Supervision cloud platform

Abstract

The invention discloses a supervision cloud platform, and relates to the field of data processing. The platform comprises an interaction terminal, a server and a control terminal, and aims to solve the problems of data isomerism, low processing efficiency, non-visual presentation and the like in an existing supervision system. The core of the platform is that the control terminal can respond to a request of the interaction terminal and determine target resource data from the server; dynamically generating a matched data processing path based on the data type, wherein the path defines an operation module for sequential execution; the control terminal activates a path and calls a module to process the data to generate a structured data packet; and finally, the data packet is routed to the interaction terminal which initiates the request for visual rendering. Through an intelligent data processing and routing mechanism, deep fusion, efficient processing and visual presentation of multi-source data are realized, and the intelligent level and decision-making efficiency of project supervision are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a supervision cloud platform. Background Technology

[0002] In large-scale engineering construction projects, supervision involves comprehensive monitoring and management of various aspects such as project quality, progress, investment, and safety. Traditional supervision models heavily rely on manual on-site inspections, paper records, and fragmented information systems, leading to a series of prominent problems. First, supervision data comes from diverse sources, including IoT sensors, BIM (Building Information Modeling) databases, and project management software. These data formats and standards vary, resulting in severe data heterogeneity and data silos, making effective integration and correlation analysis difficult. The system lacks adaptive processing mechanisms for different types of data requests, failing to dynamically optimize data processing paths and resource allocation based on data characteristics, system load, and user needs. This results in unintelligent resource scheduling, easily causing system performance bottlenecks and impacting user experience. Summary of the Invention

[0003] This application proposes a cloud platform for supervision to improve the above-mentioned problems.

[0004] This application provides a supervision cloud platform, including multiple interactive terminals, a server, and a control terminal. The supervision cloud platform is configured as follows: The control terminal responds to the data request command from the interactive terminal and determines the target resource data from the server; Based on the target resource data type, the control terminal generates a data processing path that matches the target resource data type. The data processing path defines multiple sequentially executed operation modules for accessing, extracting, transforming, and encapsulating the target resource data. The control terminal activates the data processing path, sequentially calls the operation modules to process the target resource data, and generates structured data packets; The control terminal routes the structured data packets to the interactive terminal that initiated the data request command for visualization rendering.

[0005] In some implementations, the control terminal includes a data acquisition module, which is configured to: Establish connections with external data sources through data interfaces, including IoT sensor networks, BIM model databases, and project schedule management software APIs. Obtain the resource data pattern stored in the server, and perform format verification on the heterogeneous raw data obtained from external data sources. The heterogeneous raw data includes at least sensor readings, 3D model component information, and working time records. The validated heterogeneous raw data is converted into standardized data units that conform to the resource data model, and timestamps and data source identifiers are added. Standardized data units are written to the server asynchronously.

[0006] In some implementations, a dynamic index generation module is deployed in the server, and the dynamic index generation module is configured as follows: Retrieve newly added or updated standardized data units from the server and extract their key attribute values, including project phase identifiers, spatial location coordinates, data type labels, and associated responsible party information. According to the preset weighting rules, an index weight value is assigned to each key attribute value. The weighting rules are adjusted based on the frequency and relevance of the key attribute value in historical data request instructions. Based on key attribute values ​​and their corresponding index weight values, a multidimensional inverted index structure is constructed. The multidimensional inverted index structure is used to map key attribute values ​​to physical addresses that store standardized data units.

[0007] In some implementations, the control terminal further includes a resource tagging module, which is configured to: Obtain the content characteristics of structured data packets, including data dimensions, data volume, and associated data sensitivity levels; Based on content characteristics, select one or more tagging strategies from a predefined tagging strategy library. Tagging strategies include watermark embedding, digital signature generation, or data desensitization rules. The selected tagging strategy is applied to process structured data packets and generate tagged data packets with unique identifiers and / or access control information; The control terminal routes the tagged data packets to the interactive terminal.

[0008] In some implementations, the supervision cloud platform is configured as follows: Based on the target resource data type, the control terminal generates a data processing path that matches the target resource data type, including: Determine the type of the target resource data, and based on the type, retrieve the corresponding initial data processing path template from the database; Obtain the current system load status, the priority identifier carried in the data request command, and the device performance parameters of the interactive terminal; Based on system load status, priority identifier, and device performance parameters, the execution parameters of the operation modules in the initial data processing path template are adjusted to generate the final data processing path. The adjustments include modifying the parallel processing threshold, selecting a caching strategy, or setting the data compression rate.

