Enterprise management data management method and system based on big data

Abstract

The invention relates to an enterprise management data governance method and system based on big data. The system comprises a data integration and exception recognition module, an exception handling responsibility and classification module, an attendance state and personnel matching module, a notification and confirmation feedback module, a visual range positioning and notification module and a syn-position glide monitoring module. According to the method, in the aspect of notifying processing personnel, a perfect confirmation feedback mechanism is designed, and it is ensured that the abnormal processing notification can be received in time and can be fed back rapidly. And if the feedback is not obtained, the system confirms that the processing personnel are in the visible range. The mechanism not only improves the response timeliness, but also ensures that more urgent exceptions are quickly handled by dynamically adjusting the response time. In addition, in combination with the actual office environment of the enterprise, the closest handling personnel can be quickly found in the exception handling process. Therefore, the efficiency of information transmission is improved, and response delay caused by dispersion of personnel is reduced.

Description

Technical Field

[0001] The present invention relates to the field of enterprise management and data governance, and in particular to an enterprise management data governance method and system based on big data. Background Art

[0002] The field of enterprise management and data governance aims to help enterprises operate efficiently, optimize resource allocation, and improve the scientificity and accuracy of decision-making through systematic management methods and data-driven decision support. With the rapid development of information technology, enterprises are facing the challenge of massive data, but also have more abundant resources for analysis and decision-making.

[0003] In the field of enterprise management and data governance, with the continuous advancement of technology, enterprises can manage their data assets more effectively, optimize business processes, and improve the scientificity and accuracy of decision-making. The combination of these technologies not only brings higher efficiency to enterprises, but also enhances their ability to respond to market changes and risks. Through effective data governance, enterprises can achieve better resource allocation, thereby increasing their competitive advantage.

[0004] Existing exception handling often relies on manual judgment and manual notification, resulting in untimely information transmission, which further affects the timely response to exceptions. In traditional systems, handling responsibilities are often unclear, leading to the phenomenon of shirking responsibility. In addition, previous processing methods failed to make full use of the human resources within the enterprise, and often assigned unsuitable personnel to exception handling tasks, reducing processing efficiency. At the same time, the location information of personnel is often not available in real time, resulting in the possibility that the processing personnel are not at their workstations when the exception handling instructions are issued, and thus cannot receive the instructions and take action in time. These problems have jointly led to delays in the speed of exception handling and affected the overall operational efficiency of the enterprise. Summary of the invention

[0005] The present invention aims at solving the technical problems existing in the prior art and provides an enterprise management data governance method based on big data, aiming to solve the problems existing in the background technology.

[0006] The technical solution of the present invention to solve the above technical problems is as follows: a method for enterprise management data governance based on big data, the method comprising:

[0007] Integrate data from various systems and perform data analysis to automatically identify potential anomalies and clarify the source and nature of anomalies;

[0008] Establish a list of responsibilities for the exception handling department, classify the exception status, and determine the exception level;

[0009] Obtain the attendance status of all employees in the company on that day, and based on the attendance status, combine the corresponding relationship between the internal rank and processing capacity of the company, use the employee database for matching, and screen out processing personnel who meet the processing requirements;

[0010] Send notifications to processing personnel, set up a confirmation feedback mechanism, monitor the confirmation feedback status, and call the enterprise camera equipment to collect images of the current office area if no confirmation feedback is received;

[0011] If the person is identified as being within the visible range, a notification of the person's location and handling of the abnormality is sent to other employees closest to the person;

[0012] If the person is not found within the visible range, the system will scroll down in order to match the next qualified person and repeat the notification process, and continue monitoring until confirmation feedback from the person is obtained.

[0013] As a further solution of the present invention, the integration of data from various systems and data analysis to automatically identify potential anomalies and clarify the source and nature of the anomalies specifically includes:

[0014] Determine the data sources that need to be integrated, extract the required data from each system, and integrate them in a unified format;

[0015] Build an anomaly detection model and input the integrated data into the established anomaly detection model to identify potential abnormal data.

