An HCPS data weaving system

Abstract

The present application provides a human-information-physical system (HCPS) data weaving system, comprising an HCPS data integration device, an HCPS model management device, and an HCPS data weaving bus and field device. The HCPS data integration device is used to provide a tidal big data warehousing capability, a multi-source data computing link QoS control capability, and a monitoring operation and maintenance automation capability to the HCPS data weaving bus; the HCPS model management device is used to provide a basic management capability of a model, an access control capability of the model, and a model falling link QoS control capability to the HCPS data weaving bus; and the HCPS data weaving bus is used to provide a data asset management capability, a bottom computing storage engine connectivity control, a real-time processing QoS control, a visual use and inspection function to a data weaving user. The present application realizes the fusion of HCPS personal space data flow, information space data flow and physical space data flow, and realizes consistent determination and active and passive checking operations on multi-dimensional data, and provides an HCPS data asset operation method.

Description

An HCPS data weaving system Technical Field

[0001] This invention relates to the field of data management, and in particular to an HCPS data weaving system. Background Technology

[0002] As enterprises accelerate their digital transformation, Human-Cyber-Physical Systems (HCPS) have seen rapid development across various industries, and new architectures such as Cyber-Physical-Social Systems (CPSS) and Digital Twins are also emerging rapidly, posing new requirements for data asset management. Currently, the mainstream opinion in both academia and industry agrees on the concept of "data as a product." With the development of big data analytics and data operations management technologies, data productization is gradually maturing. Having progressed through the technological development paths of data centers, data platforms, data lakes, and data weaving, data weaving within HCPS has become a necessary choice for data asset operation. Currently, academic research mainly focuses on data cataloging, data governance, data integration, and data orchestration in data weaving methods. With the integration and evolution of digital technologies, practical cases of data weaving methods have emerged in the industry, such as the Dutch Turku urban data weaving platform. The mainstream graph database platform Neo4j introduced the concept of data weaving starting from version 4.0. However, current cases and platforms have not conducted much research on the integrated architecture of personal space data flow, information space data flow, and physical space data flow, and further research is needed on HCPS data asset operation methods. Summary of the Invention

[0003] This invention provides an HCPS data weaving system that integrates personal space data streams, information space data streams, and physical space data streams. It performs consistency determination and active / passive verification on multi-dimensional data and provides an HCPS data asset operation method.

[0004] The technical solution adopted in this invention is:

[0005] An HCPS data weaving system, the system comprising an HCPS data integration device, an HCPS model management device, an HCPS data weaving bus, and field devices.

[0006] The HCPS data integration device is used to provide the HCPS data weaving bus with the ability to handle tidal big data ingestion, QoS control of multi-source data computing links, and automated monitoring and maintenance capabilities.

[0007] The HCPS model management device is used to provide basic model management capabilities, model access control capabilities, and model drop link QoS control capabilities to the HCPS data weaving bus.

[0008] The HCPS data weaving bus provides data asset management capabilities, connectivity control of the underlying computing and storage engine, real-time processing QoS control, visualization and verification functions to data weaving users.

[0009] Furthermore, the QoS control includes five parameters: integrity, accuracy, real-time performance, security, and reliability.

[0010] Furthermore, the hardware and software of each module in the HCPS data integration device, HCPS model management device, HCPS data weaving bus and field device define the fusion of HCPS personal space data flow, information space data flow and physical space data flow, realize consistency judgment and active and passive verification of multi-dimensional data, and provide an HCPS data asset operation method.

[0011] Furthermore, the HCPS data integration device includes a tidal big data ingestion module, a multi-source data computing link QoS control module, and a monitoring and maintenance automation module.

[0012] The HCPS data integration device can provide unified access to field sensing devices from different manufacturers.

[0013] The HCPS data integration device has the capability to process business data hotspots, adding data business semantics to general monitoring information such as the number of reducers, JVM memory, and hash keys, enabling business-level data hotspot monitoring, as well as automatic problem location and handling.

[0014] The HCPS data integration device has the capability to isolate business faults. It sets a business fault isolation baseline for situations such as JVM memory overflow, single node utilization exceeding the threshold, and mapredue task number exceeding the threshold, and has the capability to perform preprocessing on the connected business systems.

[0015] The HCPS data integration device has the ability to adapt to business and computing power. When facing multi-task processing scenarios, it can allocate data platform resources according to preset business priorities to ensure the bottom line of business.

