Data governance management system and method based on smart campus internet of things, and medium

Abstract

The application relates to the technical field of smart campus data governance, in particular to a data governance management system and method based on a smart campus Internet of Things and a medium, the system is oriented to scene requirements, the difference degree of a quantitative feature in a current scene and other scenes is quantified, the fluctuation characteristics of multiple data types are fused, outstanding feature values most characteristic of the current scene are accurately screened out, the feature values are highly adapted to the core target of the scene, a precise and focused target object is provided for subsequent data governance, a governance key demand index of the data type is calculated, the spreading influence of the data on related features and the supporting role of the data on independent features are comprehensively considered, scientific sequencing of the governance priority is realized, the governance resources are inclined to the core data type, the systematization of the governance is ensured, and the precision is also considered, from accurate screening of outstanding features to orderly promotion of data governance, a whole-process optimization scheme for data governance of the smart campus Internet of Things is constructed.

Description

Technical Field

[0001] This invention relates to the field of smart campus data governance technology, and more specifically, to a data governance management system, method, and medium based on the Internet of Things for smart campuses. Background Technology

[0002] With the popularization of IoT in smart campuses, the types of data on campuses are becoming increasingly diverse and originate from various sources, covering multiple business areas such as teaching, security, logistics, and environment. However, existing technologies often lack scenario-oriented approaches in data feature processing, frequently employing undifferentiated feature extraction methods that fail to fully consider the core differences in the needs of different campus scenarios. This results in low adaptability of the selected feature values ​​to the scenario targets, making it difficult to accurately reflect the key states of the scenario and causing targeting ambiguity in subsequent data governance work.

[0003] Currently, the data governance process in smart campuses lacks a scientific mechanism for determining governance priorities. Existing solutions often rely on manual experience or single-dimensional indicators to determine governance targets, failing to comprehensively consider the spillover effects of data types on related features and their supporting role for independent features. This easily leads to a misallocation of governance resources—either neglecting the priority governance of core data or investing too much effort in non-critical data, resulting in a lack of systematic and precise governance work and making it difficult to maximize governance benefits.

[0004] Existing smart campus data governance technologies have failed to form a collaborative optimization system across the entire process, with disconnects between data collection, feature extraction, and governance implementation. There is a lack of effective coordination between feature selection and governance implementation, making it impossible to accurately locate governance targets through scenario-based features, or to systematically advance governance work based on data dependencies. This makes it difficult to adapt to the diverse and dynamic operational management needs of campuses, resulting in slow data quality improvement and insufficient data value conversion capabilities. Summary of the Invention

[0005] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a data governance management system, method and medium based on the Internet of Things for smart campuses.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A data governance and management system based on the Internet of Things for smart campuses includes: The Smart Campus IoT Data Feature Set Module clarifies the data types within the Smart Campus IoT, regularly collects data of each data type, and integrates the collected data of each data type to form a Smart Campus IoT Feature Set. The Campus Scene Highlighted Feature Analysis Module clarifies the current campus scene and, based on the current scene and the smart campus IoT feature set, determines the highlighted feature values ​​of all campus scenes. The Possible Data Sequence Governance Module identifies all possible data types involved in governance based on prominent feature values ​​of all campus scenarios, generates a data governance sequence based on all possible data types, and performs governance analysis on the data of each possible data type in sequence according to the data governance sequence.

[0007] Furthermore, the process for determining the prominent feature value in the campus scenario is as follows: Select a smart campus data feature value, simultaneously determine the current scenario, obtain the average actual difference value of the smart campus data feature value in the current scenario, and simultaneously obtain the average actual difference value of the smart campus data feature value in each of the other scenarios. If the average actual difference value of the current scenario is higher than the average actual difference value of the other scenario, increase the number of scene feature anomalies by one. Finally, sum the number of scene feature anomalies to obtain the result. The average actual difference value of the smart campus data feature values ​​in the current scenario is marked as... Through formula The scene prominence index of the smart campus data feature value is calculated. Set a scene prominence threshold for the smart campus data feature value. When the scene prominence index is higher than the scene prominence threshold, mark the smart campus data feature value as a campus scene prominence feature value.

