Intelligent platform system for modern industrial system construction

Abstract

The invention discloses an intelligent platform system for modern industrial system construction, and the system specifically comprises a heterogeneous data fusion center which is used for collecting and standardizing the multi-source heterogeneous data of enterprise equipment and environment in an industrial chain in real time; the knowledge graph construction module is used for generating a real-time operation situation graph according to the multi-source heterogeneous data; the AI decision engine is used for executing risk prediction and resource optimization path planning according to the real-time operation situation map; the intelligent dispatching center is used for dynamically reconfiguring idle equipment, talents and funds of an industrial chain according to resource optimization path planning; and the ecological value contract module is used for calling a block chain to convert the data assets into on-chain verifiable collaborative benefits according to the reconfiguration result of the intelligent dispatching center. According to the invention, industrial chain data fusion, situation awareness, intelligent decision, resource dynamic configuration and data asset collaborative income conversion are realized, and the overall operation efficiency and collaborative value of an industrial system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital economy and industrial intelligence, and particularly relates to a wisdom platform system for modern industrial system construction. BACKGROUND

[0002] In the development process of traditional industrial parks, there are many technical problems to be solved, which seriously restrict the efficient operation and sustainable development of the industry. The specific manifestations are as follows:

[0003] 1. In the traditional industrial park, the data among enterprises, park management and government departments are in a relatively closed state, and cannot be effectively connected. Each subject independently stores and manages its own data, and lacks unified data interaction standards and sharing mechanisms. This data island phenomenon makes it impossible for all parties to obtain comprehensive and timely information in the decision-making process, leading to delayed decision-making. For example, when the park management makes industrial layout adjustment or policy formulation, it is difficult to make scientific and reasonable decisions due to the inability to obtain real-time production data, market dynamic data and other information of enterprises in a timely manner, thereby affecting the industrial coordination development efficiency of the entire park.

[0004] 2. The resource mismatch phenomenon in the industrial park is serious, and the idle rate of equipment is generally more than 40%. A large number of equipment is in idle state and cannot fully exert its production efficiency. At the same time, the flow efficiency of key production factors such as talents and funds is low, and talents cannot play their professional skills in suitable positions, and funds cannot accurately flow to enterprises and projects in need. This not only causes waste of resources, but also limits the development potential of enterprises and reduces the economic benefits of the entire industrial park.

[0005] 3. The coordination rate between upstream and downstream enterprises is less than 30%, and there is a lack of effective information sharing and collaborative mechanism among enterprises. In the production process, there are problems such as delayed supply of raw materials and mismatched production progress, which leads to the breakage of the industrial chain. This breakage not only affects the normal production and operation of enterprises, but also reduces the stability and competitiveness of the entire industrial chain, making it difficult to form a complete industrial ecological system.

[0006] 4. Enterprises face many challenges in the process of digital transformation, and the success rate of digital transformation is less than 50%, and the cost recovery period is long. On the one hand, enterprises lack professional digital technology talents and implementation experience, making it difficult to effectively promote digital transformation projects; on the other hand, digital transformation requires a large amount of investment in funds and resources, and enterprises often face a long cost recovery period after investment, increasing the operating risk of enterprises. This makes many enterprises cautious about digital transformation, further hindering the overall digital transformation process of the industrial park.

[0007] 5、Traditional industrial park mode faces great difficulties in attracting investment, and it is difficult to attract high-quality enterprises and industries. Due to the lack of effective industrial ecological construction and collaborative development mechanism, the attraction of the park to high-quality enterprises is insufficient. At the same time, the traditional investment mode often focuses on providing land, tax incentives and other preferential policies, and ignores the core elements such as industrial matching and technological innovation, resulting in a mismatch between the enterprises attracted and the industrial positioning of the park, and it is difficult to form an industrial agglomeration effect.

[0008] At present, the existing technologies such as ERP (Enterprise Resource Planning) and MES (Manufacturing Execution System) mainly focus on the internal process management of a single enterprise. Although they have played a certain role in improving the internal management efficiency of enterprises, they lack cross-enterprise and cross-industry collaboration capabilities. These systems cannot realize real-time sharing and interaction of data between different enterprises, and cannot meet the needs of upstream and downstream collaboration and resource optimization in the process of building a modern industrial system. They cannot fundamentally solve the above problems faced by traditional industrial parks.

[0009] In summary, traditional industrial parks have many technical defects in data sharing, resource allocation, industrial chain collaboration, enterprise transformation, and investment attraction. The existing technologies cannot meet the needs of building a modern industrial system. SUMMARY

[0010] The purpose of the present application is to provide a smart platform system for modern industrial system construction, which realizes industrial chain data fusion, situation awareness, intelligent decision-making, resource dynamic allocation and data asset collaborative income transformation, and improves the overall operation efficiency and collaborative value of the industrial system, to solve at least one of the above existing technical problems.

[0011] The present application discloses a smart platform system for modern industrial system construction, which specifically comprises:

[0012] A heterogeneous data fusion hub for real-time acquisition and standardized processing of multi-source heterogeneous data of enterprise equipment and environment in the industrial chain;

[0013] A knowledge graph construction module for generating a cross-enterprise and cross-industry real-time operation situation graph based on multi-source heterogeneous data;

[0014] An AI decision engine for performing risk prediction and resource optimization path planning based on the real-time operation situation graph;

[0015] An intelligent dispatching center for dynamically reconfiguring idle equipment, talents and funds in the industrial chain based on the resource optimization path planning;

[0016] An ecological value contract module for converting data assets into verifiable collaborative income on the chain by calling the blockchain based on the reconfiguration results of the intelligent dispatching center.

[0017] Compared with the prior art, the present application has at least one of the following technical effects:

[0018] 1. The present application realizes industry chain data fusion, situation awareness, intelligent decision-making, resource dynamic configuration and data asset collaborative benefit transformation, and improves the overall operation efficiency and collaborative value of the industry system.

[0019] 2. The present application constructs a perfect industry ontology knowledge graph, ensures accurate data semantic mapping, and provides reliable support for operation situation graph generation.

[0020] 3. The present application intuitively presents the real-time operation situation across enterprises and industries, and facilitates quick grasp of the overall operation status of the industry chain.

[0021] 4. The present application accurately predicts risks and plans resource optimization paths, providing decision-making basis for stable operation of the industry chain and rational allocation of resources.

[0022] 5. The present application quickly generates comprehensive and accurate risk prediction results by parallel processing of multi-dimensional risk prediction tasks.

[0023] 6. The present application efficiently generates a set of Pareto optimal solutions for resource optimization, taking into account multiple objectives, and provides an optimal solution for rational allocation of resources.

[0024] 7. The present application reasonably solves the conflict of solution set, outputs scientific and executable instructions, and ensures the implementation of decision-making.

[0025] 8. The present application realizes dynamic and accurate reconfiguration of idle resources in the industry chain, improves resource utilization efficiency, and promotes the coordinated development of industries.

[0026] 9. The present application uses blockchain to convert data assets into verifiable collaborative benefits, protects the rights and interests of all parties, and promotes the virtuous cycle of the industry ecosystem. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0028] Figure 1 is a structural schematic diagram of a smart platform system for modern industry system construction provided by an embodiment of the present application. DETAILED DESCRIPTION

[0029] In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular architectures, technologies, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0030] It is to be understood that the terminology "includes", "has", "holds", "contains" and / or "comprising", "including", "having" and their conjugates used in the application and the appended claims mean "including but not limited to", and are not intended to exclude or to otherwise discourage the presence of one or more other features, integers, steps, operations, elements, and / or groups thereof.

[0031] It is also to be understood that the terminology "and / or" used in the application and the appended claims means any one and / or any combination of the associated listed items and includes all possible combinations.

[0032] As used in the description of the application and the appended claims, the term "if" can be interpreted to mean "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [a described condition or event] is detected" can be interpreted to mean "upon determining" or "in response to determining" or "upon [the described condition or event] being detected" or "in response to [the described condition or event] being detected", depending on the context.

[0033] In addition, in the description of the application and the appended claims, the terms "first", "second", "third", etc. are used merely as labels for convenience, and are not intended to imply or suggest relative importance to one another.

[0034] Reference in the specification to "one embodiment" or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase "in one embodiment" or "in some embodiments" in various places in the specification are not necessarily all referring to the same embodiment, although it can. The terms "including", "containing", "having" and variations thereof are meant to encompass the terms "including but not limited to".

[0035] Figure 1 The structural schematic diagram of the smart platform system 1 for modern industrial system construction disclosed by an embodiment of the application is shown, and is described in detail as follows:

[0036] a heterogeneous data fusion hub S101 for collecting and standardizing the multi-source heterogeneous data of enterprise equipment and environment in the industrial chain in real time;

[0037] a knowledge graph construction module S102 for generating a real-time operation situation graph across enterprises and industries according to the multi-source heterogeneous data;

[0038] an AI decision engine S103 for performing risk prediction and resource optimization path planning according to the real-time operation situation graph;

[0039] an intelligent scheduling center S104 for dynamically reconfiguring idle equipment, talents and funds in the industrial chain according to the resource optimization path planning;

[0040] an ecological value contract module S105 for converting data assets into verifiable collaborative benefits on the chain by calling the blockchain according to the reconfiguration results of the intelligent scheduling center.

[0041] In this embodiment, the smart platform system for modern industrial system construction includes a heterogeneous data fusion hub, a knowledge graph construction module, an AI decision engine, an intelligent scheduling center and an ecological value contract module. The heterogeneous data fusion hub configures data collection rules and standardization processing rules to ensure accurate collection and standardization processing of data of different sources and formats. The knowledge graph construction module sets data correlation rules and graph generation algorithms to generate accurate real-time operation situation graphs according to multi-source heterogeneous data. The AI decision engine loads pre-trained risk prediction models and resource optimization path planning models and adjusts parameters according to the actual situation of the industrial park. The intelligent scheduling center configures resource reconfiguration strategies and scheduling algorithms to ensure efficient dynamic reconfiguration of idle equipment, talents and funds. The ecological value contract module is connected with the blockchain platform, and configures data asset on-chain rules and collaborative benefit verification rules.

[0042] The heterogeneous data fusion hub periodically obtains relevant data from enterprise information systems, park management information systems and government department information systems, such as enterprise production plans, sales data, park infrastructure usage, government industrial policies, etc. The heterogeneous data fusion hub cleans, converts and integrates the collected multi-source heterogeneous data. First, remove noise and error data in the data to ensure data accuracy. Then, convert data of different formats to a unified format. Finally, classify and label the data, establish the association between the data, and provide structured data for subsequent knowledge graph construction.