[0016] Furthermore, the HCPS model management device includes a cross-domain fused HCPS model, a basic management module for the model, an access control module for the model, and a QoS control module for the model drop link.

[0017] The HCPS model management device has full model coverage dimensions, supports traditional relational models, time series models, wide table models, document models, and graph models, and can support data applications of archival, measurement, analysis, and presentation types.

[0018] The HCPS model management device has access control capabilities for the model and extends the RBAC model to the business layer, enabling access control of data based on user organizational structure and ensuring secure sharing of user data.

[0019] The HCPS model management device has QoS control capabilities for the model drop link, and supports the verification of the timeliness, integrity and correctness of the push of data service links at the source end, middle platform and application layers.

[0020] Furthermore, the HCPS data weaving bus includes a weaving display management module, a weaving target management module, a data asset management module, an underlying computing and storage engine connectivity control module, a real-time processing QoS control module, and a visualization, usage, and verification module.

[0021] The HCPS data weaving bus weaves data into personal space elements, information space elements, and physical space elements according to the user's weaving goals.

[0022] The HCPS data weaving bus has connectivity with underlying computing and storage engines, supporting the following computing and storage engines: Spark, Hive, Python, ElasticSearch, DWS, Flink, HBase, JDBC, and Shell, and the following scripting languages: Spark SQL, HiveQL, Python, Shell, Pyspark, R, Scala, and JDBC.

[0023] The HCPS data weaving bus has visualization capabilities, is geared towards ordinary users, and visually displays data product elements such as data sources, data models, and data results. It also supports the generation of data products using drag-and-drop methods.

[0024] Furthermore, the HCPS data weaving system also includes a data verification device, which specifically includes:

[0025] The data acquisition module is used to acquire measurement data within a preset time period within a data window.

[0026] The measurement module is used to calculate the variance of the measurement data within the preset time period;

[0027] The detection module is used to compare the variance with a first preset threshold to obtain a comparison result;

[0028] The results module is used to evaluate the quality of the measurement data based on the comparison results;

[0029] The data acquisition module acquires measurement data within a preset time period by issuing a request to download measurement data.

[0030] Detect the type of the measurement data packet to be downloaded in the measurement data download request;

[0031] Detect the current bandwidth utilization of the communication network;

[0032] Determine the transmission status of the measurement data packet to be downloaded based on its type or bandwidth utilization.

[0033] If the measurement data packet to be downloaded belongs to a specific type, then the measurement data packet to be downloaded is sent.

[0034] If the type of the measurement data packet to be downloaded does not belong to a specific type, and the bandwidth utilization rate is greater than or equal to a first preset value, then:

[0035] At preset time intervals, a measurement data packet download request is sent to the server until the measurement data packet is downloaded.

[0036] Furthermore, the result module is used to evaluate the quality of the measurement data based on the comparison results, specifically including: if the comparison results show that the variance is greater than a first preset threshold, then the measurement data is evaluated as high-quality data;

[0037] If the comparison results show that the variance is not greater than the first preset threshold, then the measurement data that is not greater than the first preset threshold is regarded as suspicious data.

[0038] Suspicious data is stored in a suspicious database, and the correlation between each suspicious data and other data is detected.

[0039] If the correlation is high, the suspicious data is high-quality data; if the correlation is low, the suspicious data is low-quality data.

[0040] The detection of the correlation between each suspicious data point and other data specifically includes:

[0041] Calculate the correlation coefficient between each suspicious data point and the other data points;

[0042] The absolute value of the correlation coefficient is compared with the second preset threshold to determine the magnitude of the correlation between each suspicious data and other data.

[0043] Based on the comparison results, determine the correlation between each suspicious data point and other data, specifically including:

[0044] If the absolute value of the correlation coefficient is not less than the second preset threshold, then the suspicious data is determined to have a high correlation with other data.

[0045] If the absolute value of the correlation coefficient is less than the second preset threshold, it is determined that the suspicious data has a low correlation with other data.

[0046] The beneficial effects of this invention are:

[0047] This invention integrates HCPS personal space data streams, information space data streams, and physical space data streams, and performs consistency determination and active / passive verification on multi-dimensional data, providing a method for HCPS data asset operation. In summary, this invention will further promote the commercialization and practical application of HCPS data assets.

[0048] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0049] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0050] Figure 1 shows a schematic diagram of an HCPS data weaving method provided by an embodiment of the present invention;

[0051] Figure 2 shows a schematic diagram of a specific example of an HCPS data weaving system provided by an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 embodiments of the present invention, not all embodiments. 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.

