Business management method based on station inspection platform

Abstract

The invention relates to the field of business management, and discloses a business management method based on a station inspection platform, which comprises the following steps: collecting order demands and external environment data, establishing a dynamic system model of the order demands, carrying out Kalman filtering prediction, constructing an optimization model based on an optimal control theory and a variational method, and finally realizing optimization of a resource scheduling strategy. Demand fluctuation and uncertainty are processed, and resource configuration is dynamically adjusted by adopting a multi-level feedback mechanism; the invention further provides a service management system based on the station inspection platform. The service management system comprises a data acquisition module, a nonlinear dynamic model module, a demand prediction module, a resource scheduling optimization module, a random control module and a feedback control module. According to the method, a nonlinear dynamic system model and a Kalman filtering technology are combined, so that accurate order demand prediction and dynamic adjustment are realized; resource scheduling is optimized based on an optimal control theory, a random control model is introduced to cope with uncertainty, and a multilevel feedback mechanism ensures quick response and adjustment.

Description

Technical Field

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

[0002] In the existing fields of resource scheduling and demand forecasting, many platforms use rule-based static scheduling methods. Such methods usually rely on historical data and prediction models, but when faced with rapid changes in market demand, they often lack sensitivity; for example, when there are large fluctuations in demand, the system cannot adjust resource allocation in real time, resulting in low scheduling efficiency; in this way, even the initial scheduling plan may lose its timeliness during implementation and cannot adapt to market changes.

[0003] In addition, existing technologies generally adopt a centralized processing method, and all decisions are executed by a single system; this method not only easily leads to bottlenecks, but also often fails to make a quick response when faced with 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, and the lag in information flow will exacerbate the mistakes in resource allocation.

[0004] Finally, the existing systems also have certain limitations in dealing with uncertainties; although some traditional algorithms introduce probability models to handle the randomness of demand, most of them assume that market changes are stable and do not consider the uncertain factors in a complex and dynamic environment; the scheduling models constructed in this way often cannot adapt to a rapidly changing environment and are also difficult to meet the optimization goals in actual operation, resulting in a significant reduction in the scheduling effect of the system; therefore, the present invention proposes a business management method based on a one-stop inspection platform to solve the deficiencies of the existing technologies. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technologies, the present invention provides a business management method based on a one-stop inspection platform, which solves the problems of slow response of existing resource scheduling, bottlenecks caused by centralized decision-making, and insufficient handling of dynamic uncertainties. By introducing a more flexible distributed decision-making mechanism and dynamic adjustment function, the present 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 realized through the following technical solutions: A business management method based on a one-stop inspection platform, including the following steps: Collect order demand data and external environment data of the platform, where the order demand data includes the order quantity, order type, and product specification information, and the external environment data includes market fluctuations and seasonal change factors; Based on a non - linear dynamic system model, a dynamic system model of order demand is established. The non - linear dynamic system model is used to describe the change of order demand over time and the influence of external disturbance factors; Perform Kalman filtering on the order demand data, and recursively calculate to update the predicted value of the order demand in real - time; Based on the predicted order demand, an optimization model of resource scheduling is constructed using the optimal control theory. 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; Use the variational method to solve the optimal control model to obtain the optimal resource scheduling strategy, and perform resource allocation based on the strategy; Through a stochastic control model, handle the uncertainty in order demand fluctuations and resource scheduling. The stochastic control model is optimized based on an objective function of minimizing the expectation; Construct a multi - level feedback control mechanism to dynamically adjust the resource scheduling strategy according to real - time data and demand prediction results.

[0007] Preferably, the state equation of the non - linear dynamic system model is: Among them, \(x(t)\) represents the state variable of the system, usually the quantity that needs to be tracked in the system; \(u(t)\) represents the external input variable, usually the external factors that affect 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 system state evolving over time and depends on the current state \(x(t)\), control input \(u(t)\) and time \(t\).

[0008] Preferably, the steps of the Kalman filtering process include: Prediction step, predict the order demand through historical data and the system model; Update step, correct the predicted value according to real - time observation data, update the Kalman gain and optimize the state estimation.

[0009] Preferably, the objective function in the optimal control theory is: Among them, \(J(u)\) is the objective function of the resource scheduling optimization problem, which represents the total integral value of the objective function in the time interval \(0\leq t\leq T\); is a cost function related to resource scheduling, which depends on the system state x(t), control input u(t), and 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 strategy, 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 goal; T represents the time period of the optimization problem, and the integration interval of the objective function is from t = 0 to t = T.

[0010] Preferably, the variational method solving steps include: Construct a Lagrangian function through the Lagrange multiplier method to transform the resource scheduling problem into an optimization problem; Use the optimization method to solve the optimal resource scheduling strategy, which satisfies the business constraints and minimizes the total cost.