[0043] The knowledge graph construction module performs correlation analysis on the standardized multi-source heterogeneous data. By analyzing the internal relationships between enterprise equipment data, environmental data, production plan data, sales data, etc., the potential relationships between various enterprises and links in the industrial chain are mined. For example, by analyzing the raw material procurement data of an enterprise and the product sales data of another enterprise, it is found that there is an upstream and downstream cooperation relationship between them. According to the results of data correlation analysis, the knowledge graph construction module uses a graph generation algorithm to generate a real-time running situation graph across enterprises and industries. This graph displays the running status of each enterprise and link in the industrial chain in a visual graphical manner, including production progress, equipment operation, raw material inventory, product sales, etc. At the same time, the graph can be updated in real time to reflect the dynamic changes of the industrial chain.

[0044] The AI decision engine uses a pre-trained risk prediction model to predict potential risks in the industrial chain based on the real-time running situation graph. For example, by analyzing the production progress and raw material inventory of an enterprise, it predicts whether the enterprise will face a risk of insufficient raw material supply; by analyzing market sales data and enterprise production plans, it predicts whether the enterprise will face a risk of product overstock. Based on risk prediction, the AI decision engine uses a resource optimization path planning model to plan the optimal resource optimization path according to the overall goals of the industrial park and the actual needs of each enterprise. For example, based on equipment idle conditions and enterprise production needs, it plans a reasonable equipment deployment plan; based on the professional skills of talents and job requirements, it plans an optimal talent allocation plan; based on the financial needs of enterprises and the investment return rate of projects, it plans a precise funding plan.

[0045] The intelligent dispatching center dynamically reconfigures idle equipment, talents and funds in the industrial chain according to the resource optimization path planning generated by the AI decision engine. For idle equipment, the intelligent dispatching center coordinates with enterprises to deploy equipment to enterprises in need, improving equipment utilization. For talents, the intelligent dispatching center recommends suitable talents to enterprises based on their professional skills and job requirements, promoting the rational flow of talents. For funds, the intelligent dispatching center guides financial institutions to accurately invest in potential enterprises and projects, improving the efficiency of fund use. During the execution of dynamic reconfiguration, the intelligent dispatching center monitors the running status of equipment, the work of talents and the use of funds in real time. If it finds problems with the reconfiguration plan or new situations, it adjusts the plan in a timely manner to ensure the effectiveness of resource optimization.

[0046] The ecological value contract module processes the relevant data assets according to the reconfiguration results of the intelligent scheduling center. For example, the allocation records of equipment, the flow information of talents, the investment situation of funds, and other data are uploaded to the blockchain platform to ensure the data's non-tamperability and traceability. Through the smart contract function of the blockchain, the process of converting data assets into collaborative benefits is verified and distributed. When the enterprises in the industrial chain achieve economic benefit improvement through collaborative cooperation, the ecological value contract module will distribute the collaborative benefits according to the pre-set rules according to the contribution of each enterprise, and record the distribution results on the blockchain, realizing the fair and transparent distribution of collaborative benefits.

[0047] By implementing the smart platform system for modern industrial system construction, traditional industrial parks will be significantly improved in data sharing, resource allocation, industrial chain collaboration, enterprise transformation, and investment attraction, etc. The data between enterprises, park management and government departments will be shared in real time, eliminating data island phenomenon and improving the scientificity and timeliness of decision-making. The resources in the industrial park will be optimally allocated, equipment idle rate will be greatly reduced, and the flow efficiency of talents and funds will be significantly improved, enhancing the economic benefits of the entire industrial park. The collaboration rate between upstream and downstream enterprises will be greatly improved, the stability and competitiveness of the industrial chain will be enhanced, and a complete industrial ecosystem will be formed. Enterprises will be supported and guided in the process of digital transformation, the success rate of digital transformation will be improved, and the cost recovery period will be shortened, promoting the overall digital transformation process of the industrial park. At the same time, by building an effective industrial ecosystem and collaborative development mechanism, the attractiveness of the industrial park to high-quality enterprises will be enhanced, attracting more high-quality enterprises and industries, forming an industrial agglomeration effect, and promoting the high-quality development of the industrial park.

[0048] The smart platform system for modern industrial system construction has five core functions:

[0049] First, driven by technological innovation, realize the digitalization, intelligentization, greenization, and internationalization transformation and upgrading of enterprises to industries.

[0050] Second, build a safe and efficient, collaborative and integrated industrial chain and supply chain system, and build a functional and complete modern service enterprise alliance system.

[0051] Third, form an open and inclusive, benign interactive industrial ecosystem.

[0052] Fourth, tailor precise "one enterprise one strategy" transformation and upgrading solutions and auxiliary implementation for enterprises.

[0053] Fifth, give preferential treatment and support to leading, chain and alliance enterprises to achieve the goal of transformation and upgrading first.

[0054] For the first point, a green index collection module is added to the heterogeneous data fusion hub to monitor enterprise energy consumption, carbon emissions, and other data in real time. Combined with the green optimization model of the AI decision engine, an energy-saving and emission-reduction path planning is generated. Through the knowledge graph construction module, the international market demand and trade policy are dynamically analyzed to generate an international expansion map, providing cross-border cooperation, market access, and other decision support for enterprises. The intelligent scheduling center interfaces with the industrial internet platform to dynamically match intelligent transformation service providers and equipment suppliers, promoting enterprise technology iteration. Using the digital twin technology of the AI decision engine, the upgrade effect of the enterprise production line is simulated to reduce the cost of transformation trial and error. Through the blockchain storage of the ecological value contract module, carbon credit trading support for green transformation is provided to enterprises, encouraging sustainable practices.

[0055] For the second point, the knowledge graph construction module maps global supply chain risk nodes (such as geopolitical conflicts and logistics disruptions) in real time, and generates alternative solution graphs through the AI decision engine. The intelligent scheduling center establishes an emergency resource pool to dynamically allocate idle production capacity and backup logistics channels, ensuring supply chain continuity. The service enterprise alliance management module is added to interface with third-party service institutions such as logistics, finance, and testing, and the ecological value contract module is used to realize intelligent matching of service demand and supply. The blockchain technology is used to build an alliance credit system to record enterprise service performance data and optimize the alliance member access and exit mechanism.

[0056] For the third point, the ecological value contract module expands the collaborative benefit distribution rules to include innovation contribution and data sharing dimensions, encouraging enterprises to participate in ecological co-construction. The knowledge graph construction module identifies ecological gaps (such as technical shortcomings and talent gaps), and the intelligent scheduling center guides resources to fill the gaps. An industrial innovation laboratory is built to integrate the multi-source data of the heterogeneous data fusion hub and the simulation capabilities of the AI decision engine, supporting joint research and development and scenario testing by enterprises.

[0057] For the fourth point, an enterprise portrait model is embedded in the AI decision engine to comprehensively analyze production data, financial data, and market data, generating a transformation capability assessment report. Based on the assessment results, the intelligent scheduling center matches customized service providers (such as digital consulting and management training) for enterprises from an external expert database. A transformation implementation monitoring module is added to track the progress of scheme implementation in real time, and key transformation indicators (such as equipment networking rate and order response speed) are collected through the heterogeneous data fusion hub.

[0058] For the fifth point, the intelligent scheduling center sets up a priority queue for leading enterprise resources, and gives weighted allocation in equipment deployment, talent recommendation, and capital investment. Through the knowledge graph construction module, the key node role of the chain leading enterprise is identified, and the AI decision engine customizes a "chain transformation" scheme for it (such as driving the synchronous digitization of upstream and downstream enterprises). The ecological value contract module sets up a special reward pool for leading enterprise transformation, and links the proportion of its collaborative income distribution to the effect of driving the industrial chain. The chain leading enterprise open day is held regularly, and the data visualization function of the smart platform is used to display the transformation results, forming a replicable benchmark case library.

[0059] In some embodiments, the real-time collection and standardized processing of multi-source heterogeneous data of enterprise equipment and environment in the industrial chain specifically include:

[0060] Through the industrial protocol adaptive interface, real-time collection of multi-source heterogeneous raw data streams of enterprise equipment and environment in the industrial chain is performed;

[0061] The multi-source heterogeneous raw data stream is input into the protocol analysis engine to convert it into an intermediate data stream in a unified JSON format;

[0062] The intermediate data stream is subjected to stream quality cleaning, and abnormal data is identified through a dynamic threshold rule library and time series interpolation reconstruction is triggered;

[0063] The cleaned intermediate data stream is injected into the spatio-temporal alignment engine for millisecond-level alignment according to the device spatial coordinates and data generation timestamp;

[0064] Based on the aligned intermediate data stream, the industrial ontology knowledge graph is called for semantic mapping, and multi-source heterogeneous data is output.

[0065] In this embodiment, according to the distribution of enterprise equipment and environmental sensors in the industrial park and communication requirements, appropriate industrial protocol adaptive interface hardware devices are selected. These devices should have multiple communication interfaces, such as Ethernet interface, RS485 interface, RS232 interface, etc., to adapt to the data transmission requirements of different devices. Industrial protocol adaptive interface devices are installed near enterprise equipment or at the location of environmental sensors to ensure stable and reliable physical connection between them. For example, for devices that use Ethernet communication, the interface device is connected to the device through a network cable; for devices that use RS485 communication, RS485 bus is used for connection. The corresponding software is installed on the industrial protocol adaptive interface device, which has industrial protocol adaptive function and built-in common industrial protocol library such as Modbus, OPC UA, Profibus, etc.

[0066] The industrial protocol adaptive interface device collects multi-source heterogeneous raw data streams generated by enterprise devices and environmental sensors in real time according to preset collection rules. During the collection process, the interface device records metadata such as the source device identifier and collection timestamp of the data, and encapsulates this information together with the raw data to enable accurate tracing of the data source and generation time during subsequent processing. The collected raw data streams are transmitted to the data processing center through wired or wireless means, and encryption technology is used during transmission to ensure data security.

[0067] A protocol analysis engine is deployed on the server in the data processing center, which should have high-performance data processing capabilities to quickly process large amounts of raw data streams. According to the industrial protocols used by devices in the industrial park, corresponding analysis rules are configured in the protocol analysis engine. For example, for Modbus protocol data, define the mapping relationship between register addresses and data fields; for OPCUA protocol data, set the corresponding relationship between node identifiers and data structures.