[0009] In some implementations, the supervision cloud platform is configured as follows: The platform also includes a cache management layer, which is distributed across the control terminal and / or server and configured as follows: Analyze historical data request patterns to identify frequently requested resource types and key parameter combinations that have a high predictable access probability. For the identified resource data, after it is processed to generate structured data packets, the cache management layer decides whether to store its copy in a multi-layer cache structure according to a predefined cache policy. The multi-layer cache structure includes server memory cache, control terminal local cache, and edge computing node cache. When a new data request instruction arrives, the control terminal first queries the multi-layer cache structure. If a valid cache copy exists and meets the freshness requirements, it directly calls the cache copy to generate a response.

[0010] In some implementations, the control terminal integrates a user behavior analyzer, which is configured to: Continuously collect and analyze data request command sequences from various interactive terminals, and extract the operational habit characteristics of each user or user group. These characteristics include frequently requested data types, access time patterns, and commonly used combinations of filtering conditions. Based on the extracted operational habit features, construct or update the user profile model; When a new data request instruction is received, the potential intent of the instruction is predicted by combining the corresponding user profile model. When generating a data processing path based on the type of target resource data, the operation modules in the path are pre-optimized with reference to the predicted potential intent. Pre-optimization includes preloading relevant data that may be requested later or adjusting the granularity of data aggregation.

[0011] A second aspect of this invention provides a data processing method applied to a data processing system, the data processing system including multiple interactive terminals, a server, and a control terminal, comprising: In response to a data request command from an interactive terminal, the target resource data is determined from the server; Based on the target resource data type, a data processing path matching the target resource data type is generated. The data processing path defines multiple sequentially executed operation modules for accessing, extracting, transforming, and encapsulating the target resource data. The data processing path is activated, and the operation modules are called sequentially to process the target resource data and generate a structured data packet. The structured data packets are routed to the interactive terminal that initiated the data request command for visualization rendering.

[0012] A third aspect of this invention provides an electronic device, which includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method proposed in the first aspect of the present invention.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in the first aspect of the present invention.

[0014] In summary, the above platform has the following technical effects: This invention discloses a cloud platform for project supervision, comprising an interactive terminal, a server, and a control terminal. It aims to address issues such as data heterogeneity, low processing efficiency, and unintuitive presentation in existing project supervision systems. The core of the platform lies in the control terminal's ability to respond to requests from the interactive terminal, determining the target resource data from the server; subsequently, it dynamically generates a matching data processing path based on the data type, defining sequentially executed operation modules; the control terminal activates the path and calls the modules to process the data, generating a structured data packet; finally, it routes this data packet to the requesting interactive terminal for visualization rendering. This invention, through intelligent data processing and routing mechanisms, achieves deep fusion, efficient processing, and intuitive presentation of multi-source data, significantly improving the intelligence level and decision-making efficiency of project supervision. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a data processing method proposed in an embodiment of this application. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] This application proposes a supervision cloud platform, including multiple interactive terminals, a server, and a control terminal. The supervision cloud platform is configured as follows: The control terminal responds to the data request command from the interactive terminal and determines the target resource data from the server.

[0018] The control terminal receives data request instructions from the interactive terminal and accurately locates the required target resource data from the server according to the instructions.

[0019] Based on the target resource data type, the control terminal generates a data processing path that matches the target resource data type. The data processing path defines multiple sequentially executed operation modules for accessing, extracting, transforming, and encapsulating the target resource data.

[0020] Understandably, the control terminal identifies the specific type of the target resource data and dynamically generates a data processing path that closely matches that type. This path explicitly defines multiple operation modules that must be executed sequentially, including standardized operations such as data access, extraction, transformation, and encapsulation.

[0021] The control terminal activates the data processing path, sequentially calls the operation modules to process the target resource data, and generates structured data packets.

[0022] Understandably, the control terminal activates and executes the data processing path, sequentially calling each operation module defined in the path to process the target resource data. After this series of standardized operations, a well-structured and uniformly formatted structured data packet is finally generated.

[0023] The control terminal routes the structured data packets to the interactive terminal that initiated the data request command for visualization rendering.