[0011] Preferably, the objective function in the stochastic control model is: Wherein, represents solving the optimal control strategy u, and 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 perturbations; J(u) represents the objective function of the resource scheduling optimization problem; 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, usually describing order requirements and resource usage content; u(t) is the scheduling strategy, which represents the deployment decision of system resources at time t; T is the time period, which represents the scheduling optimization process from time 0 to time T.

[0012] Preferably, the multi-level feedback control mechanism includes: Feed the real-time data back into the control system and adjust the resource scheduling strategy according to the feedback information; Dynamically adjust the scheduling strategy according to the real-time order demand changes and resource status; The real-time data feedback system processes the data stream through a big data platform, and uses Apache Kafka and Apache Flink technologies to collect, transmit, and process order data and market environment data.

[0013] Preferably, the real-time data feedback system processes the data stream through a big data platform, and uses Apache Kafka and Apache Flink technologies to collect, transmit, and process order data and market environment data.

[0014] The present invention provides a business management system based on a one-stop inspection platform, including: A data acquisition module for collecting order demand data and external environment data of the platform; A non-linear dynamics model module for establishing a dynamic system model of order demand; A demand prediction module for performing Kalman filtering on order demand and generating demand prediction values; A resource scheduling optimization module for constructing and optimizing a resource scheduling optimization model according to the demand prediction result; A stochastic control module for handling uncertainties in order demand and resource scheduling; A feedback control module for dynamically adjusting the resource scheduling strategy according to real-time data and demand prediction results.

[0015] Preferably, the resource scheduling optimization module includes: An optimal control model solving unit for optimizing the resource scheduling strategy based on the variational method and optimal control theory; A multi-level feedback unit for dynamically adjusting the scheduling strategy according to real-time demand data.

[0016] The present invention provides a business management method based on a one-stop inspection platform. It has the following beneficial effects: 1. The present invention adopts a scheme combining a non-linear dynamics system model and Kalman filtering technology, which can accurately predict order demand and dynamically adjust through real-time feedback; achieving the technical effect of quickly adapting to demand changes in an uncertain market environment; compared with the single static prediction model in the prior art, the present invention can update the prediction result in real time, ensuring more flexible and efficient resource scheduling of the platform, and avoiding resource waste and order delay caused by demand prediction errors.

[0017] 2. The present invention constructs a resource scheduling optimization model based on the optimal control theory and solves it in combination with the variational method to minimize the resource scheduling cost while meeting business constraints; compared with traditional resource scheduling methods, the prior art often ignores the balance between constraints and costs and cannot comprehensively optimize resource allocation; the present invention can perform efficient scheduling in a more complex scheduling environment by optimizing the objective function and introducing information entropy, maximizing the utilization rate of resources.

[0018] 3. The present invention introduces a stochastic control model to handle the challenges brought by order demand fluctuations and external environment uncertainties; optimizing through an objective function of minimizing expectation, achieving the technical effect of stable operation in market fluctuations, emergencies and external disturbances; compared with the prior art solutions that fail to effectively cope with uncertainty factors, the present invention can optimize resource allocation under uncertainty by introducing stochastic control, improving the system's response ability and stability.

[0019] 4. The present invention adopts a multi-level feedback control mechanism to monitor the order demand and resource scheduling status in real time and dynamically adjust the scheduling strategy, achieving the technical effect of rapid response and adjustment in the case of large demand fluctuations. In the prior art, there are often problems of insufficient feedback control, resulting in the inability to flexibly respond 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 reaction speed of the system and the real-time nature of resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flowchart of the method of the present invention; Figure 2 is an architecture diagram of the system of the present invention; Figure 3 is an architecture diagram of the resource scheduling optimization module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] Please refer to Figure 1 , the embodiments of the present invention provide a business management method based on a one-stop inspection platform, including the following steps: S1. Collect order demand data and external environment data of the platform. The order demand data includes the order quantity, order type, and product specification information, and the external environment data includes market fluctuations and seasonal change factors; 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 the change of order demand and provide the necessary basic data for subsequent modeling and prediction. The collection of order demand data includes the order quantity, order type, product specification, etc. These data help to accurately describe 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.

[0023] In this embodiment, the data collection module first collects the order demand data of the platform. The order demand data mainly comes from links such as customer order placement, order execution, and product shipment. Specifically, the order demand data includes: Order quantity: That is, the total number of orders within a specified time, which is an important data for measuring the business volume and operation status of the platform; Order type: The specific classification of orders, which may be divided according to different service types, product types, or customer types; by understanding the order type, the platform can accurately analyze the demand fluctuation patterns of different types of orders; Product specification information: Different products sold on the platform have different specifications, and the order demand data includes information such as the types, specifications, and quantities 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.

