741 results about "Effective management" patented technology

Embodiments described herein provide for a healthcare ecosystem of computing devices participating as nodes of a distributed ledger (e.g., blockchain). The distributed ledger includes NFTs representing corresponding patients, where the initial NET behaves logically as a token scaffold that represents a “digital twin” of the particular patient. The initial patient token represents personal profile data of patient personal data for the patient. The nodes may generate new data and / or new sub-tokens (or other blockchain entry type) representing new data, which the nodes associate with the token scaffold. The sub-tokens contain data for representing physical or digital specimens, health data, and data derived from the health data (e.g., cell lines, organoids, other model systems). As the nodes generate and associate new sub-tokens to the patient's token scaffold over time, the token scaffold graphs and facilitates integration of patient-specific data and evolves into a robust electronic health record (EHR) and digital twin.

The invention provides a northbound mangrove forest population dynamic state, primary productivity and carbon flux evaluation system, and the system comprises a data collection unit which is used for carrying out the data collection of an ecological system of a mangrove forest planting region to be evaluated, and obtaining an original data set; the data analysis unit is used for analyzing and identifying the original data set; the evaluation unit is used for evaluating community development and carbon sink dynamic states and future change trends after mangrove forest north migration by using a mangrove forest community and carbon sink dynamic evaluation model; and the data storage unit is used for storing the original data and the evaluation result. The model is helpful for understanding and predicting the health condition and the carbon sequestration function of a coastal wetland mangrove forest system, and can realize comprehensive evaluation of community development and carbon sequestration capability after mangrove forest north migration and prediction of a future trend. Scientific basis and data support are provided for accurate accounting of coastal wetland protection and restoration after mangrove forest north migration and effective management of blue carbon resources.

The invention relates to the technical field of data analysis, in particular to an intelligent hydrological data analysis method and system, and the method comprises the following steps: collecting water level, flow velocity and precipitation data, calculating the change rate of adjacent moments, marking hydrological parameter abrupt change points, carrying out the statistics of time intervals, predicting the duration of extreme events, and obtaining the distribution characteristics of the hydrological abrupt change points. According to the method, by analyzing the time interval and the duration time of the data points, the distribution characteristics of hydrological abrupt change can be mastered in the early stage, response can be made in advance, the propagation sequence and the diffusion rate of the abrupt change points in different river reaches can be analyzed, and accurate division of the influence range and the risk level of hydrological events is enhanced; effective management of hydrological event space risks is achieved, understanding of hydrological key influence factors is deepened by screening key variables and evaluating the evolution relation of the variables along with time, accuracy of short-term and long-term hydrological risk prediction is enhanced, and a scientific basis is provided for dynamic early warning of hydrological abnormal events.

The invention relates to the technical field of cloud computing application, and discloses a cloud computing-based shale fracture modeling platform, which comprises a multi-source data acquisition module, a heterogeneous data fusion module, a fracture dynamic modeling module, a parameter optimization module and a cloud verification module. The multi-source data acquisition module acquires various data and uploads the data to the cloud; the heterogeneous data fusion module processes data isomerism to generate a fusion feature matrix; the crack dynamic modeling module constructs a three-dimensional dynamic model; the parameter optimization module optimizes physical property parameters; and the cloud verification module verifies the model in multiple dimensions and manages the version. The platform solves the problems of traditional modeling in the aspects of data acquisition, fusion, modeling algorithm, parameter optimization and model verification by means of the advantages of cloud computing, can efficiently process multi-source data, accurately construct a crack model, improve the precision and reliability of the model, realize comprehensive verification and effective management of the model, and provide powerful support for efficient exploitation of shale gas.

The invention relates to the technical field of equipment access control, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-user access control method and device of equipment, the equipment and a medium, and the method comprises the following steps: receiving a scanning result containing an equipment identity code and operator identification information; generating a verification token based on a scanning result, embedding a time efficiency parameter, distributing the verification token to a user terminal, receiving user characteristic data, establishing a binding relationship with the equipment identity code and the operator identification information, carrying out time efficiency and integrity verification, generating a verification result, and controlling the equipment access authority and generating an operation record according to the verification result. Through a binding mechanism among the equipment identity code, the operator identification information and the user characteristic data, and in combination with the verification token embedded with the aging control parameter, effective management and control of the operation access behavior under the scene that multiple persons use the same equipment at the same time are realized.