[0016] As a further solution of the present invention, the anomaly detection model is constructed, and the integrated data is input into the established anomaly detection model to identify potential abnormal data and mark the type and degree of the anomaly, specifically:

[0017] Build an anomaly detection model to measure the degree of anomaly Z of the data point:

[0018]

[0019] Among them, X is the feature value to be detected, μ is the mean of the feature, and σ is the standard deviation of the feature;

[0020] Determine the value of the abnormality level Z. If Z>3 or Z<3, it is determined that the feature value has a potential abnormality and an abnormality report is generated;

[0021] As a further solution of the present invention, the establishment of a responsibility list of the exception handling department, classification of the abnormal state, and determination of the abnormal level specifically include:

[0022] Obtain the departments responsible for handling various exceptions and their responsibilities;

[0023] For the identified potential abnormal data, identify the type and degree of the abnormality;

[0024] Classify the identified anomalies, determine the source of the anomaly, determine the system to which the anomaly is sent by analyzing the anomaly data field, clarify the nature of the identified anomaly, and determine the urgency level of the anomaly.

[0025] As a further solution of the present invention, the method of identifying potential abnormal data, marking the type and degree of the abnormality, and classifying the identified abnormality is specifically as follows:

[0026] Analyze the weight of each abnormality type in the abnormality report, and obtain the abnormality type with the highest weight as the abnormality type corresponding to the abnormality. Specifically:

[0027]

[0028] Where t represents the feature word, i.e. the anomaly type, d represents the document describing the anomaly, i.e. the anomaly report, N represents the total number of documents, DF(t) represents the number of documents containing the anomaly type t, and TF_IDF(t,d) represents the weight of the anomaly type t in the anomaly report d. The higher the value, the more representative the anomaly type t is in the anomaly report d.

[0029] Severity scores are calculated based on the anomaly type and impact:

[0030]

[0031] Among them, ω i Represents the weight of the anomaly type, x i The feature value representing the current anomaly.

[0032] As a further solution of the present invention, the attendance status of all employees in the enterprise on that day is obtained, and based on the attendance status, the corresponding relationship between the internal rank and the processing capacity of the enterprise is combined, and the employee database is used for matching to screen out the processing personnel who meet the processing requirements, specifically including:

[0033] Extract the attendance records of the day from the attendance system, screen the currently on-duty employees, and extract the rank and processing capacity information of the currently on-duty employees from the employee database;

[0034] Read the access level involved in this abnormal data, and conduct a secondary screening of the current employees based on the access level and employee rank;

[0035] For the remaining employees after the second screening, determine the exceptions that can be handled by employees of each rank and processing capability information, and accordingly select a list of all handlers who are eligible to handle this exception from the remaining employees after the second screening, and the list is sorted from high to low in terms of processing capability.

[0036] As a further solution of the present invention, the notification is sent to the processing personnel, and a confirmation feedback mechanism is set to monitor the confirmation feedback status. If no confirmation feedback is received, the enterprise camera equipment is called to collect the current office area image, which specifically includes:

[0037] Obtaining the information of the first person in the processing personnel list, and sending a processing notification to the first person, wherein the processing notification includes the abnormal content that needs to be processed;

[0038] Set up a confirmation feedback mechanism, divide the abnormal severity scores into segments, and set a response time for each score segment;

[0039] Read the severity score corresponding to the exception, analyze the response time required for the severity score, and determine whether confirmation feedback from the handler is received within the response time after sending the handling notification to the handler;

[0040] Set a timeout mechanism. If no confirmation is received within the response time, it will be marked as no feedback, and the camera equipment in the enterprise will be called to collect images of the current office area.

[0041] As a further solution of the present invention, if the person is identified as being within the visual range, a notification of the location and handling of the abnormality is sent to other employees closest to the person, specifically including:

[0042] Collect personnel information from office area images, identify personnel's real-time location, and determine whether the processing personnel are within the visible range captured by the camera;

[0043] Obtain the location information of the processing personnel, including the floor, area and workstation;

[0044] Establish an enterprise office layout model, which includes all walkable roads, and update all employee positions in real time in the model. The nodes in the model represent the position of each employee, the intersection of the passages, and the edges represent the walkable roads between two points.

[0045] Assign a weight to each edge, representing the path length or travel cost, find the shortest path from the processing worker's location to another worker, and calculate the path length:

[0046]

[0047] Among them, d i Indicates the distance of each segment on the path;

[0048] Compare the path lengths, obtain other employees closest to the processing personnel, and send a prompt notification to the other employees, wherein the prompt notification includes the person who needs to be prompted, that is, the processing personnel, the current location of the processing personnel, and a summary of the abnormal information that needs to be processed.

