a multi-level feedback control mechanism to monitor order demand and resource scheduling status in real time and dynamically adjust the scheduling strategy; it achieves the technical effect of rapid response and adjustment in the case of large demand fluctuations; the existing technology often has the problem of insufficient feedback control, resulting in the inability to respond flexibly in a rapidly changing market environment; the multi-level feedback mechanism of the present invention ensures that resource scheduling can be continuously optimized and adjusted, effectively improving the system's response speed and the real-time nature of resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flow chart of the method of the present invention;

[0049] Figure 2 This is a system architecture diagram of the present invention;

[0050] Figure 3 This is the architecture diagram of the resource scheduling optimization module of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] See also Figure 1 , an embodiment of the present invention provides a service management method based on a one-stop inspection platform, comprising the following steps:

[0053] S1. Collecting platform order demand data and external environment data, where the order demand data includes order quantity, order type, and product specification information, and the external environment data includes market fluctuations and seasonal changes;

[0054] In the implementation of the present invention, step S1 mainly involves the data collection work of the platform, including the collection of order demand data and external environment data; through this step, the system can obtain various input information related to changes in order demand and provide necessary basic data for subsequent modeling and prediction; the collection of order demand data includes order quantity, order type, product specifications and other contents, which help to accurately depict the basic situation of order demand; the collection of external environment data focuses on factors such as market fluctuations and seasonal changes, which may affect the volatility of order demand.

[0055] In this embodiment, the data collection module first collects the order demand data of the platform; the order demand data mainly comes from the stages of customer order placement, order execution and product shipment; specifically, the order demand data includes:

[0056] Order quantity: the total number of orders within a specified period of time, which is an important data for measuring the platform's business volume and operating status;

[0057] Order type: The specific classification of orders, which may be based on different service types, product types, or customer types. By understanding order types, the platform can accurately analyze the demand fluctuation patterns of different types of orders;

[0058] Product specification information: Different products sold on the platform have different specifications. Order demand data includes information such as the type, specification, and quantity of products ordered by customers. By analyzing this data, the platform can identify which products have high demand and which products have large demand fluctuations.

[0059] In addition, the collection of external environmental data is also an important part of step S1. External environmental data includes market fluctuations and seasonal changes, which have a significant impact on the platform's order demand. External environmental data is usually obtained from third-party data sources or partner systems. For example:

[0060] Market fluctuations: External economic factors such as market price fluctuations, policy changes, and industry dynamics can affect the platform's order demand. For example, when market demand is strong, customer orders may increase sharply, while when it is low, they may decrease.

[0061] Seasonal fluctuations: Different seasons or holidays may cause cyclical fluctuations in order demand; for example, the demand for certain products may increase significantly during holidays, while the demand may decrease during the off-season.

[0062] In one possible implementation, the platform obtains order demand data and external environment data by integrating multiple data sources; these data can be collected through internal systems (such as order management systems, inventory management systems) and external data interfaces (such as data provided by market research agencies, economic data, etc.).

[0063] Specifically, the processing flow of order demand data and external environment data is as follows:

[0064] Data collection: The system first obtains order data from the platform database through the API interface and records the detailed information of each order, including order quantity, type, specifications, etc.; external environment data is obtained in real time by obtaining economic data, market indexes and other content to ensure the timeliness and accuracy of the data.

[0065] Data integration and preprocessing: After collecting relevant data, the system will organize, deduplicate, and preprocess the data to ensure data quality. For example, there may be some abnormal orders or invalid data in the order data. The system will automatically filter out these data and retain valid data for subsequent analysis.

[0066] In some embodiments, to ensure the accuracy of the data, the platform may adopt a data verification mechanism; for example, by comparing actual order execution and customer feedback, the platform can calibrate and correct errors in order data to ensure that the order demand data obtained by the platform reflects the actual business situation; in addition, the platform can also continuously monitor changes in the external environment and promptly adjust the impact of seasonal changes or market fluctuations on order demand forecasts.

[0067] During data collection and preprocessing, the system will use the following mathematical model to help analyze and integrate data: D = {D1, D2, ..., D n};

[0068] Among them, D1 to D n Represents order requirements and external environment data collected from different data sources, D is the total set of data sets; each D i Contains detailed information about the order and influencing factors related to the external environment.

[0069] Furthermore, during the implementation process, the quantitative processing of external environmental data is very critical; for example, market fluctuations can be represented by market indices, and seasonal changes can be quantitatively analyzed through certain specific economic models; these quantified external environmental data can be converted into numerical values ​​and directly applied to subsequent demand forecasting models.

[0070] For example, market volatility might be modeled as follows:

[0071] M(t)=α·M0+β·P(t);

[0072] Among them, M(t) represents the market volatility index at time t, M0 is the basic market volatility index, P(t) represents an economic indicator (such as the consumer confidence index), and α and β are weight coefficients.

