Raw material supply and demand flow direction monitoring system and method based on multi-source data fusion

Abstract

The invention relates to the field of data processing, and discloses a raw material supply and demand flow direction monitoring system and method based on multi-source data fusion, and the system comprises an information collection module, a flow direction prediction module, a monitoring module, a flow direction analysis module and a supply and demand flow direction generation module. Predicting the supply and demand flow direction of the raw materials according to the standardized data to obtain a supply and demand flow direction prediction map; monitoring the transportation flow direction of the raw materials to obtain flow direction data; analyzing the supply and demand flow direction of the raw material according to the flow direction data to obtain a supply and demand flow direction actual diagram; and carrying out feasibility correction on the supply-demand flow direction prediction map to obtain a supply-demand flow direction map, constructing a supply-demand flow direction real-time monitoring model of the raw materials, and carrying out real-time monitoring on the supply-demand flow direction of the multi-source data fused raw materials according to the supply-demand flow direction real-time monitoring model to obtain the supply-demand flow direction. According to the invention, the raw material monitoring capability and the fragmented data processing capability in monitoring are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a raw material supply and demand flow monitoring system and method based on multi-source data fusion. Background Art

[0002] In today's field of raw material supply and demand flow monitoring, the existing monitoring system has exposed many problems, which seriously restricts the monitoring effect and the efficient development of related businesses. The data sources of the existing monitoring system are extremely wide, but the data formats of different sources are very different. Production companies usually use Excel tables to record the output and shipment information of raw materials, and transportation companies use custom database formats to store transportation tracks and cargo loading and unloading data. This format incompatibility makes the data encounter many difficulties in the integration process, greatly aggravating the degree of data fragmentation, making it difficult to build a unified and coherent data set for subsequent analysis.

[0003] The existing monitoring system has data update delays at many supply and demand flow nodes, and cannot reflect the dynamic changes in the supply and demand flow of raw materials in a timely manner. This makes it difficult to respond in a timely manner to emergencies such as traffic congestion and vehicle failure during transportation. At the same time, in terms of inventory monitoring, the inventory data of some warehouses is only updated once or twice a day, which cannot reflect the changes in inventory in and out in real time. When there are abnormal fluctuations in inventory, the company cannot detect and take corresponding measures in time, which seriously affects the supply guarantee capability of raw materials. These problems make the information collected by the raw material supply and demand flow monitoring system fragmented, which in turn leads to weak monitoring capabilities for raw materials. Summary of the invention

[0004] The present invention provides a raw material supply and demand flow monitoring system and method based on multi-source data fusion, the main purpose of which is to solve the problems of fragmented collected information and weak raw material monitoring capabilities.

[0005] To achieve the above-mentioned purpose, the present invention provides a raw material supply and demand flow monitoring system based on multi-source data fusion, characterized in that the system includes an information collection module, a flow prediction module, a monitoring module, a flow analysis module and a supply and demand flow generation module, wherein:

[0006] The information collection module is used to obtain the transportation and logistics data of raw materials;

[0007] The flow direction prediction module is used to clean the traffic and logistics data for noise, obtain standardized data of the traffic and logistics data, and predict the supply and demand flow direction of the raw materials according to the standardized data to obtain a supply and demand flow direction prediction diagram of the raw materials;

[0008] The monitoring module is used to monitor the transportation flow of the raw materials and obtain the flow data of the raw materials;

[0009] The flow direction parsing module is used to parse the supply and demand flow direction of the raw materials according to the flow direction data to obtain an actual supply and demand flow diagram of the raw materials;

[0010] The supply and demand flow generation module is used to make feasibility corrections to the supply and demand flow prediction diagram based on the actual supply and demand flow diagram to obtain the supply and demand flow diagram of the raw materials, build a real-time monitoring model for the supply and demand flow of the raw materials based on the supply and demand flow diagram and the flow data, and monitor the supply and demand flow of raw materials fused with multi-source data in real time based on the real-time monitoring model for supply and demand flow to obtain the supply and demand flow of the raw materials.

[0011] In a preferred embodiment, when the information collection module acquires the transportation and logistics data of raw materials, it is specifically used to:

[0012] The transportation and logistics data on raw materials are collected through the industry data platform, wherein the transportation and logistics data include: manufacturing industry chain flow data, supply chain transportation and logistics data, raw materials flow data and manufacturing product transportation and logistics data.

[0013] In a preferred embodiment, when the flow direction prediction module predicts the supply and demand flow direction of the raw materials according to the standardized data to obtain the supply and demand flow direction prediction diagram of the raw materials, it is specifically used to:

[0014] The standardized data and input weight matrix are input into the sigmoid activation function Perform calculations to obtain the newly added information of the standardized data at the current moment;

[0015] The standardized data and the forgotten weight matrix are input into the sigmoid activation function Perform calculations to obtain the retained information of the standardized data at the previous moment;

[0016] The standardized data and output weight matrix are input into the sigmoid activation function Performing calculations to obtain the information output degree of the standardized data;

[0017] Based on the newly added information at the current moment, the retained information at the previous moment, the information output degree and the hidden state calculation formula, a forecast diagram of the supply and demand flow of the raw materials is obtained, wherein the hidden state calculation formula is:

[0018]

[0019] In the formula, is the prediction graph of the raw material, is the information output level, retaining information for said last moment, Adding new information to the current moment, is the time factor, The symbol for element-by-element multiplication.