[0053] An HCPS data weaving system includes an HCPS data integration device, an HCPS model management device, an HCPS data weaving bus, and field devices.

[0054] The HCPS data integration device is used to provide the HCPS data weaving bus with the ability to handle tidal big data ingestion, QoS control of multi-source data computing links, and automated monitoring and maintenance capabilities.

[0055] The HCPS model management device is used to provide basic model management capabilities, model access control capabilities, and model drop link QoS control capabilities to the HCPS data weaving bus.

[0056] The HCPS data weaving bus provides data asset management capabilities, connectivity control of the underlying computing and storage engine, real-time processing QoS control, visualization and verification functions to data weaving users.

[0057] The software and hardware of each module in the HCPS data integration device, HCPS model management device, HCPS data weaving bus and field device define the fusion of HCPS personal space data flow, information space data flow and physical space data flow, realize consistency judgment and active and passive verification of multi-dimensional data, and provide a method for HCPS data asset operation.

[0058] The HCPS data integration device includes a tidal big data ingestion module, a multi-source data computing link QoS control module, and a monitoring and maintenance automation module.

[0059] The HCPS data integration device can provide unified access to field sensing devices from different manufacturers.

[0060] The HCPS data integration device has the capability to process business data hotspots, adding data business semantics to general monitoring information such as the number of reducers, JVM memory, and hash keys, enabling business-level data hotspot monitoring, as well as automatic problem location and handling.

[0061] The HCPS data integration device has the capability to isolate business faults. It sets a business fault isolation baseline for situations such as JVM memory overflow, single node utilization exceeding the threshold, and mapredue task number exceeding the threshold, and has the capability to perform preprocessing on the connected business systems.

[0062] The HCPS data integration device has the ability to adapt to business and computing power. When facing multi-task processing scenarios, it can allocate data platform resources according to preset business priorities to ensure the bottom line of business.

[0063] The HCPS model management device includes a cross-domain fused HCPS model, a basic management module for the model, an access control module for the model, and a QoS control module for the model drop link.

[0064] The HCPS model management device has full model coverage dimensions, supports traditional relational models, time series models, wide table models, document models, and graph models, and can support data applications of archival, measurement, analysis, and presentation types.

[0065] The HCPS model management device has access control capabilities for the model and extends the RBAC model to the business layer, enabling access control of data based on user organizational structure and ensuring secure sharing of user data.

[0066] The HCPS model management device has QoS control capabilities for the model drop link, and supports the verification of the timeliness, integrity and correctness of the push of data service links at the source end, middle platform and application layers.

[0067] The HCPS data weaving bus includes a weaving display management module, a weaving target management module, a data asset management module, an underlying computing and storage engine connectivity control module, a real-time processing QoS control module, and a visualization, usage, and verification module.

[0068] The HCPS data weaving bus weaves data into personal space elements, information space elements, and physical space elements according to the user's weaving goals.

[0069] The HCPS data weaving bus has connectivity with underlying computing and storage engines, supporting the following computing and storage engines: Spark, Hive, Python, ElasticSearch, DWS, Flink, HBase, JDBC, and Shell, and the following scripting languages: Spark SQL, HiveQL, Python, Shell, Pyspark, R, Scala, and JDBC.

[0070] The HCPS data weaving bus has visualization capabilities, is geared towards ordinary users, and visually displays data product elements such as data sources, data models, and data results. It also supports the generation of data products using drag-and-drop methods.

[0071] The HCPS data weaving system also includes a data verification device, which specifically includes:

[0072] The data acquisition module is used to acquire measurement data within a preset time period within a data window.

[0073] The measurement module is used to calculate the variance of the measurement data within the preset time period;

[0074] The detection module is used to compare the variance with a first preset threshold to obtain a comparison result;

[0075] The results module is used to evaluate the quality of the measurement data based on the comparison results;

[0076] The data acquisition module acquires measurement data within a preset time period by issuing a request to download measurement data.

[0077] Detect the type of the measurement data packet to be downloaded in the measurement data download request;

[0078] Detect the current bandwidth utilization of the communication network;

[0079] Determine the transmission status of the measurement data packet to be downloaded based on its type or bandwidth utilization.

[0080] If the measurement data packet to be downloaded belongs to a specific type, then the measurement data packet to be downloaded is sent.

[0081] If the type of the measurement data packet to be downloaded does not belong to a specific type, and the bandwidth utilization rate is greater than or equal to a first preset value, then:

[0082] At preset time intervals, a measurement data packet download request is sent to the server until the measurement data packet is downloaded.