[0008] Furthermore, the average actual difference value of smart campus data feature values ​​in a scenario is obtained as follows: Select a scenario, obtain the actual values ​​of the smart campus data feature values ​​in the same scenario before the current system time, compare all the actual values ​​of the smart campus data feature values ​​pairwise, calculate the absolute difference between the actual values ​​of each pair of compared smart campus data feature values, calculate the actual difference value of the feature, sum and average all the actual difference values ​​of the feature to obtain the average difference value, and use the min-max standardization formula to calculate the average actual difference value of the smart campus data feature values ​​in that scenario.

[0009] Furthermore, based on the prominent feature values ​​of all campus scenarios, all possible data types involved in governance are determined, as follows: Select a prominent feature value of a campus scenario, determine the data types involved in the source of the prominent feature value of the campus scenario, and mark all involved data types as possible data types for governance.

[0010] Furthermore, a data governance sequence is generated based on all possible governance data types: the key governance requirement index for each possible governance data type is obtained, all possible governance data types are sorted in descending order of the value of the key governance requirement index, and a data governance sequence is generated based on the sorting.

[0011] Furthermore, the method for obtaining the key governance demand index of potential governance data types is as follows: Obtain the scene feature association map corresponding to the current scene, combine multiple feature value matching sets, determine whether each feature value matching set is a feature-dependent influence set, mark sets that are not feature-dependent influence sets as feature-independent influence sets, select a potential governance data type, and when the source of at least one prominent feature value of a campus scene in a feature-dependent influence set involves this potential governance data type, increase the data type spread count by one; when the source of at least one prominent feature value of a campus scene in a feature-independent influence set involves this potential governance data type, increase the data type independence count by one. Finally, sum the data type spread counts and mark them as BHG, and sum the data type independence counts and mark them as RSZ. The governance key demand index for this possible governance data type was calculated. Where g1 and g2 are both weighting coefficients, and g1+g2=1.

[0012] Furthermore, the combination method of multiple feature value matching sets is as follows: Pack all prominent feature values ​​of the campus scene into a feature value matching set, denoted as set number 1. Then, iterate through and delete one prominent feature value of the campus scene, and pack the remaining prominent feature values ​​of the campus scene into a feature value matching set. This process is repeated. Finally, it is necessary to ensure that the final feature value matching set contains at least two prominent feature values ​​of the campus scene. If the feature value matching set contains only two prominent feature values ​​of the campus scene, then the deletion should be stopped.

[0013] Furthermore, the method for determining whether a feature value matching set is a feature dependency and influence set is as follows: Select a feature value matching set, map the prominent feature values ​​of the campus scene to the scene feature association graph, and when all the prominent feature values ​​of the campus scene form a connected graph in the scene feature association graph, then mark the feature value matching set as a feature dependency and influence set.

[0014] Furthermore, the data governance and management method based on the Internet of Things in smart campuses includes the following steps: Step 1: Regularly collect data of various data types and integrate them to form a smart campus IoT feature set; Step 2: Select prominent feature values ​​for campus scenarios from the smart campus IoT feature set; Step 3: Determine all possible data types to be governed and generate a data governance sequence; Step 4: Perform governance analysis on the data of each possible data type according to the data governance sequence.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The system of this invention is scenario-driven, accurately selecting the most representative prominent feature values ​​for the current scenario by quantifying the differences between features in the current scenario and other scenarios and integrating the fluctuation characteristics of multiple data types. This ensures that the feature values ​​are highly compatible with the core objectives of the scenario, providing precise and focused targets for subsequent data governance. By calculating the key governance requirement index of data types, the system comprehensively considers the spread of data to related features and its supporting role for independent features, achieving a scientific ranking of governance priorities. This allows governance resources to be tilted towards core data types, ensuring both the systematic nature of governance (driving the collaborative optimization of related features) and the precision (targeted solutions to key isolated problems). From accurately selecting prominent features to orderly promoting data governance, this system constructs a full-process optimization solution for smart campus IoT data governance, realizing intelligent connection from data collection and feature extraction to governance implementation. This effectively improves the quality and value transformation capabilities of smart campus data, and better meets the multi-scenario and dynamic operation and management needs of the campus. Attached Figure Description