[0024] Understandably, the control terminal accurately sends this structured data packet back to the interactive terminal that initially initiated the request through its internal routing mechanism. Upon receiving it, the interactive terminal can parse the data packet and perform visualization rendering, presenting the data intuitively to the user. By dynamically generating processing paths that match the data type, the flexibility and efficiency of the data processing workflow are achieved, improving the platform's response speed and service quality to diverse supervision needs.

[0025] Optionally, the control terminal includes a data acquisition module, which is configured as follows: It establishes connections with external data sources through data interfaces, including IoT sensor networks, BIM model databases, and project schedule management software APIs.

[0026] Obtain the resource data pattern stored in the server, and perform format verification on the heterogeneous raw data obtained from external data sources. The heterogeneous raw data includes at least sensor readings, 3D model component information, and working time records. The validated heterogeneous raw data is converted into standardized data units that conform to the resource data model, and timestamps and data source identifiers are added. Standardized data units are written to the server asynchronously.

[0027] Understandably, this module establishes communication connections with various external data sources through pre-defined data interfaces. These data sources cover the main data types in engineering supervision, including: IoT sensor networks: providing real-time physical data of the site environment (such as temperature, humidity, displacement, and stress); BIM model databases: providing static model data such as the three-dimensional geometric information and component attributes of buildings; and project schedule management software APIs: providing business management data such as project plans, work hour records, and approval processes. This establishes data channels from different sources, achieving initial data aggregation.

[0028] Optionally, a dynamic index generation module is deployed on the server, and the dynamic index generation module is configured as follows: Retrieve newly added or updated standardized data units from the server and extract their key attribute values, including project phase identifiers, spatial location coordinates, data type labels, and associated responsible party information. According to the preset weighting rules, an index weight value is assigned to each key attribute value. The weighting rules are adjusted based on the frequency and relevance of the key attribute value in historical data request instructions. Based on key attribute values ​​and their corresponding index weight values, a multidimensional inverted index structure is constructed. The multidimensional inverted index structure is used to map key attribute values ​​to physical addresses that store standardized data units.

[0029] Whenever a new standardized data unit is stored on the server or existing data is updated, this module immediately intervenes, automatically extracting the core feature information of the data, i.e., key attribute values. These attributes are designed to cover the core query dimensions of engineering supervision. The module does not treat all attribute values ​​equally; instead, it assigns a weight value to each attribute value according to a set of adjustable weighting rules. This allows the index structure to learn user query habits, giving higher priority and faster speed to frequent and important query conditions during retrieval.

[0030] Optionally, the control terminal also includes a resource tagging module, which is configured to: Obtain the content characteristics of structured data packets, including data dimensions, data volume, and associated data sensitivity levels; Based on content characteristics, select one or more tagging strategies from a predefined tagging strategy library. Tagging strategies include watermark embedding, digital signature generation, or data desensitization rules. The selected tagging strategy is applied to process structured data packets and generate tagged data packets with unique identifiers and / or access control information; The control terminal routes the tagged data packets to the interactive terminal.

[0031] Understandably, after the structured data packet is generated, this module first performs intelligent analysis on its content to evaluate key features. Based on the content features analyzed in the first step, the module automatically selects one or more of the most suitable tagging strategies from a predefined policy library. Through intelligent analysis, policy selection, and security processing, refined security management of data distribution is achieved, effectively preventing the risks of data leakage, tampering, and misuse during terminal use, and improving the security of the data lifecycle.

[0032] Optionally, the supervision cloud platform is configured as follows: Based on the target resource data type, the control terminal generates a data processing path that matches the target resource data type, including: Determine the type of the target resource data, and based on the type, retrieve the corresponding initial data processing path template from the database; Obtain the current system load status, the priority identifier carried in the data request command, and the device performance parameters of the interactive terminal; Based on system load status, priority identifier, and device performance parameters, the execution parameters of the operation modules in the initial data processing path template are adjusted to generate the final data processing path. The adjustments include modifying the parallel processing threshold, selecting a caching strategy, or setting the data compression rate.

[0033] Optionally, the supervision cloud platform is configured as follows: The platform also includes a cache management layer, which is distributed across the control terminal and / or server and configured as follows: Analyze historical data request patterns to identify frequently requested resource types and key parameter combinations that have a high predictable access probability. For the identified resource data, after it is processed to generate structured data packets, the cache management layer decides whether to store its copy in a multi-layer cache structure according to a predefined cache policy. The multi-layer cache structure includes server memory cache, control terminal local cache, and edge computing node cache. When a new data request instruction arrives, the control terminal first queries the multi-layer cache structure. If a valid cache copy exists and meets the freshness requirements, it directly calls the cache copy to generate a response.