[0083] The result module is used to evaluate the quality of the measurement data based on the comparison results, specifically including: if the comparison results show that the variance is greater than a first preset threshold, then the measurement data is evaluated as high-quality data;

[0084] If the comparison results show that the variance is not greater than the first preset threshold, then the measurement data that is not greater than the first preset threshold is regarded as suspicious data.

[0085] Suspicious data is stored in a suspicious database, and the correlation between each suspicious data and other data is detected.

[0086] If the correlation is high, the suspicious data is high-quality data; if the correlation is low, the suspicious data is low-quality data.

[0087] The detection of the correlation between each suspicious data point and other data specifically includes:

[0088] Calculate the correlation coefficient between each suspicious data point and the other data points;

[0089] The absolute value of the correlation coefficient is compared with the second preset threshold to determine the magnitude of the correlation between each suspicious data and other data.

[0090] Based on the comparison results, determine the correlation between each suspicious data point and other data, specifically including:

[0091] If the absolute value of the correlation coefficient is not less than the second preset threshold, then the suspicious data is determined to have a high correlation with other data.

[0092] If the absolute value of the correlation coefficient is less than the second preset threshold, it is determined that the suspicious data has a low correlation with other data.

[0093] Based on any of the above embodiments, the QoS control further includes five parameters: integrity, accuracy, real-time performance, security, and reliability.

[0094] Furthermore, the HCPS data integration device can uniformly access field sensing devices from different manufacturers.

[0095] Based on any of the above embodiments, furthermore, through the software definition of each model in the HCPS model management device, the integration of physical space, digital space, and personal space is realized, providing a data weaving method in human-information-physical space.

[0096] This invention provides an HCPS data weaving system, including an HCPS data integration device, an HCPS model management device, an HCPS data weaving bus, and field devices. The technical effects of this invention are achieved as follows:

[0097] As shown in Figure 1, an HCPS data weaving method includes the following steps:

[0098] In S101, the HCPS data integration device of the present invention provides the HCPS data braiding bus with tidal big data ingestion capability, multi-source data computing link QoS control capability, and monitoring and maintenance automation capability. QoS control includes five parameters: integrity, accuracy, real-time performance, security, and reliability. After receiving the above information, the HCPS data braiding bus generates an adapter according to the physical category of the HCPS data integration device.

[0099] In S102, the HCPS model management device provides basic model management capabilities, model access control capabilities, and model drop link QoS control capabilities to the HCPS data weaving bus. QoS control includes five parameters: integrity, accuracy, real-time performance, security, and reliability. After receiving the above information, the HCPS data weaving bus generates an adapter based on the model category of the HCPS model management device.

[0100] In S103, the HCPS data weaving bus provides data asset management capabilities, connectivity control for the underlying computing and storage engines, real-time processing QoS control, visualization usage, and verification functions to data weaving users. The QoS control includes five parameters: integrity, accuracy, real-time performance, security, and credibility. In an embodiment of the present invention, after receiving the above information, the HCPS data weaving user performs decomposition and aggregation according to the data weaving objective.

[0101] In S104, the data weaving user uses the HCPS data weaving bus to weave the data in the human-information-physical space and push the weaving result to the data weaving user. Here, the HCPS data weaving bus fuses the HCPS personal space data stream, information space data stream, and physical space data stream, and performs consistency determination and active and passive verification actions on multi-dimensional data.

[0102] Based on any of the above embodiments, further, the HCPS data weaving bus realizes effective operations such as publishing, matching, pricing, and charging for the data weaving result, providing a method for HCPS data asset operation.

[0103] Based on any of the above embodiments, further, the HCPS data integration device is installed on personal space and physical space devices.

[0104] Based on any of the above embodiments, further, the HCPS model management device is installed on digital space devices.

[0105] Based on any of the above embodiments, further, the HCPS data weaving bus is installed on a central device, and the central device can be a server or a personal computer.

[0106] The present invention also provides an embodiment. As shown in Figure 2, in the embodiment of the present invention, the driver of electric vehicle Beijing LXXXXX is used as the data weaving user, the smart home of the hotel at the navigation destination is used as the main physical control object, and the driver's carbon emission trajectory is used as the information space control element. Considering factors such as indoor and outdoor temperature and humidity, human-perceived temperature, travel time, and traffic conditions, an optimal home appliance startup and operation plan is formulated. Combining the driver's later manual adjustment factors, the goal of carbon emission optimization is achieved, demonstrating the data weaving process of people, physics, and information in a heterogeneous environment.