[0068] The collected multi-source heterogeneous raw data streams are input into the protocol analysis engine. The engine analyzes the raw data streams according to the preset analysis rules and converts them into unified JSON format intermediate data streams. During the conversion process, the engine checks the data to ensure its integrity and correctness. If it finds that the data has format errors or is missing, it will record error information and mark the abnormal data separately for special processing later. The converted intermediate data streams contain device identifiers, data item names, data values, collection timestamps, and other information, facilitating subsequent data processing and analysis.

[0069] Collect historical data from enterprise devices and environmental sensors in the industrial park, and combine with normal operation parameters of devices and environmental standards to build a dynamic threshold rule library. For different types of device data and environmental data, set reasonable threshold ranges. The dynamic threshold rule library has an automatic updating function, which can automatically adjust the threshold range according to newly collected data and changes in the running state of the device. For example, when a device is maintained or upgraded, its normal operation parameters may change, and the rule library will update the threshold values in a timely manner.

[0070] Stream quality cleaning is performed on the intermediate data stream. During the cleaning process, the system compares each data point with the corresponding threshold in the dynamic threshold rule library, identifying abnormal data that exceeds the threshold range. For the identified abnormal data, the system triggers a time series interpolation reconstruction mechanism. This mechanism uses reasonable interpolation methods (such as linear interpolation, moving average interpolation, etc.) to reconstruct abnormal data based on normal data before and after the abnormal data, generating reasonable replacement values. At the same time, the system records the processing of abnormal data, including the source of abnormal data, the reason for abnormality, the data value after reconstruction, etc., for subsequent data quality analysis and traceability.

[0071] A spatio-temporal alignment engine is deployed on the server of the data processing center, which should have strong spatio-temporal data processing capability and be able to process a large amount of data with time and space information. According to the spatial layout of equipment in the industrial park and the data collection time requirements, the mapping relationship between equipment spatial coordinates and data generation timestamps is configured in the spatio-temporal alignment engine. For example, each device is assigned a unique spatial coordinate identifier, and the precision requirement of the data collection timestamp is specified to be millisecond level.

[0072] The cleaned intermediate data stream is injected into the spatio-temporal alignment engine. The engine performs millisecond-level alignment on the intermediate data stream based on the spatial coordinates of the equipment and the data generation timestamps. During the alignment process, the engine checks the spatial coordinates and timestamps of each data point, merges and sorts data points belonging to the same device and close in time. If some data points have timestamp deviations, the engine will fine-tune them based on the time information of adjacent data points to ensure the alignment accuracy of the data. For data points with similar spatial positions but large timestamp differences, the engine will perform special processing, such as marking them as possible delayed data, to consider their impact in subsequent analysis.

[0073] An industrial ontology knowledge graph is constructed. This knowledge graph contains concepts, entities and relationships in related fields such as enterprise equipment, environment, production process and products in the industrial park. Through a combination of manual annotation and machine learning, the data in the industrial park is annotated and mined to enrich the content of the knowledge graph. For example, annotate the type, function, and affiliated enterprise of the equipment, and mine the association between equipment and the upstream and downstream relationships in the production process. Update and maintain the industrial ontology knowledge graph regularly to adapt to the development and changes of the industrial park. For example, when new equipment is introduced or the production process is adjusted in the park, update the relevant information in the knowledge graph in a timely manner.

[0074] Based on the aligned intermediate data stream, the industrial ontology knowledge graph is called for semantic mapping. The system matches each data point in the intermediate data stream with the concepts and entities in the knowledge graph, and assigns the corresponding semantic information to the data points. Through semantic mapping, the original numerical data is converted into data with clear semantic meaning, facilitating subsequent data analysis, decision support and visualization. Finally, the multi-source heterogeneous data with standardized processing and clear semantics is output, which can meet the needs of the intelligent platform system for modern industrial system construction.

[0075] Further, the construction steps of the industrial ontology knowledge graph specifically include:

[0076] An entity relationship constraint library is constructed, which is used to store the entity hierarchical relationships and attribute value domain constraints of the equipment class, environmental parameter class and production indicator class;

[0077] The entity relationship constraint library is loaded into the mapping rule engine to establish a mandatory binding relationship between the data fields and the graph attribute nodes, and associate the pre-defined industrial ontology type labels to form the industrial ontology knowledge graph;

[0078] Dynamic conflict detection is performed on the input data field stream, and the field semantic legality and value domain compliance are checked based on the mapping rule engine;

[0079] When a conflict is detected, the mapping rule rewriting engine generates alternative binding rules and updates them to the industrial ontology knowledge graph.

[0080] In this embodiment, the equipment class, environmental parameter class and production indicator class entities in the industry are comprehensively sorted. Taking the equipment class as an example, the hierarchical relationships between different types of equipment are clearly defined, such as dividing the equipment into large, medium and small equipment, and further subdividing different functional equipment (such as production equipment, detection equipment, etc.) under the large equipment. Similar hierarchical division is also made for medium and small equipment. For the environmental parameter class, hierarchical relationships are established according to its belonging environment type (such as workshop environment, warehouse environment, etc.) and monitoring indicators (such as temperature, humidity, air quality, etc.). The production indicator class is hierarchically divided according to the stages of the production process (such as raw material procurement indicators, production process indicators, product quality indicators, etc.).

[0081] Define detailed attributes for each entity and determine the value domain constraints of the attributes. For device class entities, attributes can include device name, device model, manufacturer, production date, service life, etc. For example, the device name attribute requires a string type and cannot be empty; the device model attribute needs to comply with specific naming rules; the service life attribute has a value domain of positive integers within a certain reasonable range (e.g., 1-50 years). The attributes of environmental parameter class entities, such as temperature, can have value domain constraints set according to actual environmental requirements, for example, the normal value domain of workshop temperature can be between 15°C and 35°C; the value domain of the humidity attribute can be set to 30%-70%. The attributes of production index class entities, such as product pass rate, have a value domain of 0%-100%.

[0082] Store the entity hierarchy relationship and defined attribute value domain constraints in a special database to form an entity relationship constraint library. This database should have good data structure and query performance to facilitate subsequent data processing and analysis. Establish a management mechanism for the constraint library and regularly update and maintain the constraint library. When new devices are introduced in the industry, production processes are adjusted, or environmental standards change, update the entity hierarchy relationship and attribute value domain constraints in a timely manner.

[0083] Deploy a mapping rule engine in the data processing system, which should have strong rule processing and matching capabilities. Configure the mapping rule engine to interact with the entity relationship constraint library. Set data field and graph attribute node binding rule templates in the engine, for example, for the "device name" field in device data, specify that it should be bound to the "device name" attribute node of the device class entity in the industrial ontology knowledge graph.

[0084] Load the entity relationship constraint library into the mapping rule engine and establish a forced binding relationship between data fields and graph attribute nodes based on the entity hierarchy relationship and attribute definitions in the constraint library. For example, for the "device model" field in device data, it is forced to bind to the attribute node of the corresponding model in the device class entity in the knowledge graph, ensuring that the data can be accurately mapped to the knowledge graph. At the same time, associate a pre-defined industrial ontology type label with each binding relationship. For example, associate the "device ontology" label with the binding relationship of device class data, the "environment ontology" label with environmental parameter class data, and the "production ontology" label with production index class data. These labels help subsequent classification management and query of data in the knowledge graph.

[0085] Integrate the binding relationship between data fields and graph attribute nodes and the industrial ontology type label through the mapping rule engine to form the industrial ontology knowledge graph. The knowledge graph displays the relationship between entities and attributes in a graphical manner, with edges connecting entities and labels indicating the relationship type between entities, and entity nodes labeled with their attributes and attribute values.

[0086] A dynamic conflict detection module is integrated into the data processing pipeline. When input data fields enter the system, the conflict detection mechanism is triggered. This mechanism checks the semantic legality and value domain compliance of each data field based on the binding relationships in the mapping rule engine and the entity relationship constraint library. Semantic legality check mainly checks whether the semantics of the data field are consistent with the semantics of the bound graph attribute node. For example, if a data field is bound to a "device name" attribute node, but the field content is actually device model information, it is determined to be semantically illegal. Value domain compliance checks whether the value of the data field is within the corresponding attribute value domain range. For example, for a temperature data field, check whether its value is between 15°C and 35°C.

[0087] When a conflict is detected, the system records detailed information about the conflict, including the conflicting data field, field value, bound graph attribute node, conflict type (semantic illegality or value domain non-compliance), etc. The conflict information is fed back to the data provider or relevant management personnel to promptly understand data quality problems and handle them.

[0088] A mapping rule rewriting engine is deployed in the system, which can automatically generate alternative binding rules based on conflict detection results. When a conflict is detected, the rule rewriting engine is triggered. The rule rewriting engine analyzes possible alternative binding relationships based on the type of the conflict and the characteristics of the data. For example, if it is found that the semantics of a certain data field do not match the bound attribute node, but are relatively close to the semantics of another attribute node, the engine will consider generating a rule that binds the field to another attribute node.

[0089] After the rule rewriting engine generates alternative binding rules, these rules are evaluated. The evaluation criteria include the rationality of the rules, the accuracy of the description of the data semantics, and the impact on subsequent data analysis and application, etc.

[0090] After evaluation and review, the alternative binding rules that pass the evaluation and review are updated to the mapping rule engine and the industrial ontology knowledge graph is updated synchronously. The original binding relationship is replaced by the new alternative binding relationship, so that the knowledge graph can accurately reflect the actual situation of the data. At the same time, the history information of the rule update is recorded, including the update time, update content, update reason, etc., to facilitate subsequent tracing and management.

[0091] In this embodiment, the problem of unclear semantics and weak correlation of industrial data can be effectively solved. The constructed knowledge graph has clear entity hierarchical relationships and attribute value domain constraints, and can accurately describe the relationships and attributes between various entities in the industry. The dynamic conflict detection and rule rewriting mechanism can timely detect and handle data quality problems, ensuring the accuracy and real-time performance of the knowledge graph.

[0092] In some embodiments, the generation of real-time operation situation spectrum across enterprises and industries based on multi-source heterogeneous data specifically includes:

[0093] Based on the entity relationship constraint library, the spatio-temporal relationship reasoning engine calculates the physical location association and production process dependency relationship between entities to generate entity relationship data;

[0094] Based on the entity relationship data, the industry chain topology modeler constructs a multi-level dynamic topology network, and the levels of the multi-level dynamic topology network include raw material layer, component layer, whole machine assembly layer and service layer;

[0095] The multi-level dynamic topology network is input into the situation spectrum renderer, the node state index is encoded into color gradient value, and the relationship weight is encoded into connection line width value, and the real-time operation situation spectrum containing node state index and relationship weight is generated in real time.