[0020] In a preferred embodiment, when the monitoring module monitors the transportation flow of the raw materials and obtains the flow data of the raw materials, it is specifically used to:

[0021] Screening the traffic and logistics data according to their importance to obtain important nodes of the traffic and logistics data, and using the important nodes as supply and demand flow nodes of the raw materials;

[0022] Monitoring the transportation flow of the raw materials at the supply and demand flow direction node to obtain multi-source data of the supply and demand flow direction node;

[0023] Standardizing different formats of the multi-source data to obtain standardized data of the multi-source data;

[0024] Classify the standardized data according to the data importance to obtain classified data of the standardized data, wherein the classified data includes: common data and core data;

[0025] Consensus equity calculation is performed based on the classification data and the warrant dynamic consensus algorithm to obtain the flow data of the raw materials.

[0026] In a preferred embodiment, the consensus equity calculation is performed based on the classification data and the warrant dynamic consensus algorithm to obtain the flow data of the raw materials, including:

[0027] Based on the common data and the warrant dynamic consensus algorithm Layer calculation to obtain the transportation information of the raw materials, where: The layer calculation formula is:

[0028]

[0029] In the formula, For the transportation information, is the ordinary data, It is the time information of supply and demand flow to node data. To ensure the reliability of data flowing to nodes, The supply and demand flow nodes.

[0030] Based on the core data and the warrant dynamic consensus algorithm Layer calculation to obtain the cooperation information of the raw materials, where: The layer calculation formula is:

[0031]

[0032] In the formula, For the cooperation information, For the preparation stage, is the supply and demand flow node, is the serial number of the monitoring data, For the core data, It is a symbol for expressing an ordered tuple;

[0033] The transportation information and the cooperation information are integrated to obtain the flow data of the raw materials.

[0034] In a preferred embodiment, when the flow direction parsing module performs parsing of the supply and demand flow of the raw materials according to the flow direction data to obtain the actual supply and demand flow diagram of the raw materials, it is specifically used to:

[0035] Cleaning the flow direction data of noise points to obtain standard data of the flow direction data;

[0036] Extracting flow supply and demand information of the standard data;

[0037] Standardizing and encoding the flow supply and demand information to obtain the supply and demand flow data of the raw materials;

[0038] An actual diagram of the supply and demand flow of the raw materials is constructed based on the supply and demand flow data.

[0039] In a preferred embodiment, when the supply and demand flow direction generation module performs feasibility correction on the supply and demand flow direction forecast diagram based on the supply and demand flow direction actual diagram to obtain the supply and demand flow direction diagram of the raw materials, it is specifically used to:

[0040] Comparing and identifying the flow information of the supply and demand flow direction forecast map based on the actual supply and demand flow direction map, and obtaining the difference identification points between the actual supply and demand flow direction map and the forecast supply and demand flow direction map;

[0041] The actual supply and demand flow diagram is corrected based on the difference identification points to obtain the supply and demand flow diagram of the raw materials.

[0042] In a preferred embodiment, the supply and demand flow generation module builds a real-time monitoring model for the supply and demand flow of the raw materials according to the supply and demand flow diagram and the flow data, and monitors the supply and demand flow of the raw materials fused with multi-source data in real time according to the real-time monitoring model for the supply and demand flow to obtain the supply and demand flow of the raw materials, specifically:

[0043] Identifying flow characteristics of the raw material flow data to obtain key characteristics of the raw material flow data;

[0044] A real-time monitoring model for the supply and demand flow of the raw materials is constructed based on the key features and the supply and demand flow diagram.

[0045] In a preferred embodiment, when the supply and demand flow generation module performs real-time monitoring of the supply and demand flow of raw materials fused with multi-source data according to the real-time monitoring model of the supply and demand flow to obtain the supply and demand flow of the raw materials, it is specifically used to:

[0046] Constructing a flow network of the raw materials based on the flow data;

[0047] Analyzing the node properties and flow information in the flow network to obtain the supply and demand relationship of the raw materials;

[0048] Based on the supply-demand relationship and the supply-demand flow real-time monitoring model, flow analysis is performed to obtain the supply and demand flow of the raw materials.

[0049] In order to solve the above problems, the present invention also provides a method for monitoring the supply and demand flow of raw materials based on multi-source data fusion, characterized in that the method comprises:

[0050] S1. Obtain transportation and logistics data of raw materials;

[0051] S2, cleaning the traffic and logistics data for noise to obtain standardized data of the traffic and logistics data, and predicting the supply and demand flow of the raw materials according to the standardized data to obtain a supply and demand flow prediction diagram of the raw materials;

[0052] S3, monitoring the transportation flow of the raw materials to obtain the flow data of the raw materials;

[0053] S4. Analyzing the supply and demand flow of the raw materials according to the flow data to obtain an actual supply and demand flow diagram of the raw materials;

[0054] S5. Perform feasibility correction on the supply and demand flow forecast map based on the actual supply and demand flow map to obtain the supply and demand flow map of the raw materials; construct a real-time monitoring model for the supply and demand flow of the raw materials based on the supply and demand flow map and the flow data; and monitor the supply and demand flow of raw materials fused with multi-source data in real time based on the real-time monitoring model for supply and demand flow to obtain the supply and demand flow of the raw materials.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. The information collection module uses advanced collection technology to collect extensive and in-depth data from all links of the manufacturing industry chain, including the transportation routes of raw materials from the place of production to the processing workshop, and the detailed traffic flow of manufactured products from the factory to various markets via various modes of transportation. In the subsequent processing stage, the flow prediction module first performs a detailed cleaning of the massive data collected, and then standardizes the data in accordance with strict industry specifications and standards. The flow analysis module accurately extracts key information. At the same time, through unified data standards, whether it is data from sensors or manually entered data, they can follow the same format specifications, thereby successfully overcoming the problem of data format differences between different data sources and effectively reducing data fragmentation.