[0024] In addition, the collection of external environment data is also an important part of step S1; external environment data includes market fluctuations and seasonal change factors, which have a significant impact on the order demand of the platform; external environment data is usually obtained from third-party data sources or partner systems; for example: Market fluctuations: External economic environment factors such as market price fluctuations, policy changes, and industry dynamics will affect the order demand of the platform; for example, when the market demand is strong, the order volume of customers may increase sharply, and vice versa. Seasonal change factors: Different seasons or holidays may cause periodic 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.

[0025] In a 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 and inventory management systems) and external data interfaces (such as data provided by market research agencies and economic data).

[0026] Specifically, the processing flow of order demand data and external environment data is as follows: Data collection: The system first obtains order data from the platform database through the API interface, records the detailed information of each order, including order quantity, type, specification, etc.; external environment data is obtained in real time by acquiring economic data, market indices, etc., to ensure the timeliness and accuracy of the data.

[0027] Data integration and preprocessing: After collecting the 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, and the system will automatically filter out these data and retain the valid data for subsequent analysis.

[0028] In some embodiments, to ensure data accuracy, the platform may adopt a data verification mechanism; for example, by comparing the actual order execution with customer feedback, the platform can calibrate and correct the 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 timely adjust the impact of seasonal changes or market fluctuations on order demand forecasting.

[0029] During the data collection and preprocessing process, the system will use the following mathematical model to assist in analyzing and integrating data: D = {D 1 , D 2 ,..., D n}; where D 1 to D n represent the order demand and external environment data collected from different data sources, and D is the total set of the data set; each D i contains the detailed information of the order and the influencing factors related to the external environment.

[0030] Furthermore, during the implementation process, the quantitative processing of external environment data is very crucial; for example, market fluctuations can be represented by market indices, and seasonal change factors can be quantitatively analyzed through certain specific economic models; these quantified external environment data can be converted into numerical values and directly applied to subsequent demand forecasting models.

[0031] For example, market fluctuations may be modeled in the following way: M(t) = α·M 0 + β·P(t); where M(t) represents the market fluctuation index at time t, M 0 is the basic market fluctuation index, P(t) represents a certain economic indicator (such as the consumer confidence index, etc.), and α and β are weight coefficients.

[0032] As an option, external environment data can be further processed and predicted through machine learning models, especially when dealing with a large number of non-linear relationships; through learning historical data, the system can gradually improve the prediction accuracy of future market fluctuations and seasonal changes.

[0033] In a possible implementation manner, the platform can also combine historical sales data and external environment data to build an order demand forecasting model through regression analysis or neural network models; the output of the forecasting model will be used as the input for subsequent steps (such as Kalman filtering or optimal control) to help the system make more accurate resource scheduling decisions.

[0034] S2. Establish a dynamic system model of order demand based on the non-linear dynamics system model, where the non-linear dynamics system model is used to describe the change of order demand over time and the influence of external disturbance factors. In the above steps, the platform has collected relevant order demand data and external environment data, providing data support for subsequent prediction and decision-making. Based on the collected data, the next step is to establish a non-linear dynamics system model to describe the law of order demand change over time and consider the influence of external disturbance factors on the system. The non-linear dynamics model will provide a mathematical description of the system behavior for subsequent demand prediction, resource scheduling optimization, and other links.

[0035] In this embodiment, by introducing the non-linear dynamics system model, we abstract the evolution process of order demand into a dynamic system, aiming to accurately describe the dynamic characteristics of demand change over time and how external disturbances affect this process. To adapt to a complex business environment, the model uses non-linear equations to construct the system state equation to more precisely capture the complexity and uncertainty of the system.

[0036] Specifically, the basic form of the state equation of the non-linear dynamics model is as follows: Among them, x(t) is the state vector of order demand, representing the order demand volume of the platform at time t; u(t) is the external input variable, usually representing external factors affecting system demand, such as market fluctuations, seasonal changes, promotional activities, etc.; w(t) is the system noise, reflecting the demand fluctuations caused by external factors or uncontrollable factors, often existing in the form of a stochastic process; and f(x(t), u(t), t) is a non-linear function describing the dynamic change of the system, which represents the change law of order demand.

[0037] Generally, 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 non-linear function f(x(t), u(t), t) in the model can be selected according to different application scenarios, and it usually includes the non-linear relationship between order demand volume and time, the influence of market factors, etc.

[0038] As an option, the dynamic system model can include some known non-linear function forms, such as exponential functions, logarithmic functions, Sigmoid functions, etc. These functions can effectively describe the non-linear characteristics of demand growth or decline. For example, some products may show exponential growth during the promotion period and linear decline during a sluggish market.

[0039] Specifically, the model of order demand varying with time may adopt the following non-linear relationship: f(x(t), u(t), t) = Ax(t) + Bu(t) + Cx(t) 2 + Du(t) 2 ; Where f(x(t), u(t), t) represents the dynamic change function of order demand, 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 represents the vector of order demand quantity in this case; u(t) is the external input variable, usually representing 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 external input on order demand; C represents the non-linear influence coefficient of the system state variable, reflecting the non-linear growth or decline characteristics of demand; D represents the non-linear influence coefficient of external input, reflecting the non-linear influence of external disturbances on order demand.