[0016] Figure 1 This is a system principle block diagram of a data governance and management system based on the Internet of Things in a smart campus. Figure 2 This is a block diagram illustrating the operational principle of the IoT data feature set module for smart campuses. Detailed Implementation

[0017] Example 1: Refer to Figures 1 to 2 The data governance and management system based on the Internet of Things for smart campuses includes a smart campus IoT data feature set module, a campus scene prominent feature analysis module, and a possible data sequence governance module.

[0018] The Smart Campus IoT Data Feature Set Module clarifies the data types within the Smart Campus IoT (which contains a large number of data types, including but not limited to access control data, teaching equipment usage data, student management data, teaching course management data, and teaching material management data). It periodically collects data from each data type (the corresponding time interval is set based on the dynamic characteristics of historical data, such as the frequency and volatility of changes in different data types). The module then fuses the collected data to create a Smart Campus IoT Feature Set (which contains multiple Smart Campus data feature values, such as smart blackboard usage frequency, student attendance rate, and water and electricity consumption; each feature value is calculated by fusing data from at least two different data types). For example, the frequency of use of smart blackboards needs to be calculated by combining data from both teaching equipment usage and course management. The frequency of use is calculated as: Total actual blackboard usage time / Total planned course time for the day. The actual usage time comes from teaching equipment usage data, and the planned course time for the day comes from course management data. Similarly, student attendance rate needs to be calculated by combining data from both access control and student management. The student attendance rate is calculated as: Number of students passing through access control / Number of students expected to be present. The number of students passing through access control comes from access control data, and the number of students expected to be present comes from student management data. These are just examples and not exhaustive; there are smart campus data characteristics involving many more data types, and these require the use of the min-max standardization formula. ), mapping all eigenvalues ​​to the interval [0,1], The theoretical upper limit or historical peak value of this smart campus data feature value across all smart campus scenarios. (The theoretical lower limit or historical low value of the data feature value of the smart campus in all scenarios of the smart campus).

[0019] The Campus Scene Highlight Feature Analysis Module clarifies the current campus scene (campus scenes include, but are not limited to, campus night patrol scenes, teaching building theory class scenes, and peak dining scene in the cafeteria, and clarifies the current campus scene based on the actual situation of the campus), and determines the highlight feature values ​​of all campus scenes based on the current scene and combined with the smart campus IoT feature set.

[0020] The process for determining prominent feature values ​​in a campus scenario is as follows: Select a smart campus data feature value and simultaneously determine the current scenario. Obtain the average actual difference value of this smart campus data feature value in the current scenario. Simultaneously, obtain the average actual difference value of this smart campus data feature value in each of the other scenarios (excluding the current scenario). If the average actual difference value in the current scenario is higher than the average actual difference value in any other scenario, increase the number of scene feature anomalies by one. Finally, sum the number of scene feature anomalies to obtain the result. The average actual difference value of the smart campus data feature values ​​in the current scenario is marked as... Through formula The scene prominence index of the smart campus data feature value is calculated. Set a scene prominence threshold for the smart campus data feature value (the scene prominence threshold is different for each smart campus data feature value, and the scene prominence threshold is set based on the historical PRED statistical distribution of the corresponding smart campus data feature value). When the scene prominence index is higher than the scene prominence threshold, mark the smart campus data feature value as a campus scene prominence feature value (no marking is required if it is not higher).

[0021] The average actual difference value of smart campus data feature values ​​in a given scenario is obtained as follows: Select a scenario, obtain the actual values ​​of the smart campus data feature values ​​in the same scenario up to the current system time, compare all the actual values ​​of the smart campus data feature values ​​pairwise, calculate the absolute difference between each pair of compared actual values ​​to obtain the actual difference value, sum and average all the actual difference values ​​to obtain the average difference value, and use the min-max standardization formula: The average actual difference value of the smart campus data feature values ​​in this scenario was calculated. The minimum theoretical average difference value of the smart campus data feature value in this scenario. The highest theoretical average difference value of the smart campus data feature value in this scenario.