[0049] Another object of the present invention is to provide an enterprise management data governance system based on big data, comprising:

[0050] Data integration and anomaly identification module, which is used to integrate data from various systems and perform data analysis, automatically identify potential anomalies, and clarify the source and nature of anomalies;

[0051] The exception handling responsibility and classification module is used to establish a responsibility list for the exception handling department, classify the exception status, and determine the exception level;

[0052] The attendance status and personnel matching module is used to obtain the attendance status of all employees in the enterprise on that day, and based on the attendance status, combined with the corresponding relationship between the internal rank and processing capacity of the enterprise, use the employee database for matching, and screen out processing personnel who meet the processing requirements;

[0053] The notification and confirmation feedback module is used to send notifications to processing personnel, set up a confirmation feedback mechanism, monitor the confirmation feedback status, and call the enterprise camera equipment to collect images of the current office area if no confirmation feedback is received;

[0054] The visual range positioning and notification module is used to send location and abnormal handling notifications to other employees closest to the person if the person is identified as being within the visual range;

[0055] The sequence-down monitoring module is used to slide down in sequence if the person is not found within the visible range, match the next qualified processing person and repeat the notification process, and continue monitoring until the person's confirmation feedback is obtained.

[0056] As a further solution of the present invention, the data integration and anomaly identification module includes:

[0057] The data source integration and extraction unit is used to determine the data sources that need to be integrated, extract the required data from each system, and integrate them in a unified format;

[0058] The anomaly detection model building unit is used to build an anomaly detection model, input the integrated data into the established anomaly detection model, and identify potential abnormal data.

[0059] The beneficial effect of the present invention is that the method clarifies the responsibilities and processes for handling various types of exceptions by establishing a responsibility list of the exception handling department and classifying the abnormal status, thereby eliminating the problem of unclear responsibilities in traditional methods. This clear division of responsibilities enables enterprises to respond to emergencies more efficiently and improves team collaboration efficiency.

[0060] In terms of notifying the processing personnel, this method has designed a complete confirmation feedback mechanism to ensure that every relevant person can receive the exception processing notification in time and can quickly provide feedback. If no feedback is obtained, the system will automatically call the camera equipment for real-time monitoring to ensure that the processing personnel are within the visual range. This mechanism not only improves the timeliness of response, but also ensures that more urgent exceptions are handled quickly by dynamically adjusting the response time. This timeliness is often lacking in traditional methods, affecting the overall operational efficiency of the enterprise.

[0061] In addition, the personnel positioning and path optimization strategy used in the method, combined with the actual office environment of the enterprise, can quickly find the closest processing personnel during the exception handling process. This not only improves the efficiency of information transmission, but also reduces the response delay caused by the dispersion of personnel, effectively solving the limitations of traditional communication methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A flow chart of the enterprise management data governance method based on big data provided by the present invention;

[0063] Figure 2 A flowchart for integrating data from various systems and performing data analysis to automatically identify potential anomalies and clarify the source and nature of anomalies;

[0064] Figure 3 To establish a list of responsibilities for the exception handling department, and to classify the exception status and determine the flowchart of the exception level;

[0065] Figure 4 Based on the attendance status, combined with the correspondence between the internal rank and processing capability of the enterprise, the employee database is used for matching, and the flowchart of the processing personnel who meet the processing requirements is screened out;

[0066] Figure 5 To monitor the confirmation feedback status, if no confirmation feedback is received, a flowchart is created to call the enterprise camera equipment to collect images of the current office area;

[0067] Figure 6 A flowchart for sending a notification of the location and handling anomalies to other employees closest to the person if the person is identified as being within the visual range;

[0068] Figure 7A structural diagram of the enterprise management data governance system based on big data provided by the present invention;

[0069] Figure 8 This is the structural block diagram of the data integration and anomaly identification module. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0071] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0072] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0073] A data governance method for enterprise management based on big data. This method is mainly used to detect whether there are anomalies in enterprise data. If there are anomalies, the attendance status of the enterprise on that day is read, all personnel in the enterprise on that day are obtained, and the data is classified according to the type of anomaly. The department that is suitable for handling the anomaly and the rank that is capable of handling the anomaly are determined. If an anomaly is detected, the source of the anomaly must be identified (such as the attendance system, project management tools, financial system, etc.), and machine learning or statistical methods must be used for automated anomaly detection to ensure rapid identification.

[0074] Then, according to the type of exception, such as technical problems, personnel shortages, equipment failures, etc., a list of departmental responsibilities for handling exceptions is established to ensure that each exception has a designated handling team. Next, the person who is qualified to handle the exception is matched and a notification is sent to the person's device. Multi-channel notification methods (such as email, short messages, etc.) are used to ensure the success rate of notifications, and a confirmation mechanism is set up (such as clicking a confirmation link, replying to a notification, etc.).