[0040] In a possible implementation, the system may estimate the model parameters based on historical data and experience; for example, statistical methods such as the least squares method and maximum likelihood method can be used to fit the data to obtain the optimal model parameters; through the regression analysis of historical order data and external environment data, the system can learn the laws of order demand changing with time and external factors, and adjust the system's prediction model accordingly.

[0041] In some embodiments, this non-linear dynamics model can also be combined with other machine learning algorithms to further improve the accuracy of demand prediction; for example, the system can use a neural network model to capture complex patterns in demand changes, or adopt a support vector machine (SVM) to handle non-linear characteristics in demand prediction; these algorithms can automatically learn the laws of demand changes from data, thereby enhancing the system's prediction ability.

[0042] Specifically, to improve the accuracy of the model, the establishment of the dynamic system model can be further optimized through the following steps: Collect more historical order data to ensure the representativeness and diversity of the data; Establish multiple demand prediction models according to changes in different dimensions such as the market and products; In practical applications, continuously adjust and optimize the model to ensure that the model can adapt to demand changes in different market environments.

[0043] As an option, when dealing with external disturbance factors, the platform can also utilize data fusion technology to fuse external environment data from different data sources. For example, market fluctuations can be quantified through data such as economic indices and consumer confidence indices, while seasonal change factors can be predicted by analyzing historical sales data. Through this data fusion method, the platform can more comprehensively understand the impact of external environment changes on order demand.

[0044] S3. Perform Kalman filtering on the order demand data, and recursively calculate to update the predicted value of the order demand in real time. In step S2, the change law of the order demand has been described by establishing a non-linear dynamic system model, and the impact of external factors on the demand has been considered. On this basis, in order to further improve the accuracy of order demand prediction, the system performs real-time prediction and update on the order demand data through Kalman filtering technology. Kalman filtering adjusts the predicted value of future order demand by recursion based on historical data and current real-time observation values.

[0045] In this embodiment, the Kalman filtering of the order demand data mainly includes two core steps: the prediction step and the update step. Kalman filtering is a recursive algorithm, and its advantage lies in being able to handle uncertainty and noise and maintaining a high prediction accuracy in real-time data.

[0046] Specifically, the working principle of Kalman filtering is to perform recursive calculation in combination with the system model based on existing historical data and current observation data to update the predicted value, so that the system's prediction of future order demand is more accurate.

[0047] Specifically, in this embodiment, Kalman filtering includes the following two major steps: Prediction step: In this step, the system predicts the future order demand based on the current state and external input. The predicted value is generated by the system model, assuming that the model is a linear system model. Kalman filtering makes predictions through the following formula: Where, is the predicted value of the 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 law of the state over time; u(t) is the external input variable, representing external factors that may affect the order demand (such as market fluctuations, seasonal changes, etc.); B is the control input matrix, which describes how external factors affect the change of demand.

[0048] Generally, this step can be regarded as a linear process, in which the system predicts the state of the order demand at future time points based on known historical data and current external input information.

[0049] Update step: In this stage, the Kalman filter will correct the predicted value according to the difference between the actual observed data and the predicted data; the update formula is as follows: K(t) = P(t|t - 1)H T [HP(t|t - 1)H T + R] -1 ; P(t|t) = (I - K(t)H)P(t|t - 1); where K(t) is the Kalman gain, which is used to balance the weights between the predicted value and the observed value; P(t|t - 1) is the predicted error covariance matrix, which represents the uncertainty of the system's state estimation 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 observed value; The estimated state at time t represents the predicted value of the order demand after correction; The predicted state at time t - 1 represents the estimation of the order demand based on the information of 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 updated uncertainty of the state estimation at time t; I is the identity matrix, which represents the identity operation.

[0050] As an option, H is fixed, representing the linear relationship between the observed value and the state; 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.

[0051] Specifically, through this step, the system can continuously adjust the prediction of future order demand according to 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, so as to ensure that in the case of uncertainty, the system can automatically correct the prediction result and make the subsequent order demand prediction more accurate.

[0052] In some embodiments, in order to improve the prediction accuracy, the system can adjust the parameters of the Kalman filter according to the demand situation, such as increasing or decreasing the Kalman gain, to adapt to the scenario with large market fluctuations; specifically, the choice of the Kalman gain will affect the response intensity to the actual observed data, a higher gain value will make the system adjust the predicted value more quickly, while a lower gain value will make the system more conservative in the prediction process.