[0022] The possible data sequence governance module identifies all possible data types involved in governance based on the prominent feature values ​​of all campus scenarios. It generates a data governance sequence based on all possible data types and performs governance analysis on the data of each possible data type in sequence (the specific governance analysis method is not described in detail. For example, if access control data is ranked first in the data governance sequence, the quality problems of access control data (such as missing or distorted data) and collection problems (such as unreasonable frequency) are checked first. If problems exist after the investigation, they are rectified. If no problems exist, the next possible data type in the data governance sequence is then analyzed for governance).

[0023] Based on the prominent feature values ​​of all campus scenarios, determine all possible data types involved in governance, as follows: Select a prominent feature value of a campus scenario, determine the data types involved in the source of the prominent feature value of the campus scenario (taking the student attendance rate feature value as an example, the source of the student attendance rate feature value is the number of students passing through the access control and the number of students who should be present, then the data types involved are access control data and student management data), and mark all involved data types as possible data types in governance (e.g., mark both access control data and student management data as possible data types in governance).

[0024] Generate a data governance sequence based on all possible data types: Obtain the governance key demand index for each possible data type, sort all possible data types in descending order of the value of the governance key demand index, and generate a data governance sequence based on the sorting.

[0025] The method for obtaining the key governance demand index of potential governance data types is as follows: Obtain the scene feature association map corresponding to the current scene, combine multiple feature value matching sets, determine whether each feature value matching set is a feature-dependent influence set, and mark sets that are not feature-dependent influence sets as feature-independent influence sets. Select a potential governance data type. If the source of at least one prominent feature value of a campus scene in a feature-dependent influence set involves this potential governance data type, increase the data type spread count by one. If the source of at least one prominent feature value of a campus scene in a feature-independent influence set involves this potential governance data type, increase the data type independence count by one. Finally, sum the data type spread counts and mark them as BHG, and sum the data type independence counts and mark them as RSZ. The governance key demand index for this possible governance data type was calculated. In this context, g1 and g2 are both weighting coefficients, and g1 + g2 = 1. Since the focus is on managing one data type, which drives the optimization of a batch of related prominent features, the value of g1 can be 0.7 and the value of g2 can be 0.3.

[0026] Each scenario corresponds to an independent scenario feature association graph. The scenario feature association graph contains all smart campus data feature values ​​and uses association lines to show whether different smart campus data feature values ​​are dependent. If two smart campus data feature values ​​are dependent, then there is an association line between them in the scenario feature association graph. If there is no dependency, then there is no association line between them in the scenario feature association graph. The dependency relationship between two smart campus data feature values ​​may not be the same in different scenarios. For example, in the scenario feature association graph of the teaching building theory class scenario, the smart blackboard usage frequency feature value is related to the water and electricity energy consumption feature value. In the scenario feature association graph of the campus night patrol scenario, the smart blackboard usage frequency feature value is not related to the water and electricity energy consumption feature value (in the theory class, the blackboard usage frequency and water and electricity energy consumption are dependent due to equipment use → energy consumption, while in the night patrol, there is no business relationship between the two, so there is no dependency).

[0027] The combination of multiple feature value matching sets is as follows: All prominent features of the campus scene are packaged into a single feature value matching set, designated as set 1 (e.g., initially there are 3 prominent features of the campus scene, set 1: {Prominent feature value A, Prominent feature value B, Prominent feature value C}). Then, iterate through and delete one prominent feature value at a time (deleting each prominent feature value sequentially). The remaining prominent features of the campus scene after deletion are then packaged into a feature value matching set (e.g., if prominent feature value A is deleted, prominent features values ​​B and C remain → set 2: {Prominent feature value B, Prominent feature value C}; if prominent feature value B is deleted, prominent features values ​​C remain...). The prominent feature value A of the campus scene and the prominent feature value C of the campus scene are then assigned to set 3: {Prominent feature value A of the campus scene, prominent feature value C of the campus scene}. If prominent feature value C of the campus scene is deleted, the remaining prominent feature values ​​A and B of the campus scene are assigned to set 4: {Prominent feature value A of the campus scene, prominent feature value B of the campus scene}. At this point, there are a total of four feature value matching sets: set 1, set 2, set 3, and set 4. This process continues (iterates through sets that delete two or three prominent feature values ​​of the campus scene). Ultimately, it is necessary to ensure that the final feature value matching set contains at least two prominent feature values ​​of the campus scene. If the feature value matching set contains only two prominent feature values ​​of the campus scene, then the deletion process stops.