[0057] 2. The monitoring module conducts a comprehensive and in-depth importance analysis of traffic and logistics data, uses scientific evaluation methods to accurately determine key nodes, and establishes supply and demand flow nodes based on these nodes. These nodes are like information hubs that can comprehensively collect flow data from different modes of transportation and different time periods, greatly broadening the monitoring scope. At the same time, the supply and demand flow generation module will closely integrate with the actual supply and demand flow diagram, carefully compare and correct the previously generated forecast diagram, and through continuous optimization and adjustment, build a highly accurate real-time monitoring model, thereby enhancing the ability to respond to emergencies and real-time monitoring capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A system architecture diagram of a raw material supply and demand flow monitoring system based on multi-source data fusion provided in one embodiment of the present invention;

[0059] Figure 2 A schematic flow chart of a method for monitoring the supply and demand flow of raw materials based on multi-source data fusion provided in one embodiment of the present invention.

[0060] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0063] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0064] In addition, the sequence of steps in the following method embodiments is only an example and not a strict limitation.

[0065] In fact, the server-side device deployed by the raw material supply and demand flow monitoring system based on multi-source data fusion may be composed of one or more devices. The above-mentioned raw material supply and demand flow monitoring system based on multi-source data fusion can be implemented as: business instance, virtual machine, hardware device. For example, the raw material supply and demand flow monitoring system based on multi-source data fusion can be implemented as a business instance deployed on one or more devices in the cloud node. In simple terms, the raw material supply and demand flow monitoring system based on multi-source data fusion can be understood as a software deployed on the cloud node, which is used to provide a raw material supply and demand flow monitoring system based on multi-source data fusion for each user terminal. Alternatively, the raw material supply and demand flow monitoring system based on multi-source data fusion can also be implemented as a virtual machine deployed on one or more devices in the cloud node. The virtual machine is installed with application software for managing each user terminal. Alternatively, the raw material supply and demand flow monitoring system based on multi-source data fusion can also be implemented as a server consisting of many hardware devices of the same or different types, and one or more hardware devices are set to provide a raw material supply and demand flow monitoring system based on multi-source data fusion for each user terminal.

[0066] In terms of implementation, the raw material supply and demand flow monitoring system based on multi-source data fusion and the user end are adapted to each other. That is, the raw material supply and demand flow monitoring system based on multi-source data fusion is an application installed on the cloud service platform, and the user end is a client that establishes a communication connection with the application; or the raw material supply and demand flow monitoring system based on multi-source data fusion is implemented as a website, and the user end is implemented as a web page; or the raw material supply and demand flow monitoring system based on multi-source data fusion is implemented as a cloud service platform, and the user end is implemented as a small program in the instant messaging application.

[0067] like Figure 1 , which is a system architecture diagram of a raw material supply and demand flow monitoring system based on multi-source data fusion provided by an embodiment of the present invention.

[0068] The raw material supply and demand flow monitoring system 100 based on multi-source data fusion of the present invention can be set in a cloud server. In terms of implementation, it can be used as one or more service devices, or it can be installed as an application on the cloud (such as a server of a mobile service operator, a server cluster, etc.), or it can also be developed as a website. According to the functions implemented, the raw material supply and demand flow monitoring system 100 based on multi-source data fusion can include an information collection module 101, a flow prediction module 102, a monitoring module 103, a flow analysis module 104, and a supply and demand flow generation module 105. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0069] In an embodiment of the present invention, in a raw material supply and demand flow monitoring system based on multi-source data fusion, each of the above modules can be independently implemented and called with other modules. The call here can be understood as a module that can connect to multiple modules of another type and provide corresponding services to the multiple modules connected to it. For example, the sharing evaluation module can call the same information acquisition module to obtain the information collected by the information acquisition module. Based on the above characteristics, in the raw material supply and demand flow monitoring system based on multi-source data fusion provided by an embodiment of the present invention, there is no need to modify the program code. The scope of application of the raw material supply and demand flow monitoring system architecture based on multi-source data fusion can be adjusted by adding modules and directly calling, and cluster-type horizontal expansion can be achieved, so as to achieve the purpose of quickly and flexibly expanding the raw material supply and demand flow monitoring system based on multi-source data fusion. In practical applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in cloud servers.

[0070] The following describes the various components and specific workflows of the raw material supply and demand flow monitoring system based on multi-source data fusion in combination with specific embodiments:

[0071] The information collection module 101 is used to obtain the transportation and logistics data of raw materials.

[0072] In the embodiment of the present invention, when the information collection module is executed to obtain the transportation and logistics data of the raw materials, it is specifically used to:

[0073] The transportation and logistics data on raw materials are collected through the industry data platform, wherein the transportation and logistics data include: manufacturing industry chain flow data, supply chain transportation and logistics data, raw materials flow data and manufacturing product transportation and logistics data.

[0074] Specifically, among the numerous industry data platforms, screening is carried out based on the authority of the data, the comprehensiveness of data coverage, and its relevance to the field of raw materials, transportation and logistics.

[0075] Specifically, check the background information of the platform to understand its reputation in the industry and whether the data source channels are reliable. Evaluate whether the platform has special sections or services for data on the manufacturing industry chain, supply chain, raw material flow, and manufacturing product transportation and logistics.

[0076] For example, some data platforms that focus on the logistics industry may have more detailed data on the transportation link, but insufficient data on the production and supply of raw materials in the manufacturing industry chain; while comprehensive industrial data platforms may cover all aspects, but the depth may vary. By comparing and analyzing multiple platforms, we can determine 2-3 industry data platforms that best meet our needs.