[0107] In the first step, the HCPS data integration device provides information such as the vehicle's real-time location, indoor and outdoor temperature and humidity of the hotel, human-perceived temperature, and traffic conditions to the HCPS data weaving bus. For example: Electric vehicle Beijing LXXXXX is at Beijing West Station (7 kilometers away from Huabin International Hotel), the outdoor temperature is 8 degrees Celsius, and the indoor temperature of the hotel is 17 degrees Celsius (the perceived temperature is 13 degrees Celsius). At this time, the on-site sensing device sends the data to the HCPS data weaving bus. The HCPS data weaving bus updates the received information about the vehicle and the hotel.

[0108] In the second step, the HCPS model management device provides user access rights to the carbon emission optimization model and the available model service quality to the HCPS data weaving bus. For example: The EI096 PC server deployed in the Yizhuang data center runs a carbon emission optimization model instance at the user granularity and updates the model data link service quality.

[0109] In the third step, the HCPS data weaving bus provides the underlying computing and storage interface of the on-site sensing device and the user's historical carbon emission path to the data weaving user. For example: The BQ388 PC server deployed in the Beiqijia data center sends the carbon emission path of the driver of electric vehicle Beijing LXXXXX to the data weaving user.

[0110] In the fourth step, according to the preset carbon emission optimization target requirements, the HCPS data weaving bus sends access control requirements, model deployment link requirements, and model version requirements to the HCPS model management device. For example: The BQ388 PC server sends the instruction of "the driver of Beijing LXXXXX calls the carbon emission optimization model" to the EI096 PC server.

[0111] In the fifth step, according to the preset carbon emission optimization target requirements, after decomposition and aggregation, the HCPS data weaving bus sends the data integration requirements at a certain spatio-temporal point to the HCPS data integration device. For example, the BQ388 PC server sends "query the outdoor temperature and humidity of Huabin International Hotel at 9:15 am" to the on-site sensor.

[0112] In the sixth step, based on the elements provided by the HCPS model management device, the HCPS data weaving bus weaves the data elements from the HCPS data integration device to form data assets that meet the QoS requirements of the data weaving user. For example, the carbon emission spatio-temporal data of the driver of electric vehicle Beijing LXXXXX (credibility > 95%).

[0113] In the seventh step, the HCPS data weaving bus checks and updates the on-site device data according to the data weaving result.

[0114] As can be seen from the above examples, the present invention provides an HCPS data weaving system that helps to realize the integration of HCPS personal space data flow, information space data flow, and physical space data flow, and provides an HCPS data asset operation method.

[0115] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, as some steps can be performed in other orders or simultaneously according to the present invention. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0116] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0117] It is understood that the above methods and related features in the switch can be referenced interchangeably. Furthermore, the terms "first," "second," etc., in the above embodiments are used to distinguish between embodiments and do not represent the superiority or inferiority of any particular embodiment.

[0118] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the methods, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0119] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0120] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0121] Similarly, it should be understood that, in order to simplify this disclosure and aid in understanding one or more of the various aspects of the invention, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0122] Those skilled in the art will understand that modules in the apparatus of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0123] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0124] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the apparatus provided according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0125] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