[0073] Alternatively, external environmental data can be further processed and predicted using machine learning models, especially when dealing with a large number of nonlinear relationships; by learning from historical data, the system can gradually improve the accuracy of its predictions of future market fluctuations and seasonal changes.

[0074] In one possible implementation, the platform can also combine historical sales data and external environmental data to build an order demand forecasting model through regression analysis or neural network models; the output of the forecasting model will serve as input for subsequent steps (such as Kalman filtering or optimal control) to help the system make more accurate resource scheduling decisions.

[0075] S2. Establishing a dynamic system model of order demand based on a nonlinear dynamic system model, wherein the nonlinear dynamic system model is used to describe changes in order demand over time and the influence of external disturbance factors;

[0076] In the aforementioned steps, the platform has collected relevant order demand data and external environmental data, providing data support for subsequent forecasts and decision-making. Based on this collected data, the next step is to establish a nonlinear dynamic system model to describe the changes in order demand over time and consider the impact of external disturbance factors on the system. The nonlinear dynamic model will provide a mathematical description of system behavior for subsequent demand forecasting, resource scheduling optimization, and other links.

[0077] In this embodiment, by introducing a nonlinear dynamic system model, we abstract the evolution process of order demand into a dynamic system, aiming to accurately describe the dynamic characteristics of demand changes over time and how external disturbances affect the process; in order to adapt to complex business environments, the model uses nonlinear equations to construct the system state equations to more accurately capture the complexity and uncertainty of the system.

[0078] Specifically, the basic state equation of the nonlinear dynamics model is as follows:

[0079]

[0080] Among them, x(t) is the state vector of order demand, which represents the order demand of the platform at time t; u(t) is the external input variable, which usually represents the external factors affecting system demand, such as market fluctuations, seasonal changes, promotional activities, etc.; w(t) is the system noise, which reflects the demand fluctuations caused by external factors or uncontrollable factors, and often exists in the form of a random process; and f(x(t),u(t),t) is a nonlinear function that describes the dynamic changes of the system, which represents the changing pattern of order demand.

[0081] In general, the dynamic evolution of the system is not only affected by the current order demand and external control input, but may also be affected by some random disturbance factors, which can be represented by w(t); the form of the nonlinear function f(x(t), u(t), t) in the model can be selected according to different application scenarios. It usually includes the nonlinear relationship between order demand and time, the influence of market factors, etc.

[0082] Alternatively, the dynamic system model may include some known nonlinear function forms, such as exponential function, logarithmic function, Sigmoid function, etc.; these functions can effectively describe the nonlinear characteristics of demand growth or decrease; for example, some products may show exponential growth during the promotion period, and linear decline when the market is sluggish.

[0083] Specifically, the model of order demand changing over time may adopt the following nonlinear relationship:

[0084] f(x(t),u(t),t)=Ax(t)+Bu(t)+Cx(t) 2 +Du(t) 2 ;

[0085] Among them, f(x(t),u(t),t) represents the dynamic change function of order demand, which describes the evolution process of order demand and depends on the system state x(t), external input u(t) and time t; x(t) is the state variable of the system, which in this case represents the vector of order demand; u(t) is the external input variable, which usually represents external factors such as market fluctuations and seasonal changes; A, B, C, and D are the parameters of the model, which control the relationship between the system state and external input; A represents the linear influence coefficient of the system state variable on the change of order demand; B represents the linear influence coefficient of the external input on order demand; C represents the nonlinear influence coefficient of the system state variable, which reflects the nonlinear growth or decline characteristics of demand; D represents the nonlinear influence coefficient of the external input, which reflects the nonlinear influence of external disturbances on order demand.

[0086] In one possible implementation, the system may estimate model parameters based on historical data and experience; for example, statistical methods such as least squares and maximum likelihood can be used to fit the data to obtain the optimal model parameters; through regression analysis of historical order data and external environmental data, the system can learn how order demand changes over time and with external factors, and adjust the system's prediction model accordingly.

[0087] In some embodiments, the nonlinear dynamic model can also be used in combination with other machine learning algorithms to further improve the accuracy of demand forecasting; for example, the system can use a neural network model to capture complex patterns in demand changes, or use a support vector machine (SVM) to handle the nonlinear characteristics in demand forecasting; these algorithms can automatically learn the laws of demand changes from data, thereby improving the system's predictive capabilities.

[0088] Specifically, in order to improve the accuracy of the model, the establishment of the dynamic system model can be further optimized through the following steps:

[0089] Collect more historical order data to ensure the representativeness and diversity of the data;

[0090] Establish multiple demand forecasting models according to changes in different dimensions such as market and product;

[0091] In actual applications, the model is constantly adjusted and optimized to ensure that it can adapt to changes in demand in different market environments.