[0077] Specifically, after performing a search operation on the industry data platform, the platform will present relevant data results based on the set search conditions. The data results will be preliminarily reviewed to confirm the completeness and accuracy of the data, and finally the transportation and logistics data of the relevant raw materials will be obtained.

[0078] For example, check whether there are missing values ​​or outliers in the data, and whether the data format meets the requirements for subsequent processing. For data that meets the requirements, select the appropriate data format (such as CSV, Excel and other common formats) for download according to the download function provided by the platform.

[0079] The flow prediction module 102 is used to clean the traffic and logistics data for noise, obtain standardized data of the traffic and logistics data, and predict the supply and demand flow of the raw materials based on the standardized data to obtain a supply and demand flow prediction diagram of the raw materials.

[0080] In an embodiment of the present invention, the standardized data and the input weight matrix are input into a sigmoid activation function. Perform calculations to obtain the newly added information of the standardized data at the current moment;

[0081] The standardized data and the forgotten weight matrix are input into the sigmoid activation function Perform calculations to obtain the retained information of the standardized data at the previous moment;

[0082] The standardized data and output weight matrix are input into the sigmoid activation function Performing calculations to obtain the information output degree of the standardized data;

[0083] Based on the newly added information at the current moment, the retained information at the previous moment, the information output degree and the hidden state calculation formula, a forecast diagram of the supply and demand flow of the raw materials is obtained, wherein the hidden state calculation formula is:

[0084]

[0085] In the formula, is the prediction graph of the raw material, is the information output level, retaining information for said last moment, Adding new information to the current moment, is the time factor, The symbol for element-by-element multiplication.

[0086] Specifically, the newly added information at the current moment is obtained by inputting the standardized data and the input weight matrix in the warrant dynamic consensus platform into the sigmoid activation function It is calculated to reflect the portion of information that has newly entered the system at the current moment and that may have an impact on the flow of supply and demand of raw materials.

[0087] Specifically, the information retained at the last moment is also to input the forgotten weight matrix in the standardized data and the warrant dynamic consensus platform into the sigmoid activation function It represents the part of the data from the previous moment that is selected to be retained and used to participate in the prediction of the supply and demand flow of raw materials at the current moment.

[0088] Specifically, the degree of information output is also obtained by inputting the standardized data and the input weight matrix in the warrant dynamic consensus platform into the sigmoid activation function It is calculated and determines the extent to which the information of the memory cell is output to the hidden state.

[0089] The monitoring module 103 is used to monitor the transportation flow of the raw materials and obtain the flow data of the raw materials.

[0090] In the embodiment of the present invention, when the monitoring module monitors the transportation flow of the raw materials and obtains the flow data of the raw materials, it is specifically used to:

[0091] Screening the traffic and logistics data according to their importance to obtain important nodes of the traffic and logistics data, and using the important nodes as supply and demand flow nodes of the raw materials;

[0092] Monitoring the transportation flow of the raw materials at the supply and demand flow direction node to obtain multi-source data of the supply and demand flow direction node;

[0093] Standardizing different formats of the multi-source data to obtain standardized data of the multi-source data;

[0094] Classify the standardized data according to the data importance to obtain classified data of the standardized data, wherein the classified data includes: common data and core data;

[0095] Consensus equity calculation is performed based on the classification data and the warrant dynamic consensus algorithm to obtain the flow data of the raw materials.

[0096] The consensus equity calculation is performed based on the classification data and the warrant dynamic consensus algorithm to obtain the flow data of the raw materials, including:

[0097] Based on the common data and the warrant dynamic consensus algorithm Layer calculation to obtain the transportation information of the raw materials, where: The layer calculation formula is:

[0098]

[0099] In the formula, For the transportation information, is the ordinary data, It is the time information of supply and demand flow to node data, To ensure the reliability of data flow to nodes, The supply and demand flow nodes.

[0100] Based on the core data and the warrant dynamic consensus algorithm Layer calculation to obtain the cooperation information of the raw materials, where: The layer calculation formula is:

[0101]

[0102] In the formula, For the cooperation information, For the preparation stage, is the supply and demand flow node, is the serial number of the monitoring data, For the core data, It is a symbol for expressing an ordered tuple;

[0103] The transportation information and the cooperation information are integrated to obtain the flow data of the raw materials.

[0104] Specifically, a comprehensive and in-depth importance assessment is conducted on the collected transportation and logistics data, and a scientific importance assessment system is constructed to comprehensively consider multiple factors such as the busyness of the transportation routes involved in the data, the value of the transported goods, and the impact on the overall supply chain stability.

[0105] For example, the transportation route data connecting large-scale raw material production bases and core processing enterprises are given a higher importance score because they carry a large amount of key raw materials and are crucial to the operation of the industrial chain; while some occasionally used branch transportation route data have relatively low importance scores.

[0106] Furthermore, by sorting the scores of each data, the nodes corresponding to the data with higher scores are screened out, and these nodes are identified as the supply and demand flow nodes of raw materials. These nodes play a key hub role in the supply and demand flow process of raw materials.

[0107] Specifically, a variety of advanced monitoring methods and equipment are deployed at the supply and demand flow nodes, and high-precision GPS positioning devices are used to track the location information of transport vehicles at the nodes in real time to ensure that the real-time dynamics of raw material transportation can be accurately grasped; various sensors are installed in the warehouses and loading and unloading areas of the nodes to collect data such as the loading and unloading time, quantity, and status of raw materials.

[0108] Furthermore, by connecting data with the logistics management system of the transportation company, relevant information such as transportation plan and vehicle scheduling can be obtained. The data collected by these monitoring methods from different sources complement each other.

[0109] In general, the multi-source data of supply and demand flow nodes are finally obtained, which fully reflects the transportation flow of raw materials at the supply and demand flow nodes.