[0096] In this embodiment, based on the entity relationship constraint library constructed in the early stage, cross-enterprise and cross-industry entity information is further collected and organized, including enterprises, equipment, raw materials, components, products, etc. The hierarchical relationship, business association relationship and spatio-temporal distribution information between these entities are clarified. For example, it is clear that a certain enterprise is a supplier of a certain component, the use position of the component in a certain production equipment, and the transportation time and space path of the component from the supplier to the production enterprise.

[0097] The improved entity relationship constraint library is loaded into the spatio-temporal relationship reasoning engine. The engine should have strong data processing and logical reasoning ability, and be able to perform reasoning calculation according to the rules and relationships in the constraint library.

[0098] The spatio-temporal relationship reasoning engine calculates the physical location association between different entities according to the spatial coordinate information in the entity relationship constraint library. For example, for enterprises within the same industrial park, the straight-line distance and adjacent relationship between enterprises are calculated; for suppliers and production enterprises distributed in different regions, the transportation distance and transportation time are calculated.

[0099] Considering the dynamic changes of entities such as relocation of enterprises and movement of equipment, the physical location association information is updated in time. Through integration with real-time positioning systems (such as GPS, RFID, etc.), the latest position data of entities is obtained to ensure the accuracy of the physical location association.

[0100] According to the production process information in the entity relationship constraint library, the reasoning engine analyzes the dependency relationship of different entities in the production process. For example, the supply relationship between raw material suppliers and component production enterprises, the matching relationship between component production enterprises and whole machine assembly enterprises, and the service association between whole machine assembly enterprises and after-sales service enterprises are determined.

[0101] Consider the uncertainty factors in the production process, such as production delay, quality problem, etc., and dynamically adjust the production process dependency relationship. Through integration with the enterprise's production management system, real-time production progress and quality data are obtained, and changes in dependency relationships are discovered and updated in a timely manner.

[0102] Integrate the calculated physical location association and production process dependency relationship to generate entity relationship data. These data are stored in a structured form, including entity identification, associated entity identification, association type (physical location association or production process dependency), association strength, etc. For example, it is recorded that there is a raw material supply relationship between enterprise A and enterprise B, and the association strength can be determined according to the supply amount, supply frequency, etc.

[0103] Deploy the industry chain topology modeler in the data processing system and configure it. The modeler should support the construction of multi-level networks and be able to define the hierarchical structure of the network according to different industry characteristics and business needs.

[0104] Determine the levels of the multi-level dynamic topology network, including the raw material layer, the component layer, the whole machine assembly layer, and the service layer. Define the corresponding entity types and attributes for each level, for example, the entity types of the raw material layer include various raw materials, and the attributes include raw material name, specification, inventory, etc.; the entity types of the component layer include various components, and the attributes include component model, manufacturer, production cycle, etc.

[0105] Input the entity relationship data into the industry chain topology modeler, and the modeler connects entities of different levels according to the association relationship in the data to construct a multi-level dynamic topology network. For example, connect raw material suppliers (raw material layer) and component production enterprises (component layer) through raw material supply relationship, connect component production enterprises and whole machine assembly enterprises (whole machine assembly layer) through component matching relationship, and connect whole machine assembly enterprises and after-sales service enterprises (service layer) through product sales and service relationship.

[0106] In the process of constructing the network, consider the weight relationship between entities. The weight can be determined according to the association strength, transaction amount, cooperation frequency, etc. For example, for raw material supply relationships with larger transaction amounts, give higher weights and use thicker connection lines in the topology network.

[0107] Establish a dynamic update mechanism for the multi-level dynamic topology network to monitor the changes in entity relationship data in real time. When new entities join, the relationship between entities changes, or entities exit, update the topology network in a timely manner. For example, when a new raw material supplier enters the industry chain, it is added to the raw material layer and the connection relationship with other levels is updated; when the cooperation between a component production enterprise and a whole machine assembly enterprise is terminated, the corresponding connection line is deleted.

[0108] A situation map renderer is deployed in the visualization system and configured to adapt to the rendering requirements of the multi-level dynamic topology network. The renderer should have rich graphical rendering functions and be able to display the nodes and connection lines in the network in an intuitive way.

[0109] Define visualization encoding rules for node state indicators and relationship weights. For example, encode node state indicators (such as inventory, production progress, equipment utilization, etc.) as color gradient values, with different colors representing different state levels, such as green for normal state, yellow for warning state, and red for abnormal state; encode relationship weights as connection line width values, with larger weights resulting in thicker connection lines.

[0110] Real-time input the multi-level dynamic topology network into the situation map renderer, and the renderer renders the nodes and connection lines in the network according to the configured encoding rules. For example, render the raw material node with sufficient inventory as green; render the component production enterprise node with delayed production progress as yellow; render the connection line with a large transaction amount as a thicker line.

[0111] The renderer updates the display content of the map in real time, and when the topology network changes, it immediately adjusts the color, width, and other attributes of the nodes and connection lines to ensure that the real-time running situation map accurately reflects the running state of the industry.

[0112] Preferably, add interactive functions to the real-time running situation map to allow users to interact with the map through mouse clicks, drags, and other operations. For example, users can click on a node to view detailed information about the node, including entity name, attribute value, and associated relationships; users can drag the node to adjust the layout of the map for better observation and analysis.

[0113] In this embodiment, by implementing the cross-enterprise, cross-industry real-time running situation map generation scheme, effective integration and analysis of multi-source heterogeneous data can be achieved, and the relationships, states, and weights between different entities in the industry can be intuitively displayed. Decision makers can quickly understand the running situation of the industry through the real-time running situation map, timely identify potential problems and risks, and make scientific and reasonable decisions. At the same time, the dynamic updating mechanism and interactive functions improve the practicality and flexibility of the map, which can meet the needs of industry monitoring and analysis in different scenarios, promote the coordinated development and optimization and upgrading of the industry.

[0114] In some embodiments, the risk prediction and resource optimization path planning are performed according to the real-time running situation map, specifically including:

[0115] Parse the device load rate, supply chain connection strength, and inventory turnover rate dynamic indicators from the real-time running situation map to generate a multi-dimensional feature vector;

[0116] Input the multi-dimensional feature vectors into the risk prediction model cluster, and perform supply chain disruption probability calculation, policy compliance detection, and capacity fluctuation early warning in parallel to obtain risk prediction results.

[0117] Based on the risk prediction results, trigger the resource optimization solver to generate a Pareto optimal solution set of equipment scheduling schemes and fund allocation schemes;

[0118] When the Pareto optimal solution set has conflicts, drive the decision fusion arbitrator to output the final executable instruction set through a weighted voting mechanism.

[0119] In this embodiment, real-time running situation graphs are analyzed in depth to identify dynamic indicators closely related to risk prediction and resource optimization, including equipment load rate, supply chain connection strength, and inventory turnover rate, etc. The nodes and relationships corresponding to these dynamic indicators are located in the graph. For example, the equipment load rate indicator is usually associated with the equipment node and can be obtained through the running state information of the equipment node in the graph; the supply chain connection strength indicator can be determined by analyzing the connection relationships and transaction data between different enterprise nodes; the inventory turnover rate indicator is related to raw material, spare parts, and finished product inventory nodes.

[0120] Real-time data of dynamic indicators are extracted from the data storage of real-time running situation graphs. During the extraction process, the accuracy and completeness of the data are ensured, and missing data is reasonably interpolated, and abnormal data is cleaned and corrected. The extracted data is preprocessed, including data standardization, normalization, etc. For example, the data of indicators such as equipment load rate, supply chain connection strength, and inventory turnover rate are uniformly mapped to a specific numerical range for subsequent analysis and processing.

[0121] The preprocessed dynamic indicator data such as equipment load rate, supply chain connection strength, and inventory turnover rate are combined to generate multi-dimensional feature vectors. Each feature vector represents the running state characteristics of the industry at a certain moment, and each dimension in the vector corresponds to the value of a dynamic indicator. For example, a feature vector can be represented as [equipment load rate value, supply chain connection strength value, inventory turnover rate value].

[0122] A risk prediction model cluster is constructed, which includes multiple prediction models for different risk types, such as supply chain disruption probability calculation model, policy compliance detection model, and capacity fluctuation early warning model, etc.

[0123] For each model, select the appropriate algorithm and training data for training. For example, the supply chain disruption probability calculation model can use a machine learning algorithm based on historical data and real-time information to predict the probability of disruption by analyzing the stability of each link in the supply chain, the reliability of suppliers, and other factors; the policy compliance detection model can combine the policy and regulation library and industry operation data to determine whether the industry operation complies with relevant policy requirements; the capacity fluctuation early warning model can predict possible fluctuations in capacity based on factors such as equipment operating status and market demand forecasts.

[0124] The generated multi-dimensional feature vector is input into the risk prediction model cluster in parallel, and each model independently analyzes and calculates the input feature vector to obtain the corresponding risk prediction result.

[0125] Integrate the prediction results of each model. For example, aggregate information such as supply chain disruption probability, policy compliance detection results (compliant or non-compliant), and capacity fluctuation early warning levels (high, medium, and low) to form a comprehensive risk prediction report. The report should include detailed descriptions of each risk type, occurrence probability or level, and possible impact.

[0126] According to the risk prediction results, determine the optimization target of resources. For example, in the case of high supply chain disruption risk, the optimization target may be to reduce inventory costs while ensuring the stability of the supply chain; in the case of high capacity fluctuation early warning, the optimization target may be to improve equipment utilization and reasonably arrange production plans.

[0127] Set constraints for resource optimization, such as device capacity limits, funding budget limits, and personnel staffing limits. These constraints will ensure that the generated optimization scheme is feasible in actual operation.

[0128] Trigger the resource optimization solver and input the risk prediction results, resource optimization target, and constraint conditions into the solver. The solver should have powerful optimization algorithms and computing power to search for the optimal resource allocation scheme based on the input information.

[0129] The solver generates a set of Pareto optimal solutions for the device scheduling scheme and funding allocation scheme through iterative calculation. Each solution in the Pareto optimal solution set represents a combination of schemes that achieves a relatively optimal resource optimization target while meeting the constraints. For example, one solution may be to reasonably allocate funds to different production links to minimize costs and maximize benefits while ensuring the normal operation of the equipment.