[0092] As an option, when dealing with external disturbance factors, the platform can also use data fusion technology to fuse external environmental data from different data sources; for example, market fluctuations can be quantified through data such as economic indices and consumer confidence indexes, while seasonal changes can be predicted by analyzing sales data over the years; through this data fusion method, the platform can more comprehensively understand the impact of external environmental changes on order demand.

[0093] S3. Performing Kalman filtering on the order demand data and updating the predicted value of the order demand in real time through recursive calculation;

[0094] In step S2, a nonlinear dynamic system model has been established to describe the changing pattern of order demand and take into account the impact of external factors on demand. On this basis, in order to further improve the accuracy of order demand prediction, the system uses Kalman filtering technology to predict and update order demand data in real time. Kalman filtering recursively adjusts the predicted value of future order demand based on historical data and current real-time observations.

[0095] In this embodiment, the Kalman filter processing of order demand data mainly includes two core steps: a prediction step and an update step. Kalman filtering is a recursive algorithm whose advantage is that it can process uncertainty and noise and maintain a high prediction accuracy in real-time data.

[0096] Specifically, the working principle of Kalman filtering is to perform recursive calculations based on existing historical data and current observation data, combined with the system model, to update the predicted value, thereby making the system's prediction of future order demand more accurate.

[0097] Specifically, in this embodiment, Kalman filtering includes the following two steps:

[0098] Prediction step: In this step, the system predicts future order demand based on the current state and external inputs. The prediction value is generated by the system model, which is assumed to be a linear system model. The Kalman filter predicts the following formula:

[0099]

[0100] in, is the predicted value of order demand at time t, is the updated state estimate at the previous time t-1; A is the state transition matrix, which describes the change pattern of the state over time; u(t) is the external input variable, which represents the external factors that may affect order demand (such as market fluctuations, seasonal changes, etc.); B is the control input matrix, which describes how external factors affect changes in demand.

[0101] In general, this step can be viewed as a linear process, in which the system predicts the state of order demand at a future point in time based on known historical data and current external input information.

[0102] Update step: In this stage, the Kalman filter will correct the predicted value based on the difference between the actual observed data and the predicted data; the update formula is as follows:

[0103] K(t)=P(t|t-1)H T [HP(t|t-1)H T +R] -1 ;

[0104]

[0105] P(t|t)=(IK(t)H)P(t|t-1);

[0106] Where K(t) is the Kalman gain, which is used to balance the weight between the predicted value and the observed value; P(t|t-1) is the prediction error covariance matrix, which represents the uncertainty of the system's state estimate at time t-1; H is the measurement matrix, which represents the relationship between the predicted value and the actual observed value; R is the measurement noise covariance matrix, which represents the noise level of the observation value. The estimated state at time t represents the revised order demand forecast value; The predicted state at time t-1 represents the estimate of order demand based on the information at the previous time; y(t) is the actual observed data, usually real-time order data; P(t|t) is the corrected error covariance matrix, which represents the update uncertainty of the state estimate at time t; I is the identity matrix, which represents the identity operation.

[0107] As an option, H is fixed, representing a linear relationship between observations and states; however, in some complex scenarios, the system may adjust the measurement matrix according to different observation methods to make the system more adaptable to the changing environment.

[0108] Specifically, through this step, the system can continuously adjust its forecast of future order demand based on the error between the actual observed value and the predicted value; the role of the Kalman gain K(t) is to determine the weighted ratio of the predicted value and the observed value, thereby ensuring that in uncertain situations, the system can automatically correct the prediction results, making subsequent order demand forecasts more accurate.

[0109] In some embodiments, in order to improve prediction accuracy, the system can adjust the parameters of the Kalman filter according to demand, such as increasing or decreasing the Kalman gain, to adapt to scenarios with large market fluctuations; specifically, the choice of Kalman gain will affect the response intensity to actual observation data. A higher gain value will enable the system to adjust the prediction value more quickly, while a lower gain value will make the system more conservative during the prediction process.

[0110] In one possible implementation, Kalman filtering is combined with other data analysis methods, such as neural network models and support vector machines, to further improve the accuracy of order demand forecasts. By combining these advanced data analysis methods with Kalman filtering, the system can maintain a high level of forecast accuracy even in complex and nonlinear market environments.

[0111] As an extension, this method can also be applied to real-time systems, where Kalman filtering can be combined with real-time data streams to gradually update forecasts of order demand. The system can continuously adjust its forecasts of demand changes based on the continuous influx of order data and quickly respond to market changes. Through this method, the platform can maintain efficient predictions of future demand in a dynamic environment and adjust resource allocation in a timely manner to ensure optimal resource scheduling.

[0112] S4. Based on the predicted order demand, construct an optimization model for resource scheduling using optimal control theory, where the optimization model includes an objective function, and the objective function is used to minimize the total cost of resource scheduling and satisfy business constraints;

[0113] In the previous steps, we have completed the order demand forecast and updated it in real time through Kalman filtering. Through this process, the system has obtained accurate order demand forecast data as the basic input for resource scheduling. The next step is to use optimal control theory to build an optimization model for resource scheduling. The core purpose of this model is to minimize the total cost of resource scheduling while meeting business constraints.