According to the welding process parameter recommendation method using the knowledge graph to retrieve the enhanced large model, the welding process knowledge graph is constructed, multi-source heterogeneous welding knowledge is stored in the graph, and effective management and reuse of the welding knowledge are achieved; meanwhile, through user feedback and verification, the welding process knowledge graph can be continuously updated and perfected, and continuous accumulation and iterative optimization of knowledge are achieved; the welding process knowledge graph is used as an external knowledge base to retrieve and enhance the large model, and accurate and comprehensive welding process related knowledge can be provided for the large model to serve as a reasoning basis, so that the illusion problem of the large model is effectively relieved; according to the method, intelligent recommendation of the welding process parameters is realized, personalized analysis can be carried out according to specific requirements of the user, welding process parameter suggestions meeting user expectations are quickly generated, the efficiency of welding process parameter recommendation is effectively improved, and the problems of strong expert experience dependence and relatively high cost in process parameter recommendation are solved.

The invention discloses an excess fusion computing power scheduling system based on a directed acyclic graph and a task processing method. The excess fusion computing power scheduling system comprises a task splitting module, an execution graph generation module and a quantum-classical computing power resource module. The task splitting module is configured to split an input calculation task and determine a plurality of task operators, and the task operators comprise quantum task operators and classic task operators. The execution graph generation module is configured to determine an execution graph based on the directed acyclic graph according to a dependency relationship among the plurality of task operators. And the quantum-classical computing power resource module is configured to determine the task computing power resource allocated to each task operator according to the execution graph so as to process the computing task. Therefore, through the over-fusion computing power scheduling system based on the directed acyclic graph, effective management and scheduling of quantum computing resources and classical computing resources can be realized, the processing time of the computing task is shortened, and the processing efficiency of the computing task and the system performance are improved.

The invention discloses a dynamic allocation management system for monitoring computing power resources in real time, relates to the technical field of computing power resource management, and is used for solving the problem that an existing dynamic allocation management system for the computing power resources cannot efficiently match, optimally utilize, accurately evaluate and dynamically allocate heterogeneous computing power resources. Comprising a central cloud and a plurality of edge nodes in communication connection with the central cloud, and a real-time monitoring prediction unit, a dynamic distribution management unit and a fault detection processing unit are arranged in the central cloud; according to the method, the utilization rate and delay information of the heterogeneous computing power resources are analyzed in detail, so that the resources most suitable for the target processing task can be efficiently matched; by eliminating the computing power resource with a relatively low delay value, the utilization efficiency of the whole resource is improved, and resource waste is avoided; and resource demand prediction is performed in combination with a plurality of parameters, so that the performance and value of the computing power resources to be allocated can be evaluated more accurately. And effective management and optimal utilization of heterogeneous computing power resources are realized.

The invention relates to the technical field of digital twinning, and particularly provides an airport flight area risk management method and system, a terminal and a storage medium, and the method comprises the steps: constructing a three-dimensional model of an airport, and setting a management and control area in the three-dimensional model; collecting monitoring data of an airport, identifying a moving target and a movement track of the moving target from the monitoring data, and generating intrusion early warning information if the movement track intersects with the management and control area; aircraft demand information is obtained, the demand information comprises take-off demand information and landing demand information, and a to-be-executed task is generated based on the demand information; collecting meteorological data through a meteorological sensor; and distributing a take-off and landing runway for the task to be executed based on the meteorological data and the intrusion early warning information. According to the invention, the digital twinborn model is constructed and the environment data of the airport flight area is monitored, so that the illegal invasion of the flight area is early warned in time, and the runway is reasonably distributed based on the illegal invasion early warning, so that the flight safety is effectively managed and controlled.

The invention relates to the technical field of data processing and big data, in particular to a new-generation fruit juice production line automatic control and information management system, which comprises a control module, a data acquisition module, a data analysis processing module and a man-machine interaction module, the analysis module processes data through the steps of cleaning, standardization and the like, short-term and long-term dynamic threshold values are set according to a sliding window technology, early warning is triggered through real-time comparison, the control module adjusts equipment according to the early warning, and the man-machine interaction module is used for management personnel to operate and check information; the objective of the invention is to solve the problems that fruit juice production line equipment control is not intelligent, production data cannot be effectively managed and product quality is difficult to monitor and early warn in time.