[0028] The method for determining whether a feature value matching set is a feature dependency and influence set is as follows: Select a feature value matching set, map the prominent feature values ​​of the campus scene to the scene feature association graph, and when all the prominent feature values ​​of the campus scene form a connected graph in the scene feature association graph (that is, there is at least one path between any two prominent feature values ​​of the campus scene), then mark the feature value matching set as a feature dependency and influence set (otherwise, do not mark it).

[0029] Example 2: A data governance and management method based on the Internet of Things in a smart campus. The steps are as follows: Step 1: Regularly collect data of various data types and integrate them to form a smart campus IoT feature set; Step 2: Select prominent feature values ​​for campus scenarios from the smart campus IoT feature set; Step 3: Determine all possible data types to be governed and generate a data governance sequence; Step 4: Perform governance analysis on the data of each possible data type according to the data governance sequence.

[0030] The above formulas are all dimensionless calculations, and the preset parameters in the formulas should be set by those skilled in the art according to the actual situation.

[0031] The above embodiments can be implemented, in whole or in part, by software, hardware, 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. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another; for example, the computer instructions can be transmitted from a website, computer, server, or data center via wired or wireless means. For example, infrared, wireless, microwave, etc. This method transmits data to another website, computer, server, or data center. The computer-readable storage medium can be any usable medium accessible to a computer, or a data storage device such as a server or data center containing one or more sets of usable media. The usable medium can be a magnetic medium. For example, floppy disks, hard disks, and magnetic tapes. Optical media For example, DVD Alternatively, semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0032] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes 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 this application.

[0033] 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 implementation should not be considered beyond the scope of this application.

[0034] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0035] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0036] If the aforementioned function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device... It could be a personal computer, a server, or a network device, etc. Perform all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage media include: USB flash drive, portable hard drive, and read-only memory. Read-only memory, ROM Random Access Memory Random access memory, RAM Various media that can store program code, such as magnetic disks or optical discs.

[0037] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data governance and management system based on the Internet of Things for smart campuses, characterized in that: include: The Smart Campus IoT Data Feature Set Module clarifies the data types within the Smart Campus IoT, regularly collects data of each data type, and integrates the collected data of each data type to form a Smart Campus IoT Feature Set. The Campus Scene Highlighted Feature Analysis Module clarifies the current campus scene and, based on the current scene and the smart campus IoT feature set, determines the highlighted feature values ​​of all campus scenes. The Possible Data Sequence Governance Module identifies all possible data types involved in governance based on prominent feature values ​​of all campus scenarios, generates a data governance sequence based on all possible data types, and performs governance analysis on the data of each possible data type in sequence according to the data governance sequence.

2. The data governance and management system based on the Internet of Things for smart campuses according to claim 1, characterized in that, The process for determining prominent feature values ​​in a campus scenario is as follows: Select a smart campus data feature value, simultaneously determine the current scenario, obtain the average actual difference value of this smart campus data feature value in the current scenario, and simultaneously obtain the average actual difference value of this smart campus data feature value in each of the other scenarios. If the average actual difference value in the current scenario is higher than the average actual difference value in any other scenario, increase the number of scene feature anomalies by one. Finally, sum the number of scene feature anomalies to obtain the result. The average actual difference value of the smart campus data feature values ​​in the current scenario is marked as... Through formula The scene prominence index of the smart campus data feature value is calculated. Set a scene prominence threshold for the smart campus data feature value. When the scene prominence index is higher than the scene prominence threshold, mark the smart campus data feature value as a campus scene prominence feature value.