[0114] In this embodiment, the resource scheduling problem is modeled using optimal control theory based on the order demand forecast results. The goal of the optimal control problem is to enable the platform to meet order demand while reducing the total cost of resource usage through reasonable resource allocation. To achieve this goal, we define an objective function that not only includes the cost of resource scheduling, but also includes the conditions for satisfying business constraints.

[0115] Specifically, the optimal control model is optimized through the following objective function:

[0116]

[0117] Where: J(u) is the objective function of the resource scheduling optimization problem, which represents the total cost in the time interval 0≤t≤T; is the cost function of resource scheduling, which depends on the state x(t) of order demand, the control input u(t) and time t. I(u(t)) is the information entropy of the resource scheduling strategy, which measures the uncertainty of resource allocation and reflects the complexity of resource scheduling. λ is the weight coefficient of the information entropy term, which balances the relationship between cost and uncertainty. T represents the time period of the optimization problem, and the integration interval of the objective function is from time t = 0 to t = T.

[0118] In general, the first term in the objective function It is the main cost function of resource scheduling, usually including transportation costs, warehousing costs, personnel costs, etc., which depends on the platform's operating model; this cost directly affects the efficiency and cost of resource scheduling.

[0119] As an option, the second term I(u(t)) represents the information entropy of the resource scheduling policy and is used to measure the uncertainty of the scheduling decision. In some scenarios, if there is a large uncertainty in the system's scheduling policy, for example, when order demand fluctuates greatly or the external market is unstable, this uncertainty cost will become particularly important. By introducing this term, the system can optimize the scheduling policy to avoid resource waste and inefficiency.

[0120] Specifically, the goal of the optimal control model is to adjust the scheduling strategy u(t) so that the system can maintain a timely response to order demand while reducing total costs; the scheduling strategy u(t) usually includes the allocation of different resources (such as warehouse space, transportation tools, etc.), and decides when and how to schedule these resources based on predicted order demand.

[0121] In one possible implementation, the constraints of the model may include the following aspects:

[0122] Resource limitations: such as warehouse capacity, number of transportation vehicles, etc. The total amount of these resources may be constrained.

[0123] Satisfaction of order requirements: It must be ensured that all order requirements can be fulfilled in a timely manner, and order delays or failure to meet requirements are not allowed.

[0124] Time constraints: Some businesses may have time limits. For example, an order must be delivered within a specific time. The model needs to optimize scheduling in this case.

[0125] In some embodiments, the optimal control problem can be solved using numerical optimization methods, such as dynamic programming, genetic algorithms, simulated annealing, and other algorithms; these methods can effectively solve large-scale, complex optimal control problems, especially when taking into account resource constraints and external disturbances.

[0126] As an extension, in order to improve the accuracy of resource scheduling optimization, the system can also combine machine learning algorithms (such as support vector machines, neural networks, etc.) to predict resource demand and perform optimal scheduling on this basis; by learning historical order data and resource usage, the system can automatically optimize scheduling strategies, reduce manual intervention, and improve the system's automation level.

[0127] In one possible implementation, the optimal control model can also provide dynamic feedback to the actual operating conditions of the platform. When the platform obtains new order demands or changes in the external market environment in real time, the system can adjust the scheduling strategy through the feedback mechanism to ensure the optimal allocation of resources. For example, when market demand suddenly increases, the system can immediately adjust the resource scheduling strategy to ensure the timely fulfillment of orders.

[0128] S5. Solve the optimal control model using a variational method to obtain an optimal resource scheduling strategy, and perform resource allocation based on the strategy;

[0129] In the previous steps, we established an optimization model for resource scheduling through optimal control theory and set the optimization objective function based on the predicted order demand results. The next step is to use the variational method to solve the optimal control model, obtain the optimal resource scheduling strategy, and allocate resources according to this strategy to achieve the lowest resource scheduling cost while ensuring the effective satisfaction of order demand.

[0130] In this embodiment, the calculus of variations is used to solve the optimal control model. The main goal is to find a resource scheduling strategy that can minimize the objective function through mathematical optimization methods. The calculus of variations is a commonly used technique in optimal control. By constructing a Lagrangian function, the optimization problem is transformed into a solution problem, which can be solved using existing optimization methods.