[0075] The response time is determined according to the level of the anomaly, and the response time is dynamically adjusted to adapt to the severity and impact of the anomaly. If no feedback is received from the person confirming receipt of the notification within the response time, the enterprise camera equipment is called to collect images of the current office area and perform face recognition to ensure privacy compliance, and an efficient face recognition algorithm is used to reduce the misidentification rate. If the person is identified as being within the visible range, a notification is sent to the devices of other employees closest to the person. The notification content includes the location of the person and a notification reminding the person to handle the anomaly, ensuring that the notification information is concise and clear so that the recipient can quickly understand the task. If the person cannot be found within the visible range, the order is slid down, that is, the next person who is eligible to handle the anomaly is matched, and the above operations are repeated until the person confirms receipt of the notification.

[0076] Each operation should be recorded to form a traceable log for subsequent analysis. After the exception is handled, the entire process is evaluated, feedback is collected for continuous improvement, and data analysis is regularly performed on the frequency of exceptions, processing time, etc., and reports are generated. In addition, employee training and simulation exercises are regularly conducted to ensure that the team can respond efficiently when real exceptions occur.

[0077] Figure 1 The flowchart of the enterprise management data governance method based on big data provided by the present invention is as follows: Figure 1 As shown, the method includes:

[0078] S100 integrates data from various systems and performs data analysis to automatically identify potential anomalies and clarify the source and nature of anomalies;

[0079] like Figure 2 As shown, the data from various systems are integrated and analyzed to automatically identify potential anomalies and clarify the source and nature of the anomalies, including:

[0080] S110, determine the data sources that need to be integrated, extract the required data from each system, and integrate them in a unified format;

[0081] S120, construct an anomaly detection model, input the integrated data into the established anomaly detection model, and identify potential abnormal data.

[0082] In the embodiment of the present invention, the anomaly detection model is constructed, and the integrated data is input into the established anomaly detection model to identify potential abnormal data and mark the type and degree of the anomaly, specifically:

[0083] Build an anomaly detection model to measure the degree of anomaly Z of the data point:

[0084]

[0085] Among them, X is the feature value to be detected, μ is the mean of the feature, and σ is the standard deviation of the feature;

[0086] Determine the value of the abnormality level Z. If Z>3 or Z<3, it is determined that the feature value has a potential abnormality and an abnormality report is generated;

[0087] This step will identify the various data sources that need to be integrated, including attendance systems, project management tools, financial systems, production management systems, etc., to evaluate the availability and data quality of each data source to ensure that the acquired data is up-to-date and accurate. Then extract the required data from each system and design a standardized data extraction process to ensure that the fields between different systems can correspond correctly to avoid data loss or errors. After data extraction, perform data cleaning, check and process missing values, duplicate values, and outliers to ensure data integrity and consistency, and uniformly process data in different formats to ensure that the data can be analyzed in a unified manner.

[0088] Subsequently, the cleaned data is integrated to build a unified data warehouse. This process includes connecting relevant data from various data sources to form a comprehensive data set.

[0089] Building an anomaly detection model is the key to this step. The integrated data is input into the established model, the degree of abnormality of the data point is calculated by the algorithm, and the threshold of the degree of abnormality is defined. If the calculated value is greater than 3 or less than -3, it is determined that the feature value has a potential abnormality and an abnormality report is generated. Finally, the identified potential anomalies are analyzed in detail, the type and degree of the anomaly are identified, and a visual anomaly report is generated so that decision-makers and relevant departments can quickly understand the abnormal situation and formulate response measures.

[0090] This step can achieve real-time monitoring and rapid response. Through automated data analysis and anomaly detection, enterprises can monitor operational status in real time, quickly identify potential problems, and reduce losses caused by delays. Secondly, integrating multi-dimensional data enables management to make more scientific decisions based on data and reduce the risks brought by personal subjective judgment. In addition, the establishment of anomaly detection models and automatic notification mechanisms significantly improves the response speed and efficiency of exception handling, ensuring that enterprises can respond to problems quickly. The record of each operation forms a traceable log, which is convenient for subsequent analysis and auditing, and improves the transparency of enterprise management. By conducting process evaluation and feedback collection after the exception handling is completed, a continuous improvement mechanism is established to ensure that enterprise management is continuously optimized to adapt to environmental changes. Finally, regular employee training and simulation exercises enhance the team's response capabilities, ensure efficient response when real anomalies occur, and reduce the impact of anomalies on enterprise operations.