The invention provides a self-adaptive power supply management and control method and device for a self-powered intelligent door lock, and relates to the technical field of door lock power supply management and control. Performing classification identification and training fusion on the data set by using a recurrent neural network, and constructing an electric quantity consumption adaptive prediction network; acquiring the door lock electric quantity and the working state to perform electric quantity consumption prediction in a preset time window; constructing a power supply control strategy library, matching with the strategy library based on the electric quantity prediction consumption parameter and the current electric quantity state, and determining a power supply control strategy; the door lock electric quantity prediction consumption parameters are optimized and analyzed, door lock power supply strategy control parameters are obtained, self-adaptive power supply management and control are conducted on the door lock, the technical problems that an existing self-powered intelligent door lock is unstable in electric quantity and difficult to accurately predict and effectively manage are solved, efficient utilization and intelligent scheduling of the electric quantity of the door lock are achieved, and the safety of the door lock is improved. And the autonomous power supply capability and the operation efficiency of the door lock system are improved.

Disclosed are systems and methods for effectively managing user access rights, including processing and revalidating user access requests. A computer-implemented method determines an entitlement risk score, a user risk score, and an overall risk score associated with a user access request based on criticality models and metadata. Based on the overall risk score, a criticality level is assigned to the user access request, and the computer-implemented method processes the user access request. The user access request is either automatically or manually processed based on the assigned criticality level. A plurality of user access requests requiring manual processing are processed based on an external input, and the plurality of user access requests are prioritized based on their respective assigned criticality levels and processing histories.

The invention discloses a multi-source threat intelligence privacy fusion processing method and system, and relates to the technical field of network security threat intelligence analysis and processing, and the method comprises the steps: collecting threat intelligence, and carrying out the cleaning, standardization and desensitization; establishing a threat index semantic model and aligning cross-modal features; the central coordination module completes model aggregation after organization node local training; and based on the aggregation model fusion intelligence, semantic association and attack link reconstruction are established, and a result is generated and fed back for optimization. The technical problems that in cross-organization fusion processing of multi-source heterogeneous threat intelligence, data formats are not uniform, and privacy protection and intelligence collaborative analysis contradictions exist, so that threat detection is not comprehensive and inaccurate, and accurate evaluation and effective management and control requirements are difficult to meet are solved. The technical effects that the multi-source heterogeneous threat intelligence is effectively fused on the premise of privacy protection, the comprehensiveness and accuracy of threat detection are improved, and the requirements for accurate assessment and effective management and control of threats in a cross-organization scene are met are achieved.

The invention discloses a BOM reconstruction method for large equipment manufacturing, and relates to the field of digitization. The BOM reconstruction method for large equipment manufacturing comprises the following steps: step 1, enterprise evaluation: according to the data size of enterprise development equipment, the enterprise scale, the enterprise supply management mode and the project division characteristics, evaluating an enterprise; and 2, according to the characteristics of different enterprises, dividing the construction paths into an EBOM-PBOM-MBOM construction path, an EBOM-MBOM construction path and an EBOM-PBOM construction path. According to the large-scale equipment manufacturing BOM reconstruction method, each enterprise plans and manufactures BOM according to the characteristics of developed product data size, enterprise scale, enterprise supply management mode and project division, and effective management of manufacturing end process data is realized by an optimal, most convenient and most economical process configuration management scheme; by taking effective management of product development process data as a starting point, a reconstruction mode and architecture of PBOM and MBON are planned.

The invention discloses an Internet of Things equipment power consumption management method and system based on big data, and relates to the technical field of equipment power consumption management. The Internet of Things equipment power consumption management method and system based on big data specifically comprises the following steps: S1, equipment classification; s2, power consumption evaluation and modeling; s3, implementing a dormancy awakening strategy; s4, strategy design and dynamic adjustment; through careful classification of the data center server and the Internet of Things equipment, power consumption characteristics of various types of equipment can be more accurately understood and managed. And secondly, through power consumption evaluation and modeling, an accurate power consumption model is established, and a power consumption threshold value is set, so that real-time monitoring and effective management of power consumption are realized. In addition, the implementation of the sleep wake-up strategy avoids invalid sleep of the Internet of Things equipment, and improves the energy utilization efficiency. The dynamic adjustment strategy not only realizes load balancing, but also dynamically adjusts the power consumption according to the task priority and the load condition, so that the overall power consumption is further reduced.