[0131] Specifically, in the optimal control model, the form of the objective function J(u) has been given in step S4, which includes the resource scheduling cost and the complexity (information entropy) of the scheduling strategy; the goal is to find a control strategy u(t) that can minimize the objective function, that is:

[0132]

[0133] Where: J(u) is the objective function of the resource scheduling optimization problem, which represents the total cost in the time interval 0≤t≤T; is the cost function of resource scheduling, which depends on the state x(t) of order demand, the control input u(t) and time t. I(u(t)) is the information entropy of the resource scheduling strategy, which measures the uncertainty of resource allocation and reflects the complexity of resource scheduling. λ is the weight coefficient of the information entropy term, which balances the relationship between cost and uncertainty. T represents the time period of the optimization problem, and the integration interval of the objective function is from time t = 0 to t = T.

[0134] In the application of the calculus of variations, the Lagrange multiplier method can help us construct the Lagrange function to deal with constraints; the general form of the Lagrange function is:

[0135]

[0136] in, is the Lagrangian function, which represents the total cost function including the objective function and constraints, and is used to transform the optimal control problem into an optimization problem. C(x(t),u(t)) represents the cost function related to the order demand state x(t) and the resource scheduling input u(t). Specifically, C(x(t),u(t)) describes the expenses or costs incurred by activities such as resource utilization and order execution. represents the sum of all constraints, where: μ i (t) is the Lagrange multiplier, which represents the constraint condition g i (u(t)) affects the objective function; each constraint has a corresponding Lagrange multiplier, which represents the shadow price of the constraint; g i (u(t)) is the i-th constraint function in the resource scheduling model; the constraints usually include resource limitations, such as storage capacity, number of transportation tools, order fulfillment time, etc.

[0137] In one possible implementation, in order to enable the system to solve the optimization problem more efficiently, dynamic programming, numerical optimization methods or heuristic algorithms can be used; these methods can effectively handle large-scale and complex optimization problems, especially when considering the limitations and constraints of resources in actual operations.

[0138] As an option, the system can also solve the optimal scheduling strategy through the gradient descent method; by calculating the gradient of the objective function with respect to the control strategy u(t) and adjusting the strategy based on the gradient information until the optimal solution is found; the gradient descent method is usually applied to objective functions with differentiable properties and is suitable for large-scale optimization problems.

[0139] Specifically, in practical applications, the solution process is relatively complex, so it can be optimized step by step through segmented calculations. First, a rough estimate can be made, and the scheduling strategy can be adjusted based on the preliminary solution. Then, within each cycle, resource scheduling can be further optimized based on new feedback information.

[0140] In some embodiments, in addition to traditional optimization methods, the system can also incorporate machine learning algorithms to improve the solution of optimal control strategies. For example, deep reinforcement learning can continuously improve scheduling strategies through interaction with the environment, achieving long-term optimization. This approach, which trains intelligent agents to learn how to make optimal decisions in a given environment, is particularly well-suited for dealing with highly uncertain and dynamically changing market environments.

[0141] As an extension, variational methods and optimization algorithms can also be combined with big data analysis technology to further enhance the system's decision-making capabilities. For example, by introducing real-time order data and market environment data, the system can adjust and optimize scheduling strategies in real time to ensure that resource allocation is always in the optimal state. This real-time and adaptability enables the system to respond to emergencies and demand fluctuations.

[0142] S6. Handling order demand fluctuations and resource scheduling uncertainties through a stochastic control model that is optimized based on an objective function that minimizes expectations;

[0143] In the previous steps, we have constructed a dynamic system model of order demand and used Kalman filtering to predict it in real time. At the same time, optimal control theory has helped us develop an optimization model for the resource scheduling problem, with the goal of minimizing resource scheduling costs. Based on these steps, the system now faces the challenge of how to deal with the volatility and uncertainty in order demand, especially in a complex and changing market environment. To solve this problem, step S6 introduces a stochastic control model to optimize the resource scheduling strategy.

[0144] In this embodiment, as the market environment and order demand fluctuate, the system needs to deal with these uncertainties and find the optimal resource scheduling solution. To this end, we adopt a stochastic control model, the goal of which is to optimize based on expectation minimization. Stochastic control can handle the challenges brought about by external uncertainties (such as market demand fluctuations, emergencies, etc.) in resource scheduling.

[0145] Specifically, the optimization goal of the stochastic control model is to minimize the impact of order demand fluctuations and resource scheduling uncertainties on platform operations. To handle these uncertainties, we optimize using the following objective function:

[0146]

[0147] in, Represents solving the optimal control strategy u, the goal is to minimize the subsequent expected value; represents the expectation operation, which is used to calculate the average value under the influence of random disturbances; J(u) represents the objective function of the resource scheduling optimization problem; It is the cost function of resource scheduling, which depends on the system state x(t), control input u(t) and time t, and represents the scheduling cost at a certain moment; x(t) is the state vector of the system, which usually describes the order demand and resource usage content; u(t) is the scheduling policy, which represents the allocation decision of system resources at time t; T is the time period, which represents the scheduling optimization process from time 0 to time T.