[0053] In a possible implementation, Kalman filtering is combined with other data analysis methods, such as neural network models, support vector machines, etc., to further improve the accuracy of order demand prediction; by combining these advanced data analysis methods with Kalman filtering, the system can still maintain a high prediction accuracy when facing a complex and non-linear market environment.

[0054] 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 the prediction of order demand; the system can continuously adjust the prediction of demand changes according to the continuously incoming order data and quickly respond to market changes; through this method, the platform can maintain an efficient prediction of future demand in a dynamic environment and timely adjust resource allocation to ensure optimal resource scheduling.

[0055] S4. Based on the predicted order demand, an optimization model for resource scheduling is constructed using the optimal control theory. The optimization model includes an objective function, which is used to minimize the total cost of resource scheduling and satisfy business constraints. In the above steps, we have completed the prediction of order demand and updated it in real time through Kalman filtering; through this process, the system has obtained accurate order demand prediction data as the basic input for resource scheduling; the next step is to construct an optimization model for resource scheduling using the optimal control theory. The core purpose of this model is to minimize the total cost of resource scheduling on the premise of satisfying business constraints.

[0056] In this embodiment, based on the order demand prediction result, the optimal control theory is used to model the resource scheduling problem; the goal of the optimal control problem is to enable the platform to reduce the total cost of resource use while satisfying the order demand through reasonable resource allocation; to achieve this goal, we define an objective function that includes not only the cost of resource scheduling but also the conditions for satisfying business constraints.

[0057] Specifically, the optimal control model is optimized through the following objective function: where: J(u) is the objective function of the resource scheduling optimization problem, representing the total cost within 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.

[0058] Generally, the first term in the objective function is the main cost function for resource scheduling, which usually includes transportation costs, warehousing costs, personnel costs, etc., depending on the specific operating mode of the platform; this cost directly affects the efficiency and cost of resource scheduling.

[0059] As an option, the second term I(u(t)) represents the information entropy of the resource scheduling strategy, which is used to measure the uncertainty of the scheduling decision; in some scenarios, if there is a large uncertainty in the system's scheduling strategy, for example, when the 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 strategy to avoid resource waste and inefficiency.

[0060] Specifically, the goal of this optimal control model is to adjust the scheduling strategy u(t) so that the system can reduce the total cost while maintaining a timely response to order demands; 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 according to the predicted order demands.

[0061] In a possible implementation, the constraint conditions of this model may include the following aspects: Resource limitations: such as the capacity of the warehouse, the number of transportation tools, etc., and the total amount of these resources may be restricted.

[0062] Meeting order demands: It is necessary to ensure that all order demands can be fulfilled in a timely manner, and order delays or unmet situations are not allowed.

[0063] Time constraints: There may be time limits for certain operations. For example, order deliveries need to be completed within a specific time, and the model needs to optimize the scheduling in this case.

[0064] In some embodiments, the solution of the optimal control problem can adopt numerical optimization methods, such as dynamic programming, genetic algorithms, simulated annealing and other algorithms; these methods can effectively solve large-scale and complex optimal control problems, especially when considering resource constraints and external disturbances.

[0065] 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 demands and perform optimal scheduling based on this; by learning historical order data and resource usage, the system can automatically optimize the scheduling strategy, reduce manual intervention, and improve the automation level of the system.

[0066] In a possible implementation, the optimal control model can also perform dynamic feedback with the actual operation of the platform; when the platform obtains new order demands or external market environment changes in real time, the system can adjust the scheduling strategy through a feedback mechanism to ensure the optimal allocation of resources; for example, when the market demand suddenly increases, the system can immediately adjust the resource scheduling strategy to ensure the timely fulfillment of orders.

[0067] S5. Solve the optimal control model using the variational method to obtain the optimal resource scheduling strategy, and perform resource allocation based on the strategy; In the foregoing steps, we established an optimization model for resource scheduling through the optimal control theory, and set an optimized 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 perform resource allocation according to this strategy to achieve the lowest resource scheduling cost while ensuring the effective satisfaction of order demands.

[0068] In this embodiment, the main objective of using the variational method to solve the optimal control model is to find a resource scheduling strategy that can minimize the objective function through mathematical optimization methods; the variational method is a commonly used technique in optimal control. By constructing the Lagrangian function, the optimization problem is transformed into a problem that can be solved, so that existing optimization methods can be used for solution.

[0069] 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 the control strategy u(t) that can minimize the objective function, that is: where: J(u) is the objective function of the resource scheduling optimization problem, representing the total cost within the time interval 0 ≤ t ≤ T; is the cost function of resource scheduling, which depends on the state x(t) of the order demand, the control input u(t), and the 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.