[0130] Analyze the Pareto optimal solution set and identify the conflicts present within it. Conflicts can manifest as mutually contradictory situations in terms of equipment scheduling, funding allocation, etc. For example, one solution emphasizes investing a large amount of funds into equipment upgrades to increase capacity, while another suggests using funds to expand inventory to mitigate supply chain disruption risks.

[0131] Analyze the causes and effects of the conflicts and evaluate the priority and feasibility of each solution. For example, consider the main risks currently faced by the industry, the strategic goals of the enterprise, and resource constraints, etc., to determine which solution better aligns with the actual situation and needs.

[0132] Drive the decision fusion arbitrator to arbitrate the Pareto optimal solution set. The arbitrator should have the ability to comprehensively analyze and make decisions, and can evaluate and select different solutions according to pre-set rules and weights.

[0133] Use a weighted voting mechanism to assign appropriate weights to each solution. The weights can be determined based on risk prediction results, expert opinions, historical data, etc. For example, solutions with higher supply chain disruption risks are given higher weights; solutions that align with the long-term strategic goals of the enterprise are also given some weight increase. The arbitrator votes according to the weights of each solution, and selects the solution with the highest weight as the final executable plan.

[0134] Convert the final executable plan selected by the decision fusion arbitrator into a specific set of executable instructions. The instruction set includes specific information such as the time, place and method of equipment scheduling, the specific amount and purpose of funding allocation, etc. Send the final executable instruction set to relevant departments and personnel to ensure that the plan can be effectively implemented. At the same time, establish a feedback mechanism to monitor and evaluate the execution effect of the instruction set, so as to adjust and optimize it in a timely manner.

[0135] In this embodiment, potential risks in industrial operation can be predicted in real time and accurately, and scientific and reasonable resource optimization plans can be generated. The application of the Pareto optimal solution set and the decision fusion arbitrator ensures the optimality and feasibility of the plan, effectively improves the stability and efficiency of industrial operation, and reduces the operating cost and risk.

[0136] Further, the multi-dimensional feature vector is input into the risk prediction model cluster, and the supply chain disruption probability calculation, policy compliance detection and capacity fluctuation early warning are performed in parallel to obtain the risk prediction result, specifically including:

[0137] The multi-dimensional feature vector is split into a supply chain feature subset, a policy compliance feature subset and a capacity fluctuation feature subset by the feature vector intelligent distributor according to the pre-set mapping rules;

[0138] The supply chain feature subset is input into a supply chain disruption prediction model, the supply chain disruption probability is calculated based on the logistics delay rate and the supplier node activity, and a first risk prediction result is obtained;

[0139] The policy compliance feature subset is input into a policy compliance detection model, the matching degree analysis of enterprise behavior data and policy knowledge base clauses is performed, and a second risk prediction result is obtained;

[0140] The capacity fluctuation feature subset is input into a capacity fluctuation early warning model, the time sequence correlation of equipment load rate and order saturation is analyzed, and a third risk prediction result is obtained;

[0141] The first risk prediction result, the second risk prediction result and the third risk prediction result are subjected to confidence weighted aggregation to generate a comprehensive risk prediction result.

[0142] In this embodiment, the correlation between the three risk types of supply chain disruption, policy compliance and capacity fluctuation and each index in the multi-dimensional feature vector is analyzed. For example, for the supply chain disruption risk, it is determined that the logistics delay rate, the supplier node activity and other indexes are closely related; for the policy compliance risk, the indexes related to enterprise behavior data and policy clauses are determined; for the capacity fluctuation risk, indexes with strong correlation such as equipment load rate and order saturation are found. According to the above analysis results, a preset mapping rule is formulated. The rule determines that each index in the multi-dimensional feature vector should be allocated to which feature subset of which risk type. For example, it is specified that the logistics delay rate index is mapped to the supply chain feature subset, the enterprise tax record and other behavior data indexes are mapped to the policy compliance feature subset, and the equipment running time and other indexes are mapped to the capacity fluctuation feature subset.

[0143] The feature vector intelligent distributor is deployed, and the preset mapping rule is loaded into the distributor. The distributor should have fast and accurate data processing capability, and can real-time split the multi-dimensional feature vector according to the mapping rule. When receiving the multi-dimensional feature vector, the feature vector intelligent distributor allocates each index in the vector to the corresponding feature subset according to the preset mapping rule. For example, the logistics delay rate, the supplier delivery timeliness and other indexes are extracted to form the supply chain feature subset; the enterprise environmental emission data, tax declaration record and other indexes are extracted to form the policy compliance feature subset; the equipment boot rate, production task completion amount and other indexes are extracted to form the capacity fluctuation feature subset.

[0144] The subset of supply chain features is input into the supply chain disruption prediction model. This model is based on historical data and industry experience, and comprehensively analyzes indicators such as logistics delay rate and supplier node activity. Analyze the trend of logistics delay rate, combined with factors such as historical supply records, production capacity, and geographical location of supplier nodes, to assess the activity of supplier nodes. For example, if the logistics delay rate of a supplier is continuously rising, and the recent supply volume is decreasing, the activity of the supplier node may be low, and the risk of supply chain disruption may increase accordingly. According to the analysis results, calculate the probability of supply chain disruption, and obtain the first risk prediction result. The result is presented in the form of probability value or risk level, for example, the probability of supply chain disruption is 30%, or the risk level is "medium".

[0145] The subset of policy compliance features is input into the policy compliance detection model. This model has a rich policy knowledge base, including national, local and industry-related policies, regulations, standards and other information. Perform matching degree analysis of enterprise behavior data and policy knowledge base clauses. For example, compare the enterprise's environmental emission data with the emission standards in environmental protection policies, and check the tax declaration records with the provisions in tax policies. According to the matching degree analysis results, judge whether the enterprise has policy compliance risks. If some of the enterprise's behavior data does not match the policy clauses, it is determined that there is a policy compliance risk, and the corresponding risk description and severity are given, obtaining the second risk prediction result. For example, it is found that the emission of a certain pollutant of the enterprise exceeds the standard specified in the environmental protection policy, and it is determined that there is an environmental protection policy compliance risk, with a risk severity of "high".

[0146] The subset of capacity fluctuation features is input into the capacity fluctuation early warning model. This model focuses on the time correlation between equipment load rate and order saturation. Analyze the changes in equipment load rate in different time periods and the fluctuation trend of order saturation. For example, observe the change law of equipment load rate in the production peak season and off-season, and whether the order saturation matches the change of equipment load rate. If it is found that there is a mismatch between equipment load rate and order saturation, such as high equipment load rate but insufficient order saturation, or low equipment load rate but high order saturation, it may indicate a capacity fluctuation risk. According to the analysis results, give the capacity fluctuation warning level, and obtain the third risk prediction result. For example, the warning level is "high", indicating that the capacity fluctuation risk is large.

[0147] The confidence of the first risk prediction result (supply chain disruption risk), the second risk prediction result (policy compliance risk), and the third risk prediction result (capacity fluctuation risk) is evaluated. The confidence evaluation is based on the accuracy of each model, historical prediction performance, data quality, and other factors. For example, if the supply chain disruption prediction model has a high accuracy rate in past historical predictions, and the current input supply chain feature subset data quality is good, the confidence of the first risk prediction result output by the model is high; on the contrary, if the policy compliance detection model has some errors in matching some complex policy clauses, the confidence of the second risk prediction result may be relatively low. According to the evaluation result, a corresponding confidence weight is assigned to each risk prediction result. The weight range can be between 0 and 1, and the higher the weight, the higher the reliability of the prediction result.

[0148] The first risk prediction result, the second risk prediction result, and the third risk prediction result are weighted and aggregated according to their respective confidence weights. For example, if the weight of the first risk prediction result is 0.4, the weight of the second risk prediction result is 0.3, and the weight of the third risk prediction result is 0.3, then in the aggregation process, the first risk prediction result has a relatively large impact on the comprehensive risk prediction result. Through weighted aggregation, the prediction results of the three risk types are comprehensively considered to generate a comprehensive risk prediction result. This result can be a comprehensive risk score or a description of the overall risk situation of the industry, such as "the industry is facing a moderate degree of comprehensive risk, with supply chain disruption risk and capacity fluctuation risk being more prominent, and policy compliance risk being relatively low."

[0149] In this embodiment, the information in the multi-dimensional feature vector can be fully utilized to comprehensively and accurately predict risks such as supply chain disruption, policy compliance, and capacity fluctuation. The feature vector intelligent distributor ensures that the features of different risk types are effectively utilized, and each risk prediction model can conduct in-depth analysis on specific risks. The confidence weighted aggregation improves the reliability and scientificity of the comprehensive risk prediction result.

[0150] Further, based on the risk prediction result, the resource optimization solver generates a Pareto optimal solution set of the equipment scheduling scheme and the fund allocation scheme, specifically including:

[0151] The risk prediction result is converted into a constraint condition of the resource optimization model by the multi-objective constraint converter, and the equipment scheduling geographic radius constraint and the fund allocation flow rate threshold are loaded;

[0152] Based on the constraint condition, the non-dominated sorting optimization engine is driven to evolve the equipment scheduling scheme population and the fund allocation scheme population in parallel to obtain a candidate solution set;

[0153] Performing solution set clustering compression on the candidate solution set, retaining the Pareto front representative solutions of each cluster through scheme similarity calculation;

[0154] Inputting the Pareto front representative solutions into the industrial feasibility filter, filtering out infeasible schemes based on equipment maintenance cycles and financial regulatory rules, and outputting the Pareto optimal solution set.

[0155] In this embodiment, the risk prediction results are analyzed in depth. The risk prediction results include information in multiple dimensions such as supply chain disruption risk, market fluctuation risk, and policy compliance risk. For example, supply chain disruption risk may affect the supply of raw materials for the equipment, thereby affecting the scheduling plan of the equipment; market fluctuation risk may lead to changes in order volume, affecting the budget for production and sales in the allocation of funds.

[0156] Determine the correlation between the risk prediction results and the objectives in the resource optimization model. For example, high supply chain disruption risk may mean that the scheduling of backup equipment needs to be increased, which will increase the cost and complexity of equipment scheduling; when market fluctuation risk is high, more funds may need to be reserved for market changes, which will affect the proportion of fund allocation.

[0157] Convert the risk prediction results into constraint conditions of the resource optimization model through the multi-objective constraint converter. For example, if the risk prediction results show that the supply chain disruption risk is high, a constraint condition can be set that requires a certain number of backup equipment to be included in the equipment scheduling scheme, and the scheduling range of the backup equipment should be within a certain geographical radius to ensure that it can be put into use in time when the supply chain problem occurs.