[0148] Through expectation operations, we comprehensively consider the randomness caused by external disturbances and solve the optimal scheduling strategy on this basis; generally speaking, when faced with uncertainty, the expected value can provide a reasonable decision-making basis and help the system optimize under uncertain conditions.

[0149] Alternatively, in practical applications, the expected value can be calculated based on multiple simulations or historical data using the Monte Carlo method to approximate the expected value. This method can simulate actual market fluctuations and demand fluctuations through a large number of random samplings, thereby obtaining the expected performance of the system under different scenarios.

[0150] Specifically, in the process of handling fluctuations in order demand, the system first needs to model market changes through external environmental data and historical order data; for example, fluctuations in market demand may take the form of a random process, which can be simulated by a Brownian motion model or a Markov process; in this case, the stochastic control model can rationally allocate resources based on these simulation results.

[0151] In one possible implementation, the optimization goal of the stochastic control model is not only to minimize costs, but also to consider the flexibility and response speed of resource scheduling. For example, in some cases, although the total cost of resource allocation is low, due to the lack of sufficient flexibility, order demand may not be met in a timely manner, thereby affecting the operational efficiency of the platform. Therefore, during the optimization process, the system can introduce weights for flexibility and response speed to make the optimization results more in line with actual business needs.

[0152] As an extension, stochastic control models can also be combined with other advanced optimization methods, such as reinforcement learning. In reinforcement learning, the agent learns how to adjust resource scheduling strategies through interaction with the environment to minimize the costs of random perturbations and uncertainty. This approach enables the system to gradually improve its resource scheduling optimization capabilities through continuous practice, especially in the face of rapidly changing market environments.

[0153] S7. Construct a multi-level feedback control mechanism to dynamically adjust the resource scheduling strategy based on real-time data and demand forecast results. In the above steps, the dynamic changes of order demand have been modeled through a nonlinear dynamic system model, and Kalman filtering technology has also been applied to real-time order demand forecasting. Based on these forecast results, the platform has obtained expected data on changes in order demand and optimized resource scheduling through the optimal control model. However, in actual operations, order demand is often affected by various uncertainties and external factors, such as sudden market changes, seasonal fluctuations, etc. Therefore, relying solely on model predictions and preliminary scheduling strategies cannot cope with all possible changes. In order to improve the flexibility and responsiveness of the system, this step introduces a multi-level feedback control mechanism to dynamically adjust the resource scheduling strategy through real-time data feedback, thereby ensuring the optimal allocation of resources and timely satisfaction of order demand.

[0154] In this embodiment, when establishing a multi-level feedback control mechanism, the system will monitor changes in order demand and the execution of resource scheduling in real time; the feedback control mechanism automatically adjusts the resource scheduling strategy based on these real-time data and demand forecast results to cope with market fluctuations and sudden changes in demand; specifically, the feedback control mechanism includes real-time monitoring of current resource configuration and rapid response based on real-time order volume, external environmental changes and system status; the system dynamically adjusts the resource scheduling strategy through continuous feedback to ensure that the platform can operate efficiently in a complex and uncertain market environment.

[0155] Specifically, in this embodiment, the system implements dynamic adjustment of resource scheduling strategies through the following feedback control mechanisms:

[0156] Real-time data feedback: The platform collects real-time order demand data, market environment data, and other information from the system to obtain real-time information on order changes. This data is processed and analyzed by the platform's real-time data stream processing system to ensure that the system can promptly capture demand fluctuations and changes in the external environment. For example, market emergencies or changes in demand may require the platform to immediately adjust resource allocation, and the feedback mechanism can quickly reflect such demand changes in the scheduling strategy.

[0157] Demand forecast result feedback: Based on the real-time order demand forecast results of the Kalman filter model, the system can provide dynamic changes in order demand trends; based on these forecast data, the feedback control mechanism can make scheduling decisions in advance to avoid resource waste caused by demand fluctuations.

[0158] Dynamic adjustment of scheduling strategies: The feedback control mechanism not only relies on real-time order data and forecast data, but also adjusts resource scheduling strategies based on factors such as current resource utilization and order fulfillment status. For example, if a warehouse's inventory is too low and demand forecasts indicate that a product is about to experience a peak in demand, the feedback mechanism will automatically adjust resource allocation and allocate more warehouse resources to that product.

[0159] In some embodiments, the system may implement more detailed scheduling adjustments by setting different feedback levels; for example:

[0160] Level 1 feedback: Adjustments to the overall resource allocation of the system, such as the reallocation of logistics and transportation resources;

[0161] Secondary feedback: adjustments to a specific type of resources, such as production resources and storage space for a specific product;

[0162] Level 3 feedback: Provide feedback and adjustments to the processing of a specific order to ensure that the order can be completed in a timely manner;

[0163] As an option, in order to improve the efficiency and accuracy of the feedback mechanism, the system may combine intelligent algorithms to implement feedback control; for example, the reinforcement learning (RL) model can be used to learn and optimize scheduling strategies in real time, adjust the learning strategy based on the system's feedback, and further improve the intelligence of resource allocation; in this way, the intelligent algorithm will adjust the system based on historical feedback data, gradually improving the efficiency and accuracy of resource scheduling.