[0070] In the application of the variational method, the Lagrange multiplier method can help us construct the Lagrangian function to handle the constraint conditions; the general form of the Lagrangian function is: where, is the Lagrangian function, representing the total cost function that includes the objective function and the constraint conditions, 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 generated by activities such as resource usage and order execution. represents the sum of all constraint conditions, where: μ i (t) is the Lagrange multiplier, representing the influence of the constraint condition g i (u(t)) on the objective function; each constraint condition has a corresponding Lagrange multiplier, representing the shadow price of the constraint; g i (u(t)) is the i-th constraint function in the resource scheduling model; the constraint conditions usually include resource limitations, such as storage capacity, the number of transportation tools, order fulfillment time, etc.

[0071] In a possible implementation, in order to enable the system to solve this optimization problem more efficiently, dynamic programming, numerical optimization methods or heuristic algorithms can be adopted; these methods can effectively handle large-scale and complex optimization problems, especially when considering the limitations and constraints of resources in actual operations.

[0072] 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 according to 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.

[0073] Specifically, in practical applications, the complexity of the solution process is relatively high, so it can be optimized step by step through piecewise calculation; first, a rough estimate can be made, and the scheduling strategy can be adjusted according to the preliminary solution; then, in each cycle, the resource scheduling can be further optimized according to the new feedback information.

[0074] In some embodiments, in addition to traditional optimization methods, the system can also combine machine learning algorithms to improve the solution of the optimal control strategy. For example, deep reinforcement learning can continuously improve the scheduling strategy through interaction with the environment to achieve the goal of long-term optimization. This method trains an agent to learn how to make optimal decisions in a given environment and is especially suitable for dealing with highly uncertain and dynamically changing market environments.

[0075] As an extension, variational methods and optimization algorithms can also be combined with big data analysis techniques to further enhance the system's decision-making ability. For example, by introducing real-time order data and market environment data, the system can adjust and optimize the scheduling strategy in real time to ensure that the resource allocation is always in the optimal state. This real-time nature and self-adaptability enable the system to handle emergencies and demand fluctuations.

[0076] S6. Through a stochastic control model, handle the uncertainties in order demand fluctuations and resource scheduling. The stochastic control model is optimized based on an objective function of minimizing expectations. In the previous steps, we have constructed a dynamic system model of order demand and used Kalman filtering for real-time prediction. At the same time, the optimal control theory has helped us formulate an optimization model for the resource scheduling problem with the goal of minimizing the resource scheduling cost. Based on these steps, the challenge the system is now facing is how to handle the volatility and uncertainty in order demand, especially in a complex and ever-changing market environment. To solve this problem, step S6 introduces a stochastic control model to optimize the resource scheduling strategy.

[0077] In this embodiment, with the volatility of the market environment and order demand, the system needs to handle these uncertainties and find the optimal resource scheduling solution. For this purpose, we adopt a stochastic control model, and the goal of this model is to be optimized in a way of minimizing expectations. Stochastic control can handle the challenges brought by external uncertainties (such as market demand fluctuations, emergencies, etc.) in resource scheduling.

[0078] Specifically, the optimization goal of the stochastic control model is to minimize the impact of order demand fluctuations and resource scheduling uncertainties on the platform operation. To handle these uncertainties, we optimize through the following objective function: where represents solving for the optimal control strategy u, with the goal of minimizing the subsequent expected value; represents the expectation operation, used to calculate the average value under the influence of stochastic disturbances; J(u) represents the objective function of the resource scheduling optimization problem; is the cost function of resource scheduling, which depends on the system state x(t), control input u(t), and time t, representing the scheduling cost at a certain moment; x(t) is the state vector of the system, usually describing order demand and resource usage content; u(t) is the scheduling strategy, representing the decision on the allocation of system resources at time t; T is the time period, representing the scheduling optimization process from time 0 to time T.

[0079] Through the expected value operation, we comprehensively consider the randomness caused by external disturbances and solve the optimal scheduling strategy based on this; generally, in the face of uncertainty, the expected value can provide a reasonable decision-making basis to help the system optimize under uncertain conditions.

[0080] As an option, in practical applications, the calculation of the expected value can be based on multiple simulations or historical data and approximated by the Monte Carlo method; this method can simulate the actual market fluctuations and demand fluctuations through a large number of random samplings to obtain the expected performance of the system under different scenarios.

[0081] Specifically, in the process of dealing with order demand fluctuations, the system first needs to model the market changes through external environmental data and historical order data; for example, the fluctuations in market demand may take the form of a stochastic process and can be simulated by a Brownian motion model or a Markov process; in this case, the stochastic control model can reasonably allocate resources according to these simulation results.

[0082] In a 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 relatively low, due to the lack of sufficient flexibility, it may not be able to meet order demands in a timely manner, thus affecting the operation efficiency of the platform; therefore, in 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.

[0083] As an extension, the stochastic control model can also be combined with other advanced optimization methods, such as Reinforcement Learning, etc.; in Reinforcement Learning, the agent learns how to adjust the resource scheduling strategy through interaction with the environment to minimize the costs brought by random disturbances and uncertainties; this method enables the system to gradually improve the optimization ability of resource scheduling in continuous practice, especially when facing a rapidly changing market environment.