[0158] At the same time, load other constraint conditions such as equipment scheduling geographical radius constraints and fund allocation liquidity rate thresholds. The equipment scheduling geographical radius constraint is to ensure the timeliness and cost-effectiveness of equipment scheduling, such as specifying that the equipment scheduling distance cannot exceed a certain specific geographical range to reduce transportation cost and time. The fund allocation liquidity rate threshold is to control the liquidity of funds to ensure that the enterprise has enough funds for daily operations and to respond to unexpected situations, such as specifying that the proportion of liquid funds for short-term investment in fund allocation cannot be lower than a certain threshold.

[0159] Deploy the non-dominated sorting optimization engine in the resource optimization system. The engine should have strong parallel computing capability and multi-objective optimization algorithm, and be able to handle the optimization problem of equipment scheduling scheme and fund allocation scheme at the same time. Configure the parameters of the non-dominated sorting optimization engine, including population size, iteration number, crossover probability, mutation probability, etc. The population size determines the number of schemes generated in each iteration, the iteration number affects the depth and accuracy of optimization, and the crossover probability and mutation probability control the diversity and innovation of the scheme.

[0160] Based on the loaded constraints, the non-dominated sorting optimization engine and parallel evolution device scheduling scheme population and fund allocation scheme population. In the evolution process, the engine will generate new solutions according to the constraints and optimization objectives, selection, crossover and mutation operations in the population. After several iterations, a candidate solution set is obtained. The candidate solution set contains multiple combinations of device scheduling schemes and fund allocation schemes, each of which meets the constraints to some extent and has certain advantages in device scheduling cost, fund use efficiency, risk response ability, etc.

[0161] The similarity of the device scheduling scheme and the fund allocation scheme in the candidate solution set is calculated. For the device scheduling scheme, the type, number, scheduling time, scheduling location, etc. of the scheduled device can be compared for similarity; for the fund allocation scheme, the proportion, purpose, investment project, etc. of the fund allocation can be compared for similarity. Use appropriate similarity calculation methods, such as cosine similarity based on feature vector, Euclidean distance based on distance, etc. to calculate the similarity value between each two schemes. The higher the similarity value, the more similar the two schemes.

[0162] According to the scheme similarity calculation result, the candidate solution set is clustered and compressed. Clustering algorithms such as K-means clustering algorithm can be used to classify similar schemes into a class. In each cluster, the Pareto dominance relationship of the scheme is calculated to retain the Pareto front representative solution of each cluster. The Pareto front representative solution refers to a solution in a certain cluster that does not exist other solutions that can be superior to the solution in all objectives. These representative solutions represent the optimal solution in the cluster, which can effectively reduce the number of candidate solution sets while retaining the characteristics of the optimal solution.

[0163] Sort out the device maintenance cycle and financial regulatory rules and other industrial feasibility rules. The device maintenance cycle rule specifies the regular maintenance time and content of the device to ensure the normal operation and service life of the device; the financial regulatory rules include the enterprise's fund use provisions, tax requirements, credit policy, etc. to ensure that the enterprise's fund allocation meets the legal regulations and regulatory requirements. Organize these rules into executable judgment conditions, for example, the device maintenance cycle rule can be expressed as "the device must be maintained every certain time, and cannot be used for production during maintenance"; the financial regulatory rule can be expressed as "the proportion of funds used for illegal activities in the enterprise's fund allocation is 0".

[0164] The Pareto frontier representative solutions are input into an industrial feasibility filter. The filter judges each representative solution one by one according to the combed industrial feasibility rules. If a certain representative solution violates any industrial feasibility rule, it will be screened out. For example, if a device scheduling scheme causes the device to be overused during the maintenance period, or a fund allocation scheme violates the fund use limit in the financial regulatory rules, these schemes will be excluded. After screening, the Pareto optimal solution set is output. The schemes in the Pareto optimal solution set not only achieve the optimal balance of multi-objectives in device scheduling and fund allocation, but also fully comply with the industrial feasibility rules and can be directly applied to the actual operation of the enterprise.

[0165] In this embodiment, the influence of risk factors on resource optimization can be fully considered, and high-quality optimization schemes can be generated under complex constraints. Multi-objective constraint conversion and loading ensure the rationality and feasibility of the schemes, the non-dominated sorting optimization engine improves the efficiency and diversity of scheme generation, the solution set clustering compression reduces the number of schemes, improves the decision-making efficiency, and the industrial feasibility filter ensures the compliance and operability of the schemes.

[0166] Further, when the Pareto optimal solution set has conflicts, the driving decision fusion arbitrator outputs a final executable instruction set through a weighted voting mechanism, specifically including:

[0167] Through the conflict detector, the target function conflict items of the device scheduling scheme and the fund allocation scheme in the Pareto optimal solution set are identified;

[0168] For the target function conflict items, the dynamic weight calculator is driven to generate an implementation cost weight, a risk confidence weight, and an efficiency urgency weight;

[0169] Based on the implementation cost weight, the risk confidence weight, and the efficiency urgency weight, the multi-dimensional voter triggers the enterprise node, the park management node, and the financial institution node to conduct weighted voting, and generates a weighted voting result;

[0170] Based on the weighted voting result, an executable instruction set of the fusion scheduling and fund instructions is generated.

[0171] In this embodiment, the target functions of the device scheduling scheme and the fund allocation scheme are determined. The target functions of the device scheduling scheme include production efficiency, device utilization rate, order delivery rate, etc.; the target functions of the fund allocation scheme may include investment return rate, fund liquidity, risk control level, etc.

[0172] The definition of the objective function conflict item is determined, that is, when the equipment scheduling scheme and the fund allocation scheme exist mutual restriction and are difficult to simultaneously satisfy the optimal condition on a certain objective, the objective is the conflict item. For example, improving production efficiency may require increasing equipment investment and fund support, but the fund allocation scheme may not be able to meet the demand of the equipment scheduling scheme for funds due to fund budget limitations, at which time the production efficiency and the fund budget constitute

[0173] The conflict detector is deployed, and the equipment scheduling scheme and the fund allocation scheme in the Pareto optimal solution set are input into the conflict detector. The conflict detector analyzes each scheme combination in the Pareto optimal solution set one by one according to the pre-defined objective function conflict item. By comparing the values of different scheme combinations on each objective, it is determined whether there is an objective function conflict. For example, if the production efficiency objective value required by the equipment scheduling scheme in a certain scheme combination is high, and the amount of funds provided by the fund allocation scheme cannot meet the equipment procurement and personnel training costs required to achieve the production efficiency, then the conflict detector will identify the production efficiency and the fund budget as two objective function conflict items.

[0174] For the identified objective function conflict items, three main factors affecting the decision are analyzed: implementation cost, risk confidence, and benefit urgency. The implementation cost refers to the input of resources such as funds, manpower, and material resources required to execute a certain scheme, for example, if the equipment scheduling scheme requires the purchase of new equipment, then the equipment procurement cost, installation and debugging cost, etc. are part of the implementation cost. The risk confidence refers to the degree of assessment of the risks that may be faced during the implementation of the scheme, for example, if the fund allocation scheme involves high-risk investment projects, then the risk confidence of the scheme is high. The benefit urgency refers to the timeliness and importance of the benefits that can be brought about after the implementation of the scheme, for example, a certain equipment scheduling scheme can quickly meet the demand of urgent orders, thereby improving customer satisfaction and market competitiveness, so the benefit urgency of the scheme is high.

[0175] The dynamic weight calculator is deployed, and the implementation cost weight, the risk confidence weight, and the benefit urgency weight are generated for each objective function conflict item according to the above three influencing factors. The generation of the weights can adopt the expert evaluation method or the statistical analysis method based on historical data.

[0176] The multi-dimensional voter is triggered, and information such as target function conflict items, implementation cost weights, risk confidence weights, and benefit urgency weights is sent to enterprise nodes, park management nodes, and financial institution nodes. A special knowledge graph architecture is designed for each node (enterprise node, park management node, financial institution node) to clearly define the entity type, relationship type, and attribute type of the knowledge graph. For example, the knowledge graph of the enterprise node can include enterprise basic information (name, size, industry, etc.), production and operation data (output, sales, profit, etc.), technical capabilities (number of patents, R&D team, etc.), cooperation history (cooperation records with other enterprises in the industrial chain), etc. The knowledge graph of the park management node can include park planning information, infrastructure conditions, information of enterprises in the park, policies and regulations, etc. The knowledge graph of the financial institution node can include financial product information, investment cases, risk assessment models, and liquidity data, etc. Each node receives the information, combines its own node knowledge graph and historical data, and votes on the solution to each target function conflict item. When voting, each node will comprehensively evaluate different solutions according to the implementation cost weight, risk confidence weight, and benefit urgency weight, and then give its own voting opinion. For example, the enterprise node may focus more on the benefit urgency and tend to choose a solution that can quickly bring economic benefits; the park management node may focus more on the implementation cost and risk control and tend to choose a solution with lower cost and lower risk; the financial institution node may focus more on investment returns and liquidity and tend to choose a solution that can bring higher investment returns and faster capital recovery. The multi-dimensional voter collects the voting results of each node and performs aggregation and statistics.

[0177] The weighted voting results generated by the multi-dimensional voter are analyzed. The voting tendencies of each node for different solutions and the influence of different weights on the voting results are analyzed. Based on the voting result analysis, an executable instruction set that integrates scheduling and funding instructions is generated. The instruction set clearly specifies the specific operations in device scheduling and funding allocation, such as determining the scheduling time, location, and quantity of devices, and the allocation proportion, purpose, and time node of funds, etc. The executable instruction set should be operational and executable, and can directly guide the actual operation of the enterprise. For example, the instruction set should clearly specify that the device scheduling personnel need to schedule which devices to which production workshop at what time, and the financial personnel need to allocate how much money to which project account at what time, etc.

[0178] In this embodiment, the driving decision fusion arbitrator outputs the final executable instruction set scheme through a weighted voting mechanism, which can scientifically and reasonably solve the objective function conflict problem between the equipment scheduling scheme and the fund allocation scheme. The conflict detector can accurately identify the conflict items, the dynamic weight calculator can reasonably allocate the weights, the multi-dimensional voter can fully listen to the opinions of all parties, and the finally generated executable instruction set can comprehensively consider the implementation cost, risk confidence and benefit urgency, etc., to improve the scientificity and effectiveness of the industrial resource collaborative decision-making, and provide strong support for the stable operation and development of enterprises.