[0110] Specifically, in the process of obtaining transportation and logistics data from various sources, there may be deviations in data from different sources. The warrant dynamic consensus algorithm allows all participating nodes to reach a consensus on the data.

[0111] For example, when determining the flow data of raw materials, the data of multiple nodes such as transportation companies, warehousing companies and production companies need to reach a consensus. The algorithm ensures that only data that has been verified to be consistent by multiple nodes is deemed valid, avoiding the mixing of erroneous or false data, improving data reliability, and providing a solid foundation for subsequent analysis and decision-making based on these data.

[0112] Furthermore, the warrant dynamic consensus algorithm uses different computing levels (such as POS layer and PBFT layer) according to the importance of data (such as ordinary data and core data). For ordinary data, the POS layer is used for fast processing, and the equity proof mechanism is used to efficiently calculate the transportation information based on the time information, reliability and other factors of the data;

[0113] Furthermore, for core data, high security and accuracy are ensured through the PBFT layer, and complex calculations are performed to obtain cooperation information.

[0114] In general, this hierarchical processing method improves the overall data processing efficiency and meets the processing requirements of different types of data.

[0115] The flow parsing module 104 is used to parse the supply and demand flow of the raw materials according to the flow data to obtain an actual diagram of the supply and demand flow of the raw materials.

[0116] In the embodiment of the present invention, when the flow direction parsing module performs parsing of the supply and demand flow direction of the raw material according to the flow direction data to obtain the actual supply and demand flow direction diagram of the raw material, it is specifically used to:

[0117] Cleaning the flow direction data of noise points to obtain standard data of the flow direction data;

[0118] Extracting flow supply and demand information of the standard data;

[0119] Standardizing and encoding the flow supply and demand information to obtain the supply and demand flow data of the raw materials;

[0120] An actual diagram of the supply and demand flow of the raw materials is constructed based on the supply and demand flow data.

[0121] Specifically, professional data cleaning algorithms and tools are used to clean the noise points in the collected flow data. By setting a reasonable data threshold range, data points that deviate from the normal range are identified and eliminated, such as abnormal data such as raw material transportation volume that far exceeds the reasonable maximum value of the industry or is negative.

[0122] Furthermore, the continuity of the data is checked, and missing values ​​caused by reasons such as data transmission interruption are processed. Interpolation method, mean filling method and other methods can be used to fill in the missing values ​​to obtain standard data of the flow direction information.

[0123] In general, after this series of cleaning operations, the flow data that may have noise and errors is converted into standard data that meets industry standards, is accurate and coherent, providing a reliable foundation for subsequent data extraction and analysis.

[0124] Specifically, data mining technology and pre-set rules are used to accurately extract information related to the flow of supply and demand from the cleaned standard data. For the transportation data of raw materials, the relationship between transportation direction, transportation volume and supply and demand nodes in each stage is analyzed to extract which regions are receiving raw materials (demand side), which regions are exporting raw materials (supply side) and the corresponding supply and demand quantities, so as to obtain the flow of supply and demand information.

[0125] Furthermore, from the warehouse data, we can explore the changes in inventory in different periods to determine whether it is oversupply that leads to inventory backlogs or strong demand that leads to inventory reduction, so as to sort out the supply and demand dynamics information at each stage. This information is the key to in-depth understanding of the supply and demand flow of raw materials.

[0126] Specifically, the geographic location information of suppliers and demanders is encoded according to specific geographic coding rules, such as using internationally accepted geographic coordinate coding or a regional coding system recognized by the industry.

[0127] Furthermore, for numerical information such as supply and demand quantity and transportation time, the dimensions are unified and converted into a specified digital coding format to obtain supply and demand flow data.

[0128] In general, through this standardized coding operation, the diverse flow supply and demand information is converted into a unified format that is easy for computers to recognize and process, and the supply and demand flow data of raw materials is obtained for further analysis and visualization.

[0129] Specifically, based on the standardized coded supply and demand flow data, different colors, line thicknesses or icons are used on the map to intuitively display the supply and demand locations of raw materials, connect the supply and demand sides with lines and set the line thickness according to the transportation volume, clearly present the transportation path of raw materials, and finally construct the actual supply and demand flow map of raw materials.

[0130] In general, the actual supply and demand flow diagram shows the changes in supply and demand in different time periods through a dynamic timeline, such as the level of inventory, changes in transportation frequency, etc.

[0131] In general, the actual supply and demand flow diagram constructed in this way can present the actual flow status of raw materials in the entire supply chain in an intuitive and visual way, providing intuitive data support for corporate decision-making.

[0132] The supply and demand flow generation module 105 is used to perform feasibility correction on the supply and demand flow prediction diagram based on the actual supply and demand flow diagram to obtain the supply and demand flow diagram of the raw materials, build a real-time monitoring model for the supply and demand flow of the raw materials based on the supply and demand flow diagram and the flow data, and perform real-time monitoring on the supply and demand flow of raw materials fused with multi-source data based on the real-time monitoring model for supply and demand flow to obtain the supply and demand flow of the raw materials.

[0133] In an embodiment of the present invention, when the supply and demand flow direction generation module performs feasibility correction on the supply and demand flow direction forecast diagram based on the supply and demand flow direction actual diagram to obtain the supply and demand flow direction diagram of the raw materials, it is specifically used to:

[0134] Comparing and identifying the flow information of the supply and demand flow direction forecast map based on the actual supply and demand flow direction map, and obtaining the difference identification points between the actual supply and demand flow direction map and the forecast supply and demand flow direction map;

[0135] The actual supply and demand flow diagram is corrected based on the difference identification points to obtain the supply and demand flow diagram of the raw materials.