[0084] S7. Build a multi-level feedback control mechanism to dynamically adjust the resource scheduling strategy according to real-time data and demand prediction results; in the foregoing steps, the dynamic changes in order demand have been modeled through a non-linear dynamics system model, and the Kalman filtering technology has also been applied to real-time order demand prediction; based on these prediction results, the platform has obtained the expected data on the changes in order demand and optimized the resource scheduling through an optimal control model; however, in actual operation, order demand is often affected by various uncertainties and external factors, such as sudden market changes, seasonal fluctuations, etc. Therefore, relying solely on model prediction and preliminary scheduling strategies cannot handle all possible changes; 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, so as to ensure the optimal allocation of resources and the timely satisfaction of order demand.

[0085] In this embodiment, when establishing the multi-level feedback control mechanism, the system will monitor the changes in order demand and the execution of resource scheduling in real time; the feedback control mechanism automatically adjusts the resource scheduling strategy according to these real-time data and demand prediction results to cope with market fluctuations and sudden changes in demand; specifically, the feedback control mechanism includes real-time monitoring of the current resource allocation and making a quick response according to the real-time order volume, external environment changes, and system status; through continuous feedback, the system dynamically adjusts the resource scheduling strategy to ensure that the platform can operate efficiently in a complex and uncertain market environment.

[0086] Specifically, in this embodiment, the system realizes the dynamic adjustment of the resource scheduling strategy through the following several feedback control mechanisms: Real-time data feedback: The platform collects real-time order demand data, market environment data, etc. in the system to obtain the changes in orders in real time; these data are processed and analyzed through the platform's real-time data stream processing system to ensure that the system can timely capture demand fluctuations and changes in the external environment; for example, market emergencies or demand changes may cause the platform to immediately adjust resource allocation, and the feedback mechanism can quickly reflect such demand changes in the scheduling strategy.

[0087] Demand prediction result feedback: Based on the real-time order demand prediction results of the Kalman filter model, the system can provide the dynamic change trend of order demand; according to these prediction data, the feedback control mechanism can make scheduling decisions in advance to avoid resource waste caused by demand fluctuations.

[0088] Dynamic adjustment of scheduling strategy: The feedback control mechanism not only depends on real-time order data and prediction data, but also adjusts the resource scheduling strategy according to factors such as the current utilization of resources and the order fulfillment status. For example, if the inventory level in a certain warehouse is too low and the demand forecast shows that the product is about to enter a demand peak, the feedback mechanism will automatically adjust the resource allocation and allocate more warehouse resources to this product.

[0089] In some embodiments, the system may achieve more detailed scheduling adjustment by setting different feedback levels. For example: First-level feedback: Adjustment of the overall resource configuration of the system, such as the reallocation of logistics and transportation resources; Second-level feedback: Adjustment of a specific type of resource, such as the production resources and storage space of a specific product; Third-level feedback: Feedback adjustment of the processing of a specific order to ensure that the order can be completed in a timely manner; As an option, in order to improve the efficiency and accuracy of the feedback mechanism, the system may combine intelligent algorithms to achieve feedback control. For example, the Reinforcement Learning model can be used to learn and optimize the scheduling strategy in real time, adjust the learning strategy according to the feedback of the system, and further improve the intelligence of resource allocation. In this way, the intelligent algorithm will adjust the system according to historical feedback data, gradually improving the efficiency and accuracy of resource scheduling.

[0090] Specifically, this feedback mechanism can be described by the following mathematical model: where \(u(t)\) represents the resource scheduling strategy, \(u^*(t)\) is the optimal resource scheduling strategy, \(K(t)\) is the feedback gain representing the adjustment amplitude between the current strategy and the prediction; \(y(t)\) is the actual observation value at time \(t\), \(H\) is the measurement matrix, which describes the relationship between the actual observation value \(y(t)\) and the system state opt and \(\hat{y}(t)\) is the predicted order demand; the magnitude of the feedback gain determines the impact of the feedback control strategy on resource scheduling.

[0091] In a 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 to demand changes more quickly. This dynamic adjustment ability makes the feedback control mechanism more flexible, able to quickly adjust the strategy according to market changes, thereby effectively avoiding resource waste and improving order fulfillment efficiency.

[0092] As an extension, the feedback control mechanism can also be combined with big data analysis techniques; by analyzing historical feedback data, the system can identify the most effective scheduling strategies in different scenarios, thereby further optimizing scheduling decisions; the feedback control based on big data can not only improve the real-time response ability, but also enhance the stability and intelligence level of the overall scheduling system through long-term data accumulation.