The invention discloses an Internet of Things equipment energy consumption optimization method based on artificial intelligence, and the method comprises the steps: S1, integrating the data of Internet of Things equipment, and forming a multi-dimensional data fusion set through employing a multi-source data fusion technology; s2, dynamically establishing an energy consumption baseline model by using an adaptive regression algorithm; s3, monitoring fluctuation deviation of the energy consumption equipment through a fluctuation identification technology, quickly positioning an energy consumption abnormal position by using an energy consumption detection mechanism, and diagnosing an energy consumption abnormal reason; s4, transmitting the energy consumption data and the user behavior data into an intelligent energy consumption scheduling system to optimize task scheduling of the Internet of Things equipment, and reducing an energy consumption peak value; and S5, constructing an energy consumption peak optimization mechanism, optimizing the weight and the threshold value of the Internet of Things, and realizing continuous energy consumption reduction. Through multi-source data fusion, dynamic energy consumption baseline modeling, energy consumption anomaly detection and diagnosis, intelligent task scheduling and a continuous optimization mechanism, effective management and reduction of energy consumption of the Internet of Things equipment are realized, and the overall energy efficiency of the system and the user experience are improved.

The invention belongs to the technical field of block chains and federated learning, and particularly discloses a dynamic incentive federated learning method based on a trusted execution environment and a block chain. The method comprises the following steps: firstly, constructing a core computing security area by utilizing TEE, and executing key links such as model aggregation, client screening and contribution measurement in a hardware isolated trusted environment; and then, a block chain and an intelligent contract technology are adopted as a decentralized trust root to realize effective management of identities of participants, tamper-proof records of key certificates and automatic distribution of economic incentives. Secondly, establishing a set of dynamic excitation and reputation mechanism executed in the TEE, and performing credible quantification and automatic reward on the contribution of the client based on multi-dimensional indexes such as model quality, historical reputation, asset pledge, participation stability and the like; and finally, end-to-end data encryption is realized through a session key mechanism in the TEE, so that the data privacy of the client is effectively protected.

The invention relates to a heterogeneous computing power fused cloud computing platform, a resource scheduling method and device, computer equipment and a computer readable storage medium. The cloud computing platform comprises a heterogeneous node management module used for managing hardware resources of different architectures to a computing power cluster; the heterogeneous computing power scheduling module is used for receiving a computing power calling request sent by a user, the computing power calling request carries resource type demand information and architecture demand information, and a computing power resource creation instruction is generated according to the resource type demand information and the architecture demand information; the cloud computing platform can automatically select appropriate hardware resources according to the computing power resource creation instruction, and the task is completed through a container or a virtual machine. By the adoption of the method and device, effective management and scheduling of heterogeneous hardware resources can be achieved through cooperation of the heterogeneous node management module, the heterogeneous computing power scheduling module, the container management module and the virtualization management module, and the utilization rate of computing resources is increased.

The invention discloses an active management and control method and device for high-risk operation, electronic equipment and a storage medium. The method comprises the following steps: enabling operators related to high-risk operation to carry out operation according to related requirements of construction safeguard measures and risk processing plans in a mode of automatically acquiring videos or images and an active management mode, and carrying out biological feature recognition and identity recognition on the operators so as to ensure that the operators can carry out risk processing according to the related requirements of the construction safeguard measures and the risk processing plans; the system can more intuitively assist enterprise safety management personnel to comprehensively master the safety condition of a working site, form an effective management means, supervise and urge management and execution personnel at all levels to consciously practice three-notice management, namely the management principle of site, notice and reality, and actively go deep into the site, so that the safety management efficiency is improved to the greatest extent, and the safety management efficiency is improved. The safety of a high-risk operation site and the periphery is strictly ensured, the effectiveness of safety management is improved, and a better operation environment is provided for operators.

The invention belongs to the technical field of power system data analysis, and relates to a power utilization habit-based power utilization abnormal behavior real-time analysis method and system, and the method comprises the steps: obtaining a historical power utilization data sample of a charging pile user, and carrying out the calculation to obtain a power utilization behavior habit interval; acquiring real-time power consumption data of the charging pile user according to a preset period, judging whether the real-time power consumption data is in the power consumption behavior habit interval, and if so, not giving an alarm; otherwise, giving an alarm; the advantages of wide coverage, high timeliness and high accuracy of power data are utilized, and the power utilization habit of the user is obtained through data analysis. Real-time power utilization data and historical power utilization habits are compared, abnormal power utilization behaviors are recognized, abnormal power utilization alarms are given out, and a charging pile operator is assisted in effective management, prevention and control; maintenance and upgrade plans of the charging piles are reasonably arranged, and usability of the charging piles is ensured. An electric power company can adjust an electric power supply strategy in advance, optimizes a power grid load, and avoids overlarge power grid pressure caused by a charging peak.