[0136] The supply and demand flow direction generating module builds a real-time monitoring model for the supply and demand flow direction of the raw materials according to the supply and demand flow direction diagram and the flow direction data, and monitors the supply and demand flow direction of the raw materials fused with multi-source data in real time according to the real-time monitoring model for the supply and demand flow direction to obtain the supply and demand flow direction of the raw materials, specifically:

[0137] Identifying flow characteristics of the raw material flow data to obtain key characteristics of the raw material flow data;

[0138] A real-time monitoring model for the supply and demand flow of the raw materials is constructed based on the key features and the supply and demand flow diagram.

[0139] When the supply and demand flow generation module performs real-time monitoring of the supply and demand flow of raw materials fused with multi-source data according to the supply and demand flow real-time monitoring model to obtain the supply and demand flow of the raw materials, it is specifically used to:

[0140] Constructing a flow network of the raw materials based on the flow data;

[0141] Analyzing the node properties and flow information in the flow network to obtain the supply and demand relationship of the raw materials;

[0142] Based on the supply-demand relationship and the supply-demand flow real-time monitoring model, flow analysis is performed to obtain the supply and demand flow of the raw materials.

[0143] Specifically, the flow information of the actual supply and demand flow map and the supply and demand flow forecast map is compared in detail point by point and path by path.

[0144] Furthermore, for the transportation routes of raw materials in the actual map and the predicted map, check whether their starting points, end points and intermediate key nodes are consistent. If there are position deviations or route inconsistencies, record these difference positions.

[0145] Furthermore, in terms of traffic data, the actual transport volume is compared with the predicted transport volume. When the quantitative difference between the two exceeds a certain threshold, the traffic data point is marked as a difference point. At the same time, attention is paid to the transportation time nodes, such as the difference between the actual transportation arrival time and the predicted time, and points with inconsistent time are also included in the difference identification range.

[0146] In general, through comprehensive comparative analysis, we can accurately identify various differences between the actual supply and demand flow diagram and the predicted diagram, and form a detailed list of difference identification points to provide a basis for subsequent flow path corrections.

[0147] Specifically, according to the difference identification points, the flow path of the actual supply and demand flow map is corrected in a targeted manner. For the difference points where there are deviations in the transportation route, if the actual transportation route shows that a certain raw material goes from place A to place C, while the predicted map shows that it goes from place A to place B, and after verification, the actual route is more accurate, then the transportation route in the actual map is adjusted to place A to place C. For the flow data difference points, if the actual transportation volume is larger than the predicted volume, the flow display on the transportation path is increased accordingly in the actual map.

[0148] Furthermore, for the transportation time difference point, if the actual arrival time is later than the predicted time, the time information in the actual graph is updated, and the possible reasons for the time delay, such as traffic congestion, vehicle failure, etc., are analyzed, and these reasons are recorded as remark information in the actual graph to obtain the supply and demand flow graph of the raw materials.

[0149] In general, by processing each difference point one by one, the flow path of the actual supply and demand flow diagram can more accurately reflect the actual supply and demand flow of raw materials, and finally the revised raw material supply and demand flow diagram is obtained, providing more reliable decision-making support for enterprises.

[0150] Specifically, a comprehensive and in-depth analysis of the acquired raw material flow data is carried out. First, attention is paid to the time series characteristics in the data.

[0151] For example, the time interval between the departure of raw materials from the production site and the arrival at various processing and consumption sites at different time points can be used to identify the pattern of the raw material transportation cycle and determine the peak and trough periods through time series analysis.

[0152] Furthermore, we analyze spatial characteristics, study the geographical routes that raw materials take from their production areas to their consumption areas, identify key transportation nodes and routes, and determine which routes are the main transportation channels and which nodes play a hub role in the transportation process.

[0153] Furthermore, the flow characteristics are mined, and the changes in the transportation volume of raw materials at different stages are statistically analyzed to determine which links have a large amount of raw material flow, as well as the relationship between these flow changes and market demand, seasonal factors, etc.

[0154] In general, through comprehensive analysis of multiple features such as time, space, and flow, key features that can accurately reflect the nature and laws of raw material flow are extracted to form a set of key features, providing core data support for the subsequent construction of a monitoring model.

[0155] Specifically, based on the acquired flow direction data, each transportation starting point, transfer point and end point in the data is set as a network node.

[0156] For example, the origin of raw materials, the logistics hub during transportation, and the final processing plant or sales terminal all become nodes.

[0157] Furthermore, by analyzing the transportation paths and frequencies of raw materials between different nodes in the flow data, a flow network is obtained by connecting each node with directed edges, where the direction of the directed edge represents the flow direction of the raw materials, and the weight of the edge is determined according to factors such as transportation volume or transportation frequency.

[0158] For example, if the amount of raw material transportation on a certain route is large, the weight of the corresponding directed edge is set higher.

[0159] In general, the flow network converts discrete flow data into an intuitive and structured structure, comprehensively displays the flow paths and interrelationships of raw materials throughout the transportation process, and provides a clear framework for subsequent analysis.

[0160] Specifically, an in-depth analysis is conducted on the node properties and flow information in the constructed flow network. From the perspective of node properties, those nodes where raw materials flow in but rarely flow out are most likely on the demand side, such as large processing and manufacturing enterprises, where a large amount of raw materials flow in for production and processing.

[0161] Furthermore, nodes with more raw material outflows but less inflows are usually the supply side, such as raw material mining sites or large-scale production bases. By counting the inflows and outflows of each node, the supply and demand relationship can be further quantified.