[0034] Optionally, the control terminal integrates a user behavior analyzer, which is configured as follows: Continuously collect and analyze data request command sequences from various interactive terminals, and extract the operational habit characteristics of each user or user group. These characteristics include frequently requested data types, access time patterns, and commonly used combinations of filtering conditions. Based on the extracted operational habit features, construct or update the user profile model; When a new data request instruction is received, the potential intent of the instruction is predicted by combining the corresponding user profile model. When generating a data processing path based on the type of target resource data, the operation modules in the path are pre-optimized with reference to the predicted potential intent. Pre-optimization includes preloading relevant data that may be requested later or adjusting the granularity of data aggregation.

[0035] This invention discloses a cloud platform for project supervision, relating to the field of data processing. The platform includes an interactive terminal, a server, and a control terminal, aiming to solve problems such as data heterogeneity, low processing efficiency, and unintuitive presentation in existing project supervision systems. The core of the platform lies in the control terminal's ability to respond to requests from the interactive terminal, determining the target resource data from the server; subsequently, it dynamically generates a matching data processing path based on the data type, defining sequentially executed operation modules; the control terminal activates the path and calls the modules to process the data, generating a structured data packet; finally, the data packet is routed to the requesting interactive terminal for visualization rendering. This invention, through intelligent data processing and routing mechanisms, achieves deep fusion, efficient processing, and intuitive presentation of multi-source data, significantly improving the intelligence level and decision-making efficiency of project supervision.

[0036] Based on the same inventive concept, embodiments of this application also propose a data processing method; please refer to [link to relevant documentation]. Figure 1 This is applied to a data processing system, which includes multiple interactive terminals, a server, and a control terminal, including: S101: In response to the data request command from the interactive terminal, determine the target resource data from the server; S102: Based on the target resource data type, generate a data processing path that matches the target resource data type. The data processing path defines multiple sequentially executed operation modules for accessing, extracting, transforming, and encapsulating the target resource data. S103: Activate the data processing path, sequentially call the operation modules to process the target resource data, and generate a structured data packet; S104: The structured data packet is routed to the interactive terminal that initiated the data request command for visualization rendering.

[0037] Based on the same inventive concept, embodiments of this application also propose an electronic device, which includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the automatic overheat protection method based on the universal testing machine according to the embodiments of this application.

[0038] In addition, to achieve the above objectives, embodiments of this application also propose a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the automatic overheat protection method based on a universal testing machine according to embodiments of this application.

[0039] The following is a detailed introduction to the various components of the electronic device: In this context, the processor is the control center of the electronic device. It can be a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0040] Alternatively, the processor can perform various functions of the electronic device by running or executing software programs stored in memory and by calling data stored in memory.

[0041] The memory is used to store the software program that executes the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, which will not be repeated here.

[0042] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processor through the interface circuit of the electronic device; the embodiments of the present invention do not specifically limit this.

[0043] A transceiver is used to communicate with network devices or with terminal devices.

[0044] Optionally, the transceiver may include a receiver and a transmitter. The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0045] Optionally, the transceiver can be integrated with the processor or exist independently and coupled to the processor through the router's interface circuit. This embodiment of the invention does not specifically limit this.

[0046] Furthermore, the technical effects of the electronic device can be referred to the technical effects of the data transmission method in the above method embodiments, and will not be repeated here.

[0047] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0048] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0049] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0050] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0051] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0052] It should be understood that, in various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0053] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

Claims

1. A supervisory cloud platform, characterized by, The supervision cloud platform comprises a plurality of interactive terminals, a server and a control terminal, and is configured to: The control terminal determines target resource data from the server in response to a data request instruction of the interactive terminal; The control terminal generates a data processing path matched with the target resource data type based on the target resource data type, wherein the data processing path defines a plurality of sequentially executed operation modules for accessing, extracting, transforming and packaging the target resource data; The control terminal activates the data processing path, sequentially calls the operation modules to process the target resource data, and generates a structured data package; The control terminal routes the structured data package to the interactive terminal initiating the data request instruction for visual rendering.