[0179] In some embodiments, the dynamic reconfiguration of the idle equipment, talents and funds in the industrial chain according to the resource optimization path planning specifically includes:

[0180] Through the resource state real-time monitor, the geographical position and technical parameters of the idle equipment in the industrial chain, the skill matrix of the talents to be configured, the size and period of the fund to be scheduled are obtained, and the resource state data is formed;

[0181] Based on the resource optimization path planning, the demand-resource matching engine is driven to calculate the equipment technical parameter matching degree, the talent skill matching degree and the fund space-time adaptation degree in combination with the resource state data;

[0182] By comparing the size relationship between the equipment technical parameter matching degree, the talent skill matching degree and the fund space-time adaptation degree and the respective preset threshold values, the idle equipment, talents and funds in the industrial chain are dynamically reconfigured, and the reconfiguration result is obtained.

[0183] In this embodiment, resource state real-time monitors are deployed at each key node of the industrial chain, such as production enterprises, logistics centers, research institutions, etc. These monitors can use sensor, Internet of Things device and other technical means to realize real-time monitoring of idle equipment, talents and funds. For idle equipment, the monitor can be installed on or around the equipment to obtain real-time geographical position information of the equipment, such as determining the latitude and longitude coordinates of the equipment through GPS positioning technology; at the same time, the technical parameters of the equipment are collected, such as the model, specification, production capacity, running state, etc. of the equipment. For talents to be configured, the monitor can obtain the skill matrix information of the talents through the enterprise's internal human resource management system or online learning platform. The skill matrix includes the skills, skill levels, relevant work experience, etc. mastered by the talents. For the fund to be scheduled, the monitor can be connected with the enterprise's financial management system to obtain the size information of the fund, such as the total amount of available funds; and the period information of the fund, such as the arrival time of the fund, the available period, etc.

[0184] The resource state real-time monitor integrates and stores the idle equipment's geographical location and technical parameters, the talent's skill matrix, the size and term of the fund, and other information, forming resource state data.

[0185] In the resource management system, a demand-resource matching engine is deployed, which can calculate the equipment technical parameter matching degree, talent skill fit degree, and fund space-time adaptation degree according to the resource optimization path planning and resource state data.

[0186] For the equipment technical parameter matching degree, the engine compares the technical parameters of the idle equipment with the demand parameters of the equipment in each link of the industrial chain. For example, if a certain production link needs a device with a certain production capacity and precision, the engine will select the device with technical parameters meeting the requirements from the idle equipment, and calculate its matching degree. The matching degree can be evaluated comprehensively according to the similarity of the equipment technical parameters, the degree of meeting the demand, etc.

[0187] For the talent skill fit degree, the engine matches the skill matrix of the talent to be allocated with the skill requirements of each post in the industrial chain. For example, a certain R&D post needs talents with certain professional skills and R&D experience, the engine will find out the personnel with high skill fit degree from the talent to be allocated, and evaluate its fit degree. The fit degree can be measured according to the matching degree of talent skills and post requirements, the degree of skill level, etc.

[0188] For the fund space-time adaptation degree, the engine compares the size and term of the fund with the fund demand of each project in the industrial chain. For example, a certain project needs a certain size of fund support within a certain time, the engine will judge whether the adjustable fund can meet the space-time demand of the project, and calculate the adaptation degree. The adaptation degree can be evaluated according to the matching degree of fund size and demand size, the fit degree of fund term and project cycle, etc.

[0189] According to the actual situation and operation target of the industrial chain, preset thresholds are set for the equipment technical parameter matching degree, talent skill fit degree, and fund space-time adaptation degree. The setting of the preset threshold should consider the scarcity of resources, the importance of demand, cost-effectiveness, etc. For example, for the deployment of key equipment, the preset threshold of the equipment technical parameter matching degree can be set higher to ensure that the equipment can meet the high requirements of production; for the deployment of talents in some non-key posts, the preset threshold of the talent skill fit degree can be appropriately reduced to expand the range of talent selection.

[0190] By comparing the size relationship between the equipment technical parameter matching degree, the talent skill fit degree, and the fund space-time adaptation degree and their respective preset thresholds, the idle equipment, talent, and fund in the industrial chain are dynamically reconfigured.

[0191] If the equipment technical parameter matching degree is higher than the preset matching degree threshold, it indicates that the idle equipment can meet the demand of a certain link in the industrial chain, and it can be allocated to the link for use. For example, an enterprise has an idle precision machining equipment, and the technical parameters thereof have a higher matching degree with the R&D production demand of another enterprise in the industrial chain, and are higher than the preset matching degree threshold, so the equipment can be allocated to the enterprise.

[0192] If the talent skill matching degree is higher than the preset matching degree threshold, it indicates that the talent can be competent for the work of a certain post in the industrial chain, and it can be arranged to the post. For example, the skills possessed by a certain talent have a higher matching degree with the skill requirements of a certain R&D post in the industrial chain, and are higher than the preset matching degree threshold, so the talent can be allocated to the post.

[0193] If the fund space-time adaptation degree is higher than the preset adaptation degree threshold, it indicates that the allocable fund can meet the fund demand of a certain project in the industrial chain, and it can be allocated to the project. For example, the fund demand of a certain project matches the allocable fund in terms of time and scale, and the adaptation degree is higher than the preset adaptation degree threshold, so the fund can be allocated to the project.

[0194] According to the dynamic reconfiguration decision, a reconfiguration result is generated. The reconfiguration result should clearly indicate the allocation direction, allocation time, and allocation quantity of the idle equipment, talent, and fund. For example, the reconfiguration result can include the allocation of a certain idle equipment from A enterprise to B enterprise, the allocation time is [specific date], and the transportation method and handover process of the equipment are explained; the allocation of a certain talent from C department to D post, the allocation time is [specific date], and the work handover and training arrangement of the talent are specified; the allocation of a certain fund from E account to F project, the allocation time is [specific date], and the use supervision and evaluation mechanism of the fund are specified.

[0195] In this embodiment, the utilization efficiency of the resources of the industrial chain can be improved, the operating cost of the enterprise can be reduced, and the collaborative development of the industrial chain can be promoted. By reasonably allocating the idle equipment, the idle time of the equipment can be reduced, and the production efficiency of the equipment can be improved; by optimizing the talent allocation, the skill advantages of the talent can be fully utilized, and the innovation ability of the enterprise can be improved; by scientifically scheduling the fund, the fund chain of the enterprise can be stabilized, and the strategic development of the enterprise can be supported.

[0196] In some embodiments, according to the reconfiguration result of the intelligent scheduling center, the blockchain is called to convert the data asset into a chain-verified collaborative benefit, specifically including:

[0197] Based on the reconfiguration result, the blockchain scheduling instruction chain generates a device ownership transfer smart contract, a talent service smart contract, and a fund flow direction smart contract;

[0198] Based on the cross-agent collaborative executor, according to the equipment ownership transfer smart contract, talent service smart contract and fund flow direction smart contract, the equipment start-stop control, talent cross-enterprise scheduling instruction and fund chain transfer are executed.

[0199] The execution state data of the cross-agent collaborative executor is fed back to the AI decision engine in real time.

[0200] In this embodiment, after the idle equipment, talent and fund are dynamically reconfigured by the intelligent scheduling center, the reconfiguration results are stored and managed in the form of structured data. These data include the ownership information of the equipment (such as the equipment owner, the service period, etc.), the allocation information of the talent (such as the talent source enterprise, the target post, the service period, etc.), and the flow direction information of the fund (such as the fund transfer-out party, the transfer-in party, the amount, the purpose, etc.).

[0201] The block chain scheduling instruction chain analyzes the reconfiguration results and extracts key information to prepare for the subsequent generation of smart contracts. For example, from the equipment reconfiguration results, the unique identification of the equipment, the original owner address, the new owner address, the ownership transfer time, etc. are extracted; from the talent reconfiguration results, the identity information of the talent, the original enterprise identification, the target enterprise identification, the service start and end time, etc. are extracted; from the fund reconfiguration results, the fund account information, the transfer amount, the transfer time, the fund purpose description, etc. are extracted.

[0202] According to the reconfiguration result information after analysis, the block chain scheduling instruction chain triggers the smart contract generation mechanism to generate the equipment ownership transfer smart contract, the talent service smart contract and the fund flow direction smart contract.

[0203] The equipment ownership transfer smart contract clearly specifies the conditions, procedures and responsibilities of the equipment ownership transfer. For example, the contract stipulates that after the conditions of normal equipment state, confirmation by both parties, etc. are met, the ownership of the equipment will be transferred from the original owner to the new owner; at the same time, the rights and obligations of each party in the ownership transfer process are clearly defined, such as the original owner has the obligation to provide complete equipment information, the new owner has the obligation to pay the equipment use fee according to the agreement, etc.

[0204] The talent service smart contract specifies the service content, service period, service compensation and the rights and obligations of both parties of the talent cross-enterprise service. For example, the contract clearly defines the specific work tasks, work duration, service quality standards, etc. that the talent needs to complete in the target enterprise; at the same time, it stipulates that the target enterprise should pay the service fee to the talent original enterprise according to the agreed time and manner, and the talent original enterprise should guarantee the personal safety and legal rights and interests of the talent during the service period, etc.

[0205] The smart contract for fund flow ensures that funds are transferred on-chain according to the requirements of the reallocation results, and specifies the conditions, timing, and methods of fund transfer. For example, the contract stipulates that funds will be transferred from the sender's account to the recipient's account after the project progress requirements and acceptance are met; at the same time, details such as transaction fees and arrival time of fund transfer are clearly defined.

[0206] Deploy cross-entity collaborative executors across various participants in the industry chain (such as enterprises and financial institutions). These executors are capable of communicating with the blockchain network and receiving and executing instructions from smart contracts. Cross-entity collaborative executors need to be integrated with the internal management systems of each entity, such as the enterprise's equipment management system, human resource management system, and financial management system, to directly control equipment startup and shutdown, personnel allocation, and fund transfer operations.

[0207] After receiving the smart contract for equipment ownership transfer, the smart contract for talent services, and the smart contract for fund flow, the cross-entity collaborative executor executes the corresponding instructions according to the contract content.

[0208] Device Start-up and Shutdown Control: For smart contracts involving the transfer of device ownership, cross-entity collaborative executors control the start-up and shutdown of the device according to the ownership transfer time and conditions specified in the contract. For example, after the formal transfer of device ownership to the new owner, the executor sends start-up and shutdown commands to the device to ensure that the device can be used according to the new owner's requirements. Simultaneously, the executor records information such as the device's start-up and shutdown time and operating status, and uploads this information to the blockchain network for subsequent traceability and verification.