[0164] Specifically, the feedback mechanism can be described by the following mathematical model:

[0165]

[0166] Among them, u(t) represents the resource scheduling strategy, u opt (t) is the optimal resource scheduling strategy, K(t) is the feedback gain that represents the adjustment range between the current strategy and the prediction; y(t) is the actual observation value at time t, and H is the measurement matrix that describes the relationship between the actual observation value y(t) and the system state The relationship between is the predicted order demand; the size of the feedback gain determines the impact of the feedback control strategy on resource scheduling.

[0167] In one possible implementation, the system can adjust the parameters of the Kalman filter based on real-time feedback data, such as increasing or decreasing the Kalman gain, so that the system can respond more quickly to changes in demand. This dynamic adjustment capability makes the feedback control mechanism more flexible and can quickly adjust strategies according to market changes, thereby effectively avoiding resource waste and improving order fulfillment efficiency.

[0168] As an extension, the feedback control mechanism can also be combined with big data analysis technology; by analyzing historical feedback data, the system can identify the most effective scheduling strategy in different scenarios, thereby further optimizing scheduling decisions; big data-based feedback control can not only improve real-time response capabilities, but also enhance the stability and intelligence level of the overall scheduling system through long-term data accumulation.

[0169] See also Figure 2 , a business management system based on the one-stop inspection platform, including:

[0170] The data collection module is used to collect order demand data and external environmental data from all aspects of the platform, including demand information such as order quantity, product specifications, order type, as well as external factors such as market fluctuations and seasonal changes. This module connects with the order management system and external data interfaces (such as market data providers) to ensure that the collected data is accurate and timely, providing reliable input data for subsequent model building and forecasting.

[0171] The nonlinear dynamics model module is used to build a dynamic system model of order demand changes based on the platform's historical order demand data and external environmental factors. This module uses nonlinear dynamic equations to describe the change process of order demand over time and the impact of external disturbances on demand. This helps the platform identify and model the patterns of demand fluctuations, thereby providing a scientific basis for subsequent demand forecasting and scheduling decisions.

[0172] The demand forecasting module processes order demand data using Kalman filtering technology to perform dynamic forecasting. This module uses system models and historical order data, combined with real-time feedback, to make forecast corrections, thereby continuously improving the accuracy of future order demand forecasts. Through the recursive calculation of the Kalman filter, the demand forecasting module can update the order demand forecast value in real time to adapt to changes in the market environment and demand fluctuations.

[0173] The resource scheduling optimization module builds an optimization model for resource scheduling based on the forecast results provided by the demand forecasting module. This module rationally allocates resources by applying optimal control theory and variational methods to ensure optimal resource allocation while meeting agreed business requirements. The optimization process considers resource utilization efficiency, cost, and constraints, thereby minimizing resource scheduling costs and achieving efficient order fulfillment.

[0174] The stochastic control module addresses the uncertainties that arise in order demand and resource scheduling. This module establishes a stochastic control model and optimizes it based on an objective function that minimizes expectations, ensuring that resource scheduling can effectively respond to market fluctuations and external disturbances. By introducing the randomness of external disturbances, the stochastic control model enables the system to better adapt to uncertain environments and optimize resource allocation in actual operations.

[0175] Feedback control module, the feedback control module is responsible for dynamically adjusting the resource scheduling strategy based on real-time data and demand forecast results; this module quickly responds to market fluctuations by monitoring order demand, market changes and other data in real time, and adjusts resource allocation based on feedback information; feedback control ensures that the system can flexibly adjust according to real-time conditions in actual operations, avoiding resource waste or order fulfillment delays due to demand changes or emergencies.

[0176] See also Figure 3 , the resource scheduling optimization module includes:

[0177] The optimal control model solving unit is responsible for optimizing resource scheduling strategies based on the calculus of variations and optimal control theory. Based on the platform's order demand forecasts and constraints, this unit constructs optimization problems and solves the optimal resource scheduling strategy. Through mathematical optimization methods such as the Lagrange multiplier method and dynamic programming, the optimal control model solving unit can achieve efficient resource allocation, minimize costs, and ensure optimal resource scheduling performance while satisfying all constraints.

[0178] Multi-level feedback unit, the multi-level feedback unit dynamically adjusts resource scheduling strategies based on real-time demand data; this unit can continuously monitor market fluctuations, changes in order demand and resource usage, and adjust scheduling decisions based on real-time feedback; through a multi-level feedback mechanism, the system can flexibly respond to market changes and demand fluctuations according to different feedback levels, ensure timely allocation and effective utilization of resources, and improve overall operational efficiency and order fulfillment rates.