[0162] Furthermore, for flow information, observe the flow path and flow changes of raw materials from supply nodes to demand nodes. If a demand node has multiple supply nodes delivering raw materials to it, the supply share of each supply node can be analyzed to clarify the importance of different supply nodes in meeting the demand node. By combining these analysis results, the supply and demand relationship of raw materials can be fully and accurately derived.

[0163] Specifically, the supply and demand relationship of raw materials obtained from the previous analysis is combined with the constructed real-time monitoring model of supply and demand flow, and in-depth flow analysis is carried out. The supply nodes, demand nodes and supply and demand quantities between them in the supply and demand relationship are input into the real-time monitoring model. The existing time, space, flow and other feature analysis modules in the model are used in combination with real-time monitored data, such as the current inventory level of each node, the real-time location and transportation status of the transport vehicles, to dynamically analyze the flow of raw materials in different time periods and different spatial locations, so as to obtain the supply and demand flow of the raw materials.

[0164] For example, the model predicts possible changes in the supply path and supply volume of downstream demand nodes due to an increase in output at a certain supply node within a certain period of time, thereby accurately obtaining the supply and demand flow of raw materials in the entire supply chain, providing accurate decision-making basis for enterprises to rationally arrange production, transportation, inventory and other links.

[0165] Reference Figure 2 FIG. 1 is a flow chart of a method for monitoring the supply and demand flow of raw materials based on multi-source data fusion according to an embodiment of the present invention. In this embodiment, the method for monitoring the supply and demand flow of raw materials based on multi-source data fusion includes:

[0166] Obtain transportation and logistics data of raw materials;

[0167] Cleaning the traffic and logistics data for noise to obtain standardized data of the traffic and logistics data, and predicting the supply and demand flow of the raw materials based on the standardized data to obtain a supply and demand flow prediction diagram of the raw materials;

[0168] Monitoring the transportation flow of the raw materials to obtain flow data of the raw materials;

[0169] Analyzing the supply and demand flow of the raw materials according to the flow data to obtain an actual supply and demand flow diagram of the raw materials;

[0170] Based on the actual supply and demand flow diagram, the supply and demand flow forecast diagram is corrected for feasibility, and the supply and demand flow diagram of the raw materials is obtained. According to the supply and demand flow diagram and the flow data, a real-time monitoring model for the supply and demand flow of the raw materials is constructed. According to the real-time monitoring model for supply and demand flow, the supply and demand flow of raw materials fused with multi-source data is monitored in real time to obtain the supply and demand flow of the raw materials.

[0171] In solving the problem of information fragmentation, the present invention relies on the advantage of multi-source data fusion. The information collection module widely collects data such as the manufacturing industry chain, raw materials and manufacturing product traffic flow. The subsequent flow prediction module cleans and standardizes the data. The flow analysis module extracts key information and integrates it. At the same time, it unifies the data standards, overcomes the problem of data format differences, effectively reduces data fragmentation, and provides a coherent data set for analysis. The monitoring module determines the nodes and establishes supply and demand flow nodes through importance analysis of traffic and logistics data, comprehensively collects flow data, and broadens the monitoring scope. The supply and demand flow generation module corrects the prediction map based on the actual supply and demand flow map, and constructs a real-time monitoring model to timely reflect dynamic changes and enhance the response to emergencies and real-time monitoring capabilities.

[0172] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0173] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0174] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive needs, acquire knowledge and use knowledge to obtain the best results.

[0175] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A raw material supply and demand flow monitoring system based on multi-source data fusion, characterized by: The system includes an information collection module, a flow prediction module, a monitoring module, a flow analysis module, and a supply and demand flow generation module, wherein: The information collection module is used to obtain the transportation and logistics data of raw materials; The flow prediction module is used to clean the traffic and logistics data for noise, obtain standardized data of the traffic and logistics data, and predict the supply and demand flow of the raw materials based on the standardized data to obtain a supply and demand flow prediction diagram of the raw materials; The monitoring module is used to monitor the transportation flow of the raw materials and obtain the flow data of the raw materials; The flow direction parsing module is used to parse the supply and demand flow direction of the raw materials according to the flow direction data to obtain an actual supply and demand flow diagram of the raw materials; The supply and demand flow generation module is used to make feasibility corrections to the supply and demand flow prediction diagram based on the actual supply and demand flow diagram to obtain the supply and demand flow diagram of the raw materials, build a real-time monitoring model for the supply and demand flow of the raw materials based on the supply and demand flow diagram and the flow data, and monitor the supply and demand flow of raw materials fused with multi-source data in real time based on the real-time monitoring model for supply and demand flow to obtain the supply and demand flow of the raw materials.

2. The raw material supply and demand flow monitoring system based on multi-source data fusion as claimed in claim 1, characterized in that: When the information collection module is executed to obtain the transportation and logistics data of raw materials, it is specifically used to: The transportation and logistics data on raw materials are collected through the industry data platform, wherein the transportation and logistics data include: manufacturing industry chain flow data, supply chain transportation and logistics data, raw materials flow data and manufacturing product transportation and logistics data.

3. The raw material supply and demand flow monitoring system based on multi-source data fusion as claimed in claim 1, characterized in that: When the flow direction prediction module predicts the supply and demand flow direction of the raw materials according to the standardized data to obtain the supply and demand flow direction prediction diagram of the raw materials, it is specifically used to: The standardized data and input weight matrix are input into the sigmoid activation function Perform calculations to obtain the newly added information of the standardized data at the current moment; The standardized data and the forgotten weight matrix are input into the sigmoid activation function Perform calculations to obtain the retained information of the standardized data at the previous moment; The standardized data and output weight matrix are input into the sigmoid activation function Performing calculations to obtain the information output degree of the standardized data; Based on the newly added information at the current moment, the retained information at the previous moment, the information output degree and the hidden state calculation formula, a forecast diagram of the supply and demand flow of the raw materials is obtained, wherein the hidden state calculation formula is: In the formula, is the prediction graph of the raw material, is the information output level, retaining information for said last moment, Adding new information to the current moment, is the time factor, The symbol for element-by-element multiplication.