The invention provides a task scheduling execution method and system based on a hybrid computing environment. The task scheduling execution method comprises the steps of receiving a task, performing task type division and quantum coding on the task to form quantum coding information for representing the task, and determining an initial quantum state of the task; evaluating the quantum enhancement priority of the task based on the quantum coding information, and converting a quantum enhancement priority evaluation result of the task into task priority information for a classical task scheduling execution method; and scheduling and executing the task based on the task priority information. According to the method, the dual advantages of fusion quantum computing and classical computing are utilized, effective management of complex tasks is achieved, the isomerized computing environment and task requirements are met, the problem of low-efficiency transfer caused by insufficient integration in the current hybrid computing environment is solved, and the efficiency is improved. And the comprehensive performance, the resource use efficiency and the task scheduling capability of the hybrid computing environment are effectively improved.

According to the Linux real-time enhancement method based on the domestic real-time kernel, by introducing a kernel adaptation layer and designing a packaging mechanism of the kernel adaptation layer, it is ensured that code space between the real-time kernel and the Linux kernel is effectively isolated, the code coupling degree is reduced, the kernel and the Linux kernel can harmoniously coexist in the same environment and do not interfere with each other, and the real-time performance of the Linux kernel is improved. The real-time task can be called by a non-real-time system and inherits the existing functions and services of the Linux, so that the ecological advantages of the Linux are brought into full play, effective management of hardware and memory resources is ensured through flexible and diversified resource allocation and sharing strategies, and the problems of resource contention and performance reduction are reduced.

The invention relates to the technical field of power systems, and discloses a management and control system for effectively managing and controlling distributed power supply loads based on power grid automation equipment. The system comprises an acquisition module for acquiring dynamic load parameters of a distributed power supply and topological information of a target power grid; the division module extracts load sudden change characteristics and divides the power grid into coordinated regulation and control partitions in combination with topological information; the injection module injects a preset disturbance signal to a time delay sensitive area node in the partition to obtain voltage offset distribution; the positioning module is used for positioning electrical weak nodes according to the oscillation attenuation rate of the electrical nodes and topological connection distribution; a verification module performs gridding ratio verification on the weak nodes and the coordination regulation and control partition to obtain a dynamic boundary constraint condition; an output module reconstructs a distributed power supply power output priority instruction based on the condition. According to the invention, the problems of lagging response and insufficient precision of power grid management and control under large-scale access of the distributed power supply are solved.

The invention relates to the technical field of industrial process control, and discloses a digital twinning-based fermentation process temperature adaptive control system, which comprises a data acquisition and state representation module, a probability generation type digital twinning module, an adaptive risk management module, an opportunity constraint model prediction control module and a model iteration trigger module, according to the method, probability distribution of future states is predicted by constructing probability generation type digital twinning, and the uncertainty of the model is quantified in real time; the system can dynamically adjust the risk tolerance, drives the chance constraint model prediction controller, seeks an optimal control solution on the premise of ensuring the process safety probability, triggers the model adaptive update by means of an online evaluation mechanism, and realizes the optimal control solution on the basis of effectively managing the process uncertainty. The fermentation temperature is subjected to high-performance self-adaptive control considering safety and economy, and the problem that a traditional deterministic model is insufficient in control robustness is solved.

The invention discloses a BIM construction progress dynamic tracking method based on multi-modal data fusion, and the method comprises the steps: obtaining BIM data, component progress data and visual data of each building component in a construction region in real time, and carrying out the unified space-time reference and data cleaning processing of the component progress data and the visual data; performing feature extraction on the processed data and the BIM data to obtain a standardized spatial-temporal feature data set, constructing a construction process digital twin map according to the data set, performing spatial-temporal convolutional network processing to obtain a component construction progress state reasoning result of each building component, setting a threshold value, and obtaining a construction progress state reasoning result of each building component; and comparing the construction progress state reasoning result to obtain a component risk assessment result, taking measures according to the risk assessment result, executing the measures, and simultaneously obtaining the executed multi-modal data to update the digital twin map. According to the invention, through multi-modal data fusion, dynamic tracking and effective management of the construction progress are realized, the construction risk can be found and processed in time, and the construction management efficiency and accuracy are improved.