[0179] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A business management method based on a one-stop inspection platform, characterized in that: The following steps are involved: Collecting platform order demand data and external environment data, the order demand data includes order quantity, order type, and product specification information, and the external environment data includes market fluctuations and seasonal changes; Establishing a dynamic system model of order demand based on a nonlinear dynamic system model, wherein the nonlinear dynamic system model is used to describe the change of order demand over time and the influence of external disturbance factors; Performing Kalman filtering on the order demand data and updating the predicted value of the order demand in real time through recursive calculation; Based on the predicted order demand, an optimization model for resource scheduling is constructed using optimal control theory, wherein the optimization model includes an objective function, and the objective function is used to minimize the total cost of resource scheduling and meet business constraints; Solving the optimization model using a calculus of variations to obtain an optimal resource scheduling strategy, and performing resource allocation based on the strategy; Handling order demand fluctuations and resource scheduling uncertainties through a stochastic control model that optimizes an objective function based on expected minimization; Build a multi-level feedback control mechanism to dynamically adjust resource scheduling strategies based on real-time data and demand forecast results; The state equation of the nonlinear dynamic system model is: Where x(t) represents the state variable of the system, which is usually the quantity that needs to be tracked in the system; u(t) represents the external input variable, which is usually the external factor that affects the system; w(t) represents the system noise, which reflects the randomness and uncertainty that the model cannot capture; f(x(t),u(t),t) is the dynamic function of the system, which represents the law of the evolution of the system state over time, which depends on the current state x(t), the control input u(t) and the time t. The objective function in the optimal control theory is: Where J(u) is the objective function of the resource scheduling optimization problem, which represents the total integral value of the objective function in the time interval 0≤t≤T; is a cost function related to resource scheduling, which depends on the system state x(t), the control input u(t), and the time t, and is usually used to represent the cost of resource scheduling; I(u(t)) is the information entropy of the resource scheduling policy, which is used to measure the uncertainty of resource allocation; λ is the weight coefficient of the information entropy term, which controls the balance between cost and uncertainty in the resource scheduling optimization objective; T represents the time period of the optimization problem, and the integration interval of the objective function is from t = 0 to t = T; The objective function in the stochastic control model is: in, Represents solving the optimal control strategy u, the goal is to minimize the subsequent expected value; represents the expectation operation, which is used to calculate the average value under the influence of random disturbances; J(u) represents the objective function of the resource scheduling optimization problem; It is the cost function of resource scheduling, which depends on the system state x(t), control input u(t) and time t, and represents the scheduling cost at a certain moment; x(t) is the state vector of the system, which usually describes the order demand and resource usage content; u(t) is the scheduling policy, which represents the allocation decision of system resources at time t; T is the time period, which represents the scheduling optimization process from time 0 to time T.

2. The business management method based on the one-stop inspection platform according to claim 1 is characterized in that: The steps of the Kalman filter processing include: Prediction step: predicting order demand through historical data and system models; In the update step, the predicted value is corrected according to the real-time observation data, the Kalman gain is updated and the state estimation is optimized.

3. The business management method based on the one-stop inspection platform according to claim 1 is characterized in that: The variational method solution step includes: The Lagrangian function is constructed by the Lagrangian multiplier method to transform the resource scheduling problem into an optimization problem; Utilize optimization methods to solve the optimal resource scheduling strategy to meet business constraints and minimize total cost.

4. The business management method based on the one-stop inspection platform according to claim 1 is characterized in that: The multi-level feedback control mechanism includes: Feedback real-time data into the control system and adjust resource scheduling strategies based on the feedback information; Dynamically adjust scheduling strategies based on real-time order demand changes and resource status.

5. The business management method based on the one-stop inspection platform according to claim 4 is characterized in that: The control system performs data stream processing through a big data platform, and utilizes Apache Kafka and Apache Flink technologies to collect, transmit and process order data and market environment data.

6. A business management system based on a one-stop inspection platform, applied to a business management method based on a one-stop inspection platform according to any one of claims 1 to 5, characterized in that: include: Data collection module, used to collect platform order demand data and external environment data; Nonlinear dynamics model module, used to establish a dynamic system model of order demand; Demand forecasting module, used to perform Kalman filtering on order demand and generate demand forecast values; Resource scheduling optimization module, used to build resource scheduling optimization model and perform optimization based on demand forecast results; Stochastic control module, used to handle uncertainty in order demand and resource scheduling; The feedback control module is used to dynamically adjust resource scheduling strategies based on real-time data and demand forecast results.

7. The business management system based on the one-stop inspection platform according to claim 6 is characterized in that: The resource scheduling optimization module includes: Optimal control model solving unit, which optimizes resource scheduling strategies based on the calculus of variations and optimal control theory; Multi-level feedback units dynamically adjust scheduling strategies based on real-time demand data.

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