[0209] Cross-enterprise talent dispatch command execution: For talent service smart contracts, a cross-entity collaborative executor is responsible for coordinating talent dispatch between the talent's original company and the target company. For example, the executor sends a talent transfer-out notice to the talent's original company and a talent transfer-in notice to the target company, and assists in handling relevant procedures for the talent, such as work handover and social security transfer. During the talent service period, the executor will periodically collect the talent's work performance data and upload it to the blockchain network as the basis for service payment.

[0210] On-chain fund transfer: Based on the smart contract for fund flow, cross-entity collaborative executors execute on-chain fund transfer operations. The executors interact with the payment system on the blockchain to ensure that funds are transferred from the sender's account to the recipient's account according to the time, amount, and method stipulated in the contract. Simultaneously, the executors record detailed information about the fund transfer, such as transaction hash value, transfer time, and transaction fees, and upload this information to the blockchain network for auditing and querying.

[0211] In the process of executing the smart contract, the cross-entity collaborative executor collects execution state data in real time, including device operation state data (such as device working time, fault frequency, maintenance record, etc.), talent service state data (such as talent attendance, work completion progress, customer evaluation, etc.), and fund transfer state data (such as whether the fund has arrived, whether the arrival amount is accurate, etc.).

[0212] The cross-entity collaborative executor feeds back the collected execution state data to the AI decision engine in real time. The feedback method can use technical means such as message queue and API interface to ensure timely data transmission.

[0213] After receiving the execution state data, the AI decision engine analyzes and processes these data. For example, by analyzing the device operation state data, the maintenance needs and remaining service life of the device are predicted; by analyzing the talent service state data, the service quality and performance of the talent are evaluated to provide a reference for subsequent talent allocation; by analyzing the fund transfer state data, the use efficiency and risk situation of the fund are monitored.

[0214] The AI decision engine provides decision support for the intelligent scheduling center based on the analysis results, helping the intelligent scheduling center to optimize the subsequent resource allocation scheme and further improve the collaborative efficiency and income level of the industrial chain.

[0215] In this embodiment, the scheme of converting data assets into chain-verified collaborative income on the blockchain can fully utilize the advantages of blockchain technology to ensure the transparency and credibility of the resource reconfiguration process in the industrial chain, such as equipment, talent, and fund. Equipment ownership transfer, talent cross-enterprise scheduling, and fund transfer on the chain can be recorded and verified on the blockchain, avoiding disputes and risks that may occur in traditional methods. At the same time, the execution state data of the cross-entity collaborative executor is fed back to the AI decision engine in real time, which can realize closed-loop management of data and provide more accurate decision basis for the intelligent scheduling center, promoting the collaborative development and income improvement of the industrial chain.

[0216] In the above embodiments, the description of each embodiment focuses on different aspects, and the parts not described or recorded in a certain embodiment can be referred to the related description of other embodiments.

[0217] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0218] In the embodiments disclosed in the present application, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other manners. For example, the apparatus / terminal device embodiments described above are merely schematic; for example, the division of the modules or units is only a logical function division; there can be another division manner in actual implementation; for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0219] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

Claims

1. A smart platform system for modern industrial system construction, characterized in that, The system specifically comprises: A heterogeneous data fusion hub for real-time collection and standardized processing of multi-source heterogeneous data of enterprise equipment and environment in the industrial chain; A knowledge graph construction module for generating a cross-enterprise and cross-industry real-time operation situation graph based on multi-source heterogeneous data; An AI decision engine for performing risk prediction and resource optimization path planning based on the real-time operation situation graph; An intelligent scheduling center for dynamically reconfiguring idle equipment, talents and funds in the industrial chain based on the resource optimization path planning; An ecological value contract module for converting data assets into verifiable collaborative benefits on the chain by calling the blockchain based on the reconfiguration results of the intelligent scheduling center.

2. The system of claim 1, wherein, The real-time collection and standardized processing of multi-source heterogeneous data of enterprise equipment and environment in the industrial chain specifically comprises: Real-time collection of multi-source heterogeneous raw data streams of enterprise equipment and environment in the industrial chain through an industrial protocol adaptive interface; Input of the multi-source heterogeneous raw data streams into a protocol analysis engine to convert them into intermediate data streams in a unified JSON format; Performing stream quality cleaning on the intermediate data streams, identifying abnormal data through a dynamic threshold rule library, and triggering time series interpolation reconstruction; Injecting the cleaned intermediate data streams into a space-time alignment engine for millisecond-level alignment according to device spatial coordinates and data timestamp; Based on the aligned intermediate data streams, calling the industrial ontology knowledge graph for semantic mapping, and outputting multi-source heterogeneous data.

3. The system of claim 2, wherein, The construction steps of the industrial ontology knowledge graph specifically comprise: Constructing an entity relationship constraint library for storing entity hierarchical relationships and attribute value domain constraints of equipment classes, environment parameter classes and production indicator classes; Loading the entity relationship constraint library into a mapping rule engine to establish a mandatory binding relationship between data fields and graph attribute nodes, and associate with pre-defined industrial ontology type labels to form an industrial ontology knowledge graph; Performing dynamic conflict detection on the input data field stream, and checking the field semantic legality and value domain compliance based on the mapping rule engine; When a conflict is detected, triggering the mapping rule rewriting engine to generate alternative binding rules and updating them to the industrial ontology knowledge graph.

4. The system of claim 3, wherein, The generation of a cross-enterprise and cross-industry real-time operation situation graph based on multi-source heterogeneous data specifically comprises: Based on the entity relationship constraint library, driving a space-time relationship reasoning engine to calculate the physical location association and production process dependency relationship between entities to generate entity relationship data; Based on the entity relationship data, triggering an industrial chain topology modeler to construct a multi-level dynamic topology network, the levels of which include raw material layer, component layer, whole machine assembly layer and service layer; Inputting the multi-level dynamic topology network into a situation graph renderer, encoding node state indicators into color gradient values and relationship weights into connection line width values, and real-time generating a real-time operation situation graph containing node state indicators and relationship weights.

5. The system of claim 1, wherein, The risk prediction and resource optimization path planning based on the real-time operation situation graph specifically comprises: Parsing device load rate, supply chain connection strength and inventory turnover rate dynamic indicators from the real-time operation situation graph to generate a multi-dimensional feature vector; Input the multi-dimensional feature vector into the risk prediction model cluster, perform supply chain disruption probability calculation, policy compliance detection, and capacity fluctuation early warning in parallel, and obtain risk prediction results; Based on the risk prediction results, trigger the resource optimization solver to generate a Pareto optimal solution set of equipment scheduling schemes and fund allocation schemes; When the Pareto optimal solution set conflicts, drive the decision fusion arbitrator to output the final executable instruction set through the weighted voting mechanism.

6. The system of claim 5, wherein, The method comprises the following steps: Through the feature vector intelligent distributor, the multi-dimensional feature vector is split into a supply chain feature subset, a policy compliance feature subset, and a capacity fluctuation feature subset according to a preset mapping rule; The supply chain feature subset is input into a supply chain disruption prediction model, the supply chain disruption probability is calculated based on the logistics delay rate and the supplier node activity, and the first risk prediction result is obtained; The policy compliance feature subset is input into a policy compliance detection model, the clause matching degree analysis of enterprise behavior data and policy knowledge base is performed, and the second risk prediction result is obtained; The capacity fluctuation feature subset is input into a capacity fluctuation early warning model, the time sequence correlation of the equipment load rate and the order saturation degree is analyzed, and the third risk prediction result is obtained; The first risk prediction result, the second risk prediction result, and the third risk prediction result are subjected to confidence weighted aggregation to generate a comprehensive risk prediction result.

7. The system of claim 5, wherein, The method comprises the following steps: Through the multi-objective constraint converter, the risk prediction results are converted into constraint conditions of the resource optimization model, and the equipment scheduling geographic radius constraint and the fund allocation flow rate threshold are loaded; Based on the constraint conditions, drive the non-dominated sorting optimization engine to evolve the equipment scheduling scheme population and the fund allocation scheme population in parallel to obtain a candidate solution set; Perform solution set clustering compression on the candidate solution set, and retain the Pareto front representative solutions of each cluster through scheme similarity calculation; The Pareto front representative solutions are input into an industrial feasibility filter, and the infeasible schemes are filtered out based on the equipment maintenance period and the financial supervision rules to output the Pareto optimal solution set.

8. The system of claim 5, wherein, The method comprises the following steps: Through the conflict detector, identify the objective function conflict items of the equipment scheduling scheme and the fund allocation scheme in the Pareto optimal solution set; For the objective function conflict items, drive the dynamic weight calculator to generate the implementation cost weight, the risk confidence weight, and the benefit urgency weight; Based on the implementation cost weight, the risk confidence weight, and the benefit urgency weight, trigger the multi-dimensional voter to organize the enterprise node, the park management node, and the financial institution node to perform weighted voting to generate a weighted voting result; Based on the weighted voting result, generate an executable instruction set of the fusion scheduling and fund instructions.

9. The system of claim 1, wherein, The method comprises the following steps: Through the resource state real-time monitor, the geographical position and technical parameters of idle equipment in the industrial chain, the skill matrix of the talent to be configured, the size and period of the fund that can be dispatched are obtained to form resource state data; Based on resource optimization path planning, combined with resource state data, the demand-resource matching engine calculates the equipment technical parameter matching degree, the talent skill matching degree and the fund space-time adaptation degree; By comparing the size relationship of the equipment technical parameter matching degree, the talent skill matching degree and the fund space-time adaptation degree with the respective preset threshold value, the idle equipment, talent and fund in the industrial chain are dynamically reconfigured to obtain the reconfiguration result.

10. The system of claim 9, wherein, According to the reconfiguration result of the intelligent dispatching center, the blockchain is called to convert the data assets into on-chain verifiable collaborative benefits, specifically including: Based on the reconfiguration result, the blockchain dispatch instruction chain generates the equipment ownership transfer smart contract, the talent service smart contract and the fund flow direction smart contract; Based on the cross-subject collaborative executor, according to the equipment ownership transfer smart contract, the talent service smart contract and the fund flow direction smart contract, the equipment start-stop control, the talent cross-enterprise dispatch instruction and the fund on-chain transfer are executed; The execution state data of the cross-subject collaborative executor is fed back to the AI decision engine in real time.

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