2. The supervisory cloud platform of claim 1, wherein, The control terminal comprises a data acquisition module configured to: connect with external data sources through a data interface, wherein the external data sources include an Internet of Things sensor network, a BIM model database and an API of project progress management software; obtain a resource data mode stored in the server, and perform format verification on heterogeneous raw data obtained from the external data sources, wherein the heterogeneous raw data at least includes sensor readings, three-dimensional model component information and work record; convert the heterogeneous raw data that passes the verification into standardized data units conforming to the resource data mode, and add timestamps and data source identifiers; asynchronously write the standardized data units into the server.

3. The supervisory cloud platform of claim 2, wherein, The server is deployed with a dynamic index generation module configured to: obtain the standardized data units newly added or updated in the server, and extract key attribute values thereof, wherein the key attribute values include project phase identifiers, spatial position coordinates, data type labels and associated responsible party information; assign an index weight value to each key attribute value according to a preset weight rule, wherein the weight rule is adjusted based on the frequency and correlation of the key attribute value in historical data request instructions; construct a multi-dimensional inverted index structure based on the key attribute values and their corresponding index weight values, wherein the multi-dimensional inverted index structure is used to map the key attribute values to physical addresses where the standardized data units are stored.

4. The supervisory cloud platform of claim 3, wherein, The control terminal further comprises a resource marking module configured to: obtain content features of the structured data package, wherein the content features include data dimensions, data size and associated data sensitivity levels; select one or more marking strategies from a pre-defined marking strategy library based on the content features, wherein the marking strategies include watermark embedding, digital signature generation or data desensitization rules; apply the selected marking strategies to process the structured data package, and generate a marked data package with unique identifiers and / or permission control information; The control terminal routes the marked data package to the interactive terminal.

5. The supervisory cloud platform of claim 1, wherein, The supervision cloud platform is configured to: The control terminal generates a data processing path matched with the target resource data type based on the target resource data type, including: determining the type of the target resource data, and based on the type, calling a corresponding initial data processing path template from a database; obtaining the current system load state, the priority identifier carried in the data request instruction, and the device performance parameter of the interactive terminal; based on the system load state, the priority identifier, and the device performance parameter, adjusting the execution parameter of the operation module in the initial data processing path template to generate the data processing path for final execution, the adjustment including parallel processing threshold modification, cache strategy selection, or data compression rate setting.

6. The supervisory cloud platform of claim 1, wherein, The supervision cloud platform is configured to: The platform further includes a cache management layer distributed in the control terminal and / or server, which is configured to: analyze the mode of historical data request instructions, and identify the resource data type and key parameter combination that is frequently requested or has a high predicted access probability; for the identified resource data, after it is processed to generate the structured data packet, the cache management layer decides whether to store a copy thereof in a multi-layer cache structure according to a predefined cache strategy, the multi-layer cache structure including a server memory cache, a control terminal local cache, and an edge computing node cache; when a new data request instruction arrives, the control terminal first queries the multi-layer cache structure, and if there is a valid cache copy and the freshness requirement is met, the cache copy is directly called to generate a response.

7. The supervisory cloud platform of claim 1, wherein, The control terminal is integrated with a user behavior analyzer, which is configured to: continuously collect and analyze data request instruction sequences from each interactive terminal, extract operation habit features of each user or user group, including frequently requested data types, access time rules, and frequently used filtering condition combinations; based on the extracted operation habit features, build or update a user portrait model; when a new data request instruction is received, the potential intent of the instruction is predicted in combination with the portrait model of the corresponding user; when generating a data processing path based on the type of the target resource data, refer to the predicted potential intent to pre-optimize the operation modules in the path, the pre-optimization including pre-loading related data that may be requested subsequently or adjusting the granularity of data aggregation.

8. A data processing method, characterized by, Applied to a data processing system including a plurality of interactive terminals, a server, and a control terminal, the method includes: determining target resource data from the server in response to a data request instruction of the interactive terminal; generating a data processing path matched with the target resource data type based on the target resource data type, wherein the data processing path defines a plurality of sequentially executed operation modules for accessing, extracting, transforming, and packaging the target resource data; activating the data processing path, sequentially calling the operation modules to process the target resource data, and generating a structured data packet; routing the structured data packet to the interactive terminal that initiated the data request instruction for visual rendering.

9. An electronic device, comprising: comprising: at least one processor; and a memory in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, execution of the instructions by the at least one processor enables the at least one processor to perform a data processing method as claimed in claim 8.

10. A computer-readable storage medium, characterized in that, a computer program stored thereon, which program, when executed by a processor, implements a data processing method as claimed in claim 8.

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