3. The data governance and management system based on the Internet of Things for smart campuses according to claim 2, characterized in that, The average actual difference value of smart campus data feature values ​​in a scenario is obtained as follows: Select a scenario, obtain the actual values ​​of the smart campus data feature values ​​in the same scenario before the current system time, compare all the actual values ​​of the smart campus data feature values ​​pairwise, calculate the absolute difference between the actual values ​​of each pair of compared smart campus data feature values, calculate the actual difference value of the feature, sum and average all the actual difference values ​​of the feature to obtain the average difference value, and use the min-max standardization formula to calculate the average actual difference value of the smart campus data feature values ​​in that scenario.

4. The data governance and management system based on the Internet of Things for smart campuses according to claim 1, characterized in that, Based on the prominent feature values ​​of all campus scenarios, all possible data types involved in governance are determined as follows: Select a prominent feature value of a campus scenario, determine the data types involved in the source of the prominent feature value of the campus scenario, and mark all involved data types as possible data types for governance.

5. The data governance and management system based on the Internet of Things for smart campuses according to claim 1, characterized in that, Generate a data governance sequence based on all possible data types: Obtain the governance key demand index for each possible data type, sort all possible data types in descending order of the value of the governance key demand index, and generate a data governance sequence based on the sorting.

6. The data governance and management system based on the Internet of Things for smart campuses according to claim 5, characterized in that, The method for obtaining the key governance demand index of potential governance data types is as follows: Obtain the scene feature association map corresponding to the current scene, combine multiple feature value matching sets, determine whether each feature value matching set is a feature-dependent influence set, and mark sets that are not feature-dependent influence sets as feature-independent influence sets. Select a potential governance data type. If the source of at least one prominent feature value of a campus scene in a feature-dependent influence set involves this potential governance data type, increase the data type spread count by one. If the source of at least one prominent feature value of a campus scene in a feature-independent influence set involves this potential governance data type, increase the data type independence count by one. Finally, sum the data type spread counts and mark them as BHG, and sum the data type independence counts and mark them as RSZ. The governance key demand index for this possible governance data type was calculated. Where g1 and g2 are both weighting coefficients, and g1+g2=1.

7. The data governance and management system based on the Internet of Things for smart campuses according to claim 6, characterized in that, The combination of multiple feature value matching sets is as follows: Pack all prominent feature values ​​of the campus scene into a feature value matching set, denoted as set number 1. Then, iterate through and delete one prominent feature value of the campus scene. Pack the remaining prominent feature values ​​of the campus scene into a feature value matching set. Continue in this manner. Finally, it is necessary to ensure that the final feature value matching set contains at least two prominent feature values ​​of the campus scene. If the feature value matching set contains only two prominent feature values ​​of the campus scene, then stop deleting.

8. The data governance and management system based on the Internet of Things for smart campuses according to claim 6, characterized in that, The method for determining whether a feature value matching set is a feature dependency and influence set is as follows: Select a feature value matching set, map the prominent feature values ​​of the campus scene to the scene feature association graph, and when all the prominent feature values ​​of the campus scene form a connected graph in the scene feature association graph, then mark the feature value matching set as a feature dependency and influence set.

9. A data governance and management method based on the Internet of Things for smart campuses, applied to the data governance and management system based on the Internet of Things for smart campuses as described in any one of claims 1-8, characterized in that, The steps are as follows: Step 1: Regularly collect data of various data types and integrate them to form a smart campus IoT feature set; Step 2: Select prominent feature values ​​for campus scenarios from the smart campus IoT feature set; Step 3: Determine all possible data types to be governed and generate a data governance sequence; Step 4: Perform governance analysis on the data of each possible data type according to the data governance sequence.

10. A data governance and management medium based on the Internet of Things for smart campuses, characterized in that: It is applied to the data governance and management system based on the Internet of Things for smart campuses as described in any one of claims 1-8.

Images