Claims

1. An HCPS data weaving system, characterized in that: The system includes an HCPS data integration device, an HCPS model management device, an HCPS data weaving bus, and field devices. The HCPS data integration device provides the HCPS data weaving bus with tidal big data ingestion capabilities, multi-source data computation link QoS control capabilities, and automated monitoring and maintenance capabilities. The HCPS data integration device includes a tidal big data ingestion module, a multi-source data computation link QoS control module, and a automated monitoring and maintenance module. The HCPS data integration device provides unified access to field sensing devices from different manufacturers. The HCPS data integration device has business data hotspot processing capabilities, including reducer count, JVM memory, and hash processing. The key adds data business semantics, enabling business-level data hotspot monitoring and automatic problem location and handling. The HCPS data integration device has business fault isolation capabilities, setting a business fault isolation baseline for JVM memory overflow, single node utilization exceeding thresholds, and MapReduce task count exceeding thresholds, and has the ability to preprocess connected business systems. The HCPS data integration device has business and computing power adaptation capabilities, and can allocate data platform resources according to preset business priorities in multi-task processing scenarios to ensure business bottom lines. The HCPS model management device provides basic model management capabilities, model access control capabilities, and model fallback QoS control capabilities to the HCPS data weaving bus. The HCPS model management device includes a cross-domain fused HCPS model, a basic model management module, a model access control module, and a model fallback QoS control module. The HCPS model management device has full model coverage dimensions, supporting traditional relational models, time series models, wide table models, document models, and graph models, and can support archive-type and measurement models. The HCPS model management device provides data applications categorized into three types: class, analysis, and presentation. It possesses model access control capabilities, extending the RBAC model at the business layer. This enables access control of data based on user organizational structure, ensuring secure data sharing. The model deployment link QoS control capability refers to the ability to manage the data transmission quality of the three-layer data transmission link from the deployment source through the data platform to the application terminal in multiple dimensions, including verification of link push timeliness, data integrity, and transmission correctness. The HCPS data weaving bus provides data asset management capabilities, underlying computing and storage engine connectivity control, real-time processing QoS control, and visualization and verification functions to data weaving users. The HCPS data weaving bus includes a weaving display management module, a weaving target management module, a data asset management module, an underlying computing and storage engine connectivity control module, a real-time processing QoS control module, and a visualization and verification module. The HCPS data weaving bus performs data weaving on personal space elements, information space elements, and physical space elements according to the user's weaving target.The HCPS data weaving bus has connectivity with underlying computing and storage engines, supporting the following engines: Spark, Hive, Python, ElasticSearch, DWS, Flink, HBase, JDBC, and Shell. Supported scripting languages ​​include: Spark SQL, HiveQL, Python, Shell, PySpark, R, Scala, and JDBC. The HCPS data weaving bus also features visual usability, designed for ordinary users, visually displaying data sources, data models, and data results, and supporting drag-and-drop data product generation.

2. The HCPS data weaving system according to claim 1, characterized in that: The QoS control includes five parameters: integrity, accuracy, real-time performance, security, and reliability.

3. The HCPS data weaving system according to claim 1, characterized in that: The software and hardware of each module in the HCPS data integration device, HCPS model management device, HCPS data weaving bus and field device define the fusion of HCPS personal space data flow, information space data flow and physical space data flow, realize consistency judgment and active and passive verification operations for multi-dimensional data, and provide a method for HCPS data asset operation.

4. The HCPS data weaving system according to claim 1, characterized in that: It also includes a data verification device, which specifically includes: a data acquisition module for acquiring measurement data within a preset time period; a calculation module for calculating the variance of the measurement data within the preset time period; a detection module for comparing the variance with a first preset threshold to obtain a comparison result; and a result module for evaluating the quality of the measurement data based on the comparison result. The data acquisition module acquires measurement data within the preset time period by issuing a measurement data download request; detects the type of the measurement data packet to be downloaded in the measurement data download request; detects the bandwidth utilization of the current communication network; determines the transmission status of the measurement data packet to be downloaded based on the type of the measurement data packet to be downloaded or the bandwidth utilization; if the type of the measurement data packet to be downloaded belongs to a specific type, then the measurement data packet to be downloaded is sent; if the type of the measurement data packet to be downloaded does not belong to a specific type, and the bandwidth utilization is greater than or equal to a first preset value, then: the measurement data packet download request is transmitted to the server at preset time intervals until the measurement data packet to be downloaded is sent.

5. The HCPS data weaving system according to claim 4, characterized in that: The result module is used to evaluate the quality of the measurement data based on the comparison results, specifically including: if the comparison results show that the variance is greater than a first preset threshold, then the measurement data is evaluated as high-quality data; if the comparison results show that the variance is not greater than the first preset threshold, then the measurement data not greater than the first preset threshold is considered suspicious data; the suspicious data is stored in a suspicious database, and the correlation between each suspicious data and other data is detected; if the correlation is high, the suspicious data is considered high-quality data; if the correlation is low, the suspicious data is considered low-quality data; the detection of the correlation between each suspicious data and other data specifically includes: calculating the correlation coefficient between each suspicious data and other data; comparing the absolute value of the correlation coefficient with the size of a second preset threshold, and then determining the magnitude of the correlation between each suspicious data and other data; judging the magnitude of the correlation between each suspicious data and other data based on the comparison results, specifically including: if the absolute value of the correlation coefficient is not less than the second preset threshold, then the correlation between the suspicious data and other data is determined to be high; if the absolute value of the correlation coefficient is less than the second preset threshold, then the correlation between the suspicious data and other data is determined to be low.

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