[0093] Please refer to Figure 2 , a business management system based on the one-stop inspection platform, including: A data collection module, which is used to collect order demand data and external environment data from various links of the platform, including demand information such as order quantity, product specifications, order types, and external factors such as market fluctuations and seasonal changes; this module ensures the accuracy and timeliness of the collected data by docking with the order management system and external data interfaces (such as market data providers), and can provide reliable input data for subsequent model establishment and prediction. A non-linear dynamics model module, which is used to establish a dynamic system model of order demand changes based on the platform's historical order demand data and external environment factors; this module describes the change process of order demand over time and the impact of external disturbances on demand through non-linear dynamics equations, helping the platform identify and model the laws of demand fluctuations, so as to provide a scientific basis for subsequent demand forecasting and scheduling decisions. A demand forecasting module, which processes order demand data through Kalman filtering technology for dynamic forecasting; this module uses the system model and historical order data, combined with real-time feedback for prediction correction, so as to continuously improve the forecasting accuracy of future order demands; through the recursive calculation of Kalman filtering, 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. A resource scheduling optimization module, which constructs an optimization model for resource scheduling according to the prediction results provided by the demand forecasting module; this module rationally allocates resources by applying optimal control theory and variational methods to ensure the optimization of resource allocation while meeting the agreed business requirements; the optimization process considers the utilization efficiency, cost and constraints of resources, so as to minimize the resource scheduling cost and achieve efficient order fulfillment. A stochastic control module, which deals with the uncertainties that occur in the process of order demand and resource scheduling; this module optimizes based on the objective function of expected minimization by establishing a stochastic control model to ensure that the resource scheduling can effectively respond to market fluctuations and external disturbances; the stochastic control model makes the system better adapt to the uncertain environment in actual operation and optimize resource allocation by introducing the randomness factor of external disturbances. Feedback control module. The feedback control module is responsible for dynamically adjusting the resource scheduling strategy based on real-time data and demand prediction results. This module monitors data such as order demand and market changes in real time, quickly responds to market fluctuations, and adjusts resource allocation according to the feedback information. Feedback control ensures that the system can be flexibly adjusted according to real-time situations during actual operation, avoiding resource waste or order fulfillment delays caused by demand changes or emergencies.

[0094] Please refer to Figure 3 , the resource scheduling optimization module includes: Optimal control model solving unit. The optimal control model solving unit is responsible for optimizing the resource scheduling strategy based on the variational method and optimal control theory. This unit constructs an optimization problem and solves the optimal resource scheduling strategy according to the order demand prediction and constraint conditions of the platform. 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 the optimal performance of the platform in resource scheduling under the premise of meeting all constraint conditions. Multi-level feedback unit. The multi-level feedback unit dynamically adjusts the resource scheduling strategy according to real-time demand data. This unit can continuously monitor market fluctuations, order demand changes, and resource usage conditions, and adjust scheduling decisions based on real-time feedback. Through the multi-level feedback mechanism, the system can flexibly respond to market changes and demand fluctuations according to different feedback levels, ensure the timely allocation and effective utilization of resources, and improve the overall operation efficiency and order fulfillment rate.

[0095] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present 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 the platform's 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; A dynamic system model of order demand is established 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; The optimal control model is solved by using the variational method to obtain the optimal resource scheduling strategy, and resources are allocated based on the strategy; order demand fluctuations and uncertainties in resource scheduling are handled by using a stochastic control model, and the stochastic control model is optimized based on an objective function that is expected to be minimized; Build a multi-level feedback control mechanism to dynamically adjust resource scheduling strategies based on real-time data and demand forecast results.

2. The business management method based on the one-stop inspection platform according to claim 1 is characterized in that: The state equation of the nonlinear dynamic system model is: 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), control input u(t) and time t.

3. 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 prediction value is corrected according to the real-time observation data, the Kalman gain is updated and the state estimation is optimized.

4. The business management method based on the one-stop inspection platform according to claim 1 is characterized in that: 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; It is a cost function related to resource scheduling, which depends on the system state x(t), control input u(t) and 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 strategy, 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.

5. 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; Use optimization methods to solve the optimal resource scheduling strategy to meet business constraints and minimize total cost.

6. The business management method based on the one-stop inspection platform according to claim 1 is characterized in that: The objective function in the stochastic control model is: in, It means 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 disturbance; 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 strategy, 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.

7. 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; 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.

8. The business management method based on the one-stop inspection platform according to claim 7 is characterized in that: 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.

9. 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 8, characterized in that: include: Data collection module, used to collect the platform's order demand data and external environment data; Nonlinear dynamics model module, used to establish a dynamic system model of order demand; The demand forecasting module is 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 optimize according to demand forecast results; Stochastic control module to handle uncertainties 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.

10. The business management system based on the one-stop inspection platform according to claim 9 is characterized in that: The resource scheduling optimization module includes: The optimal control model solving unit 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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