4. The raw material supply and demand flow monitoring system based on multi-source data fusion according to claim 1 is characterized in that: When the monitoring module monitors the transportation flow of the raw materials and obtains the flow data of the raw materials, it is specifically used to: Screening the traffic and logistics data according to their importance to obtain important nodes of the traffic and logistics data, and using the important nodes as supply and demand flow nodes of the raw materials; Monitoring the transportation flow of the raw materials at the supply and demand flow direction node to obtain multi-source data of the supply and demand flow direction node; Standardizing different formats of the multi-source data to obtain standardized data of the multi-source data; Classify the standardized data according to the data importance to obtain classified data of the standardized data, wherein the classified data includes: common data and core data; Consensus equity calculation is performed based on the classification data and the warrant dynamic consensus algorithm to obtain the flow data of the raw materials.

5. The raw material supply and demand flow monitoring system based on multi-source data fusion as claimed in claim 4, characterized in that: The consensus equity calculation is performed based on the classification data and the warrant dynamic consensus algorithm to obtain the flow data of the raw materials, including: Based on the common data and the warrant dynamic consensus algorithm Layer calculation to obtain the transportation information of the raw materials, where: The layer calculation formula is: In the formula, For the transportation information, is the ordinary data, It is the time information of supply and demand flow to node data, To ensure the reliability of data flow to nodes, is the supply and demand flow node; Based on the core data and the warrant dynamic consensus algorithm Layer calculation to obtain the cooperation information of the raw materials, where: The layer calculation formula is: In the formula, For the cooperation information, For the preparation stage, is the supply and demand flow node, is the serial number of the monitoring data, For the core data, It is a representation symbol of an ordered tuple; The transportation information and the cooperation information are integrated to obtain the flow data of the raw materials.

6. The raw material supply and demand flow monitoring system based on multi-source data fusion according to claim 1 is characterized in that: When the flow direction parsing module performs parsing of the supply and demand flow direction of the raw materials according to the flow direction data to obtain the actual supply and demand flow direction diagram of the raw materials, it is specifically used to: Cleaning the flow direction data of noise points to obtain standard data of the flow direction data; Extracting flow supply and demand information of the standard data; Standardizing and encoding the flow supply and demand information to obtain the supply and demand flow data of the raw materials; An actual diagram of the supply and demand flow of the raw materials is constructed based on the supply and demand flow data.

7. The raw material supply and demand flow monitoring system based on multi-source data fusion as claimed in claim 1, characterized in that: When the supply and demand flow direction generation module performs feasibility correction on the supply and demand flow direction forecast diagram based on the supply and demand flow direction actual diagram to obtain the supply and demand flow direction diagram of the raw materials, it is specifically used to: Comparing and identifying the flow information of the supply and demand flow direction forecast map based on the actual supply and demand flow direction map, and obtaining the difference identification points between the actual supply and demand flow direction map and the forecast supply and demand flow direction map; The actual supply and demand flow diagram is corrected based on the difference identification points to obtain the supply and demand flow diagram of the raw materials.

8. The raw material supply and demand flow monitoring system based on multi-source data fusion as claimed in claim 1, characterized in that: The supply and demand flow direction generating module builds a real-time monitoring model for the supply and demand flow direction of the raw materials according to the supply and demand flow direction diagram and the flow direction data, and monitors the supply and demand flow direction of the raw materials fused with multi-source data in real time according to the real-time monitoring model for the supply and demand flow direction to obtain the supply and demand flow direction of the raw materials, specifically: Identifying flow characteristics of the raw material flow data to obtain key characteristics of the raw material flow data; A real-time monitoring model for the supply and demand flow of the raw materials is constructed based on the key features and the supply and demand flow diagram.

9. The raw material supply and demand flow monitoring system based on multi-source data fusion according to claim 1, characterized in that: When the supply and demand flow generation module performs real-time monitoring of the supply and demand flow of raw materials fused with multi-source data according to the supply and demand flow real-time monitoring model to obtain the supply and demand flow of the raw materials, it is specifically used to: Constructing a flow network of the raw materials based on the flow data; Analyzing the node properties and flow information in the flow network to obtain the supply and demand relationship of the raw materials; Based on the supply-demand relationship and the supply-demand flow real-time monitoring model, flow analysis is performed to obtain the supply and demand flow of the raw materials.

10. A method for monitoring the flow of raw material supply and demand based on multi-source data fusion, characterized in that: The method comprises: S1. Obtain transportation and logistics data of raw materials; S2, cleaning the traffic and logistics data for noise to obtain standardized data of the traffic and logistics data, and predicting the supply and demand flow of the raw materials according to the standardized data to obtain a supply and demand flow prediction diagram of the raw materials; S3, monitoring the transportation flow of the raw materials to obtain the flow data of the raw materials; S4. Analyzing the supply and demand flow of the raw materials according to the flow data to obtain an actual supply and demand flow diagram of the raw materials; S5. Perform feasibility correction on the supply and demand flow forecast map based on the actual supply and demand flow map to obtain the supply and demand flow map of the raw materials; construct a real-time monitoring model for the supply and demand flow of the raw materials based on the supply and demand flow map and the flow data; and monitor the supply and demand flow of raw materials fused with multi-source data in real time based on the real-time monitoring model for supply and demand flow to obtain the supply and demand flow of the raw materials.

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