The invention discloses an LCD display screen production quality collaborative management and control method based on a detection data chain, particularly relates to the technical field of industrial production quality management, and is used for solving the problem of quality root cause misjudgment caused by complex association of multi-source data in the existing liquid crystal display screen manufacturing process. The method comprises the following steps of: constructing a detection data chain covering an array, a box forming process and a module process, firstly performing multi-source data correlation analysis to obtain a variable pair set, then analyzing an intermediary influence path between variable pairs to eliminate false correlation, and then determining a real causal direction by applying a causal discovery algorithm based on conditional independence. Carrying out unsupervised clustering analysis on the basis of defect characteristic indexes, verifying the consistency of causal directions in different data subsets, and finally outputting a stable quality root cause analysis result to realize accurate positioning and effective management and control of key process parameters.

The invention discloses a tea tree germplasm resource garden management method, which adopts a tea tree germplasm resource garden management system consisting of a data storage module, a data processing module, a system operation module, a system display module and an external port module. Through overall plane data and block distribution data of a tea tree germplasm resource garden, meteorological station and soil moisture content information data, basic information data of tea tree germplasm resources entering the garden, germplasm resource agronomic characteristic original data and data processing, agronomic character mean values of vigorous tea tree germplasm resource states in the fourth, fifth and sixth years are converted into standard values; and comparing agronomic character data of the seventh year with a standard value, carrying out fixed-period tea tree data acquisition, and combining a scientific management mode of resource nursery soil and climate factors to realize effective management of dynamic growth of resources of the resource nursery.

The invention relates to the technical field of network security equipment, and discloses a network security equipment adaptive optimization method based on rule analysis, which comprises the following steps: acquiring a historical feedback behavior mode of network security equipment, and dividing the historical feedback behavior mode into a historical security feedback behavior mode and a historical abnormal feedback behavior mode; determining a security feedback rule data set and an exception feedback rule data set, calculating an initial historical security processing factor and an initial historical exception processing factor, performing normalization processing, constructing an initial processing factor set, splitting the initial processing factor set into a first initial processing factor set and a second initial processing factor set, calculating a historical security event processing coefficient, and calculating a second historical security event processing coefficient; the historical security event processing coefficient of each network security device is extracted, the multiple network security devices are adaptively optimized, the network security devices with poor protection effects are removed, effective management and coordination are realized, the protection function of each network security device is fully played, the efficiency and effect of network security protection are improved, and the network security protection method is suitable for the network security devices. And safe and stable operation of a network system is ensured.

The invention relates to a data construction task planning method based on a knowledge graph, and belongs to the technical field of data construction task planning in artificial intelligence. According to the method, the knowledge graph is constructed by adopting an ontology technology of the knowledge graph, and knowledge reasoning iteration is performed on the constructed knowledge graph to realize enhancement of the knowledge graph; then determining a data resource construction demand by using the enhanced knowledge graph, and identifying a specific collection object of the data resource construction collection demand; and determining a target data construction task according to the specific collection object, issuing the target data construction task to a corresponding operation unit, and completing planning of the data resource construction task after collection and reporting. Therefore, the isolated data are associated in a knowledge graph mode, the data are effectively managed and efficiently utilized, deep-level target information and features are mined, and support is provided for related requirements of task planning.

The invention relates to the field of order risk prevention and control, and particularly discloses an order risk prevention and control system based on dealer big data, which comprises a completed order risk management module used for performing data acquisition on completed orders of dealers, evaluating risk management values corresponding to the completed orders of the dealers, and sending the risk management values to the dealers; therefore, the risk prevention and control threshold value corresponding to the completed order of the dealer is screened out, and a data support basis is provided for the subsequent analysis of the risk prevention and control condition of the uncompleted order. The uncompleted order risk prevention and control module is used for acquiring risk management data corresponding to uncompleted orders of the sales enterprises, monitoring logistics management conditions of the orders in real time, judging risk prevention and control values corresponding to the uncompleted orders of the sales enterprises, and providing powerful support for optimizing order risk management and improving risk response capability; and the order risk prevention and control early warning module is used for carrying out risk prevention and control early warning on the uncompleted orders of the dealers, and can continuously carry out effective management on order risk prevention and control.