[0091] S200, establish a responsibility list for the exception handling department, classify the exception status, and determine the exception level;

[0092] like Figure 3 As shown, the above-mentioned establishment of a responsibility list of the exception handling department, classification of the abnormal status, and determination of the abnormal level include:

[0093] S210, obtaining the departments and responsibilities for handling various types of exceptions;

[0094] S220, for the identified potential abnormal data, identify the type and degree of the abnormality;

[0095] S230, classify the identified anomalies, determine the source of the anomalies, determine the system to which the anomalies are sent by analyzing the anomaly data fields, clarify the nature of the identified anomalies, and determine the emergency level of the anomalies.

[0096] In the embodiment of the present invention, the steps of identifying potential abnormal data, marking the type and degree of the abnormality, and classifying the identified abnormality are as follows:

[0097] Analyze the weight of each abnormality type in the abnormality report, and obtain the abnormality type with the highest weight as the abnormality type corresponding to the abnormality. Specifically:

[0098]

[0099] Where t represents the feature word, i.e. the anomaly type, d represents the document describing the anomaly, i.e. the anomaly report, N represents the total number of documents, DF(t) represents the number of documents containing the anomaly type t, and TF_IDF(t,d) represents the weight of the anomaly type t in the anomaly report d. The higher the value, the more representative the anomaly type t is in the anomaly report d.

[0100] Severity scores are calculated based on the anomaly type and impact:

[0101]

[0102] Among them, ω i Represents the weight of the anomaly type, x i The feature value representing the current anomaly.

[0103] In this step, in order to identify potential abnormal data, the type and degree of the abnormality must be identified, which can be achieved by analyzing the abnormal data field. Through in-depth analysis of the data, the source of the abnormality can be determined, such as whether it comes from the attendance system, project management tools, or financial systems, so as to clarify the nature and urgency of the identified abnormality. Next, according to the characteristics of the abnormality, it is classified, for example, the abnormality is divided into different types such as technical problems, personnel shortages, equipment failures, etc., and corresponding processing procedures and responsibility lists are formulated according to different types of abnormalities.

[0104] In addition, in order to ensure that each type of exception has a targeted handling team, a detailed list of departmental responsibilities needs to be created to specify the specific responsibilities and handling procedures of each department in different exception situations. This not only helps to respond quickly, but also improves processing efficiency and ensures that all departments can collaborate in an orderly manner when faced with emergencies. By recording the types of exceptions and their responses, data support is provided for subsequent exception management, which can help companies accumulate experience and improve in future exception handling.

[0105] By clarifying the departments and responsibilities for handling various types of exceptions, it is helpful to quickly find the right handling team when an exception occurs, avoiding delayed responses due to poor information flow. Secondly, effective classification and analysis of potential exceptions ensures that each exception has a targeted handling solution, which improves handling efficiency and effectiveness. In addition, the ability to conduct quantitative analysis based on the characteristics of the exceptions enables enterprises to prioritize the types of exceptions with the greatest impact, thereby optimizing resource allocation and handling strategies.

[0106] S300, obtaining the attendance status of all employees in the enterprise on that day, and matching them using the employee database based on the attendance status and the corresponding relationship between the internal rank and processing capability of the enterprise, to screen out processing personnel who meet the processing requirements;

[0107] like Figure 4 As shown, the attendance status of all employees in the enterprise on that day is obtained, and based on the attendance status, the corresponding relationship between the internal rank and processing capacity of the enterprise is combined, and the employee database is used for matching to screen out processing personnel who meet the processing requirements, specifically including:

[0108] S310, extracting the attendance records of the day from the attendance system, screening the currently on-duty employees, and extracting the rank and processing capability information of the currently on-duty employees from the employee database;

[0109] S320, reading the access level involved in the abnormal data, and performing a secondary screening of the current employees according to the access level and the employee rank;

[0110] S330, for the remaining employees after the second screening, determine the exceptions that can be handled by employees of each job grade and processing ability information, and select a list of all processing personnel who are eligible to handle this exception from the remaining employees after the second screening, and the list is sorted from high to low in processing ability.

[0111] In this step, the attendance records for the day are extracted from the attendance system to obtain detailed information about the employees currently on duty. This process involves a systematic review of the attendance records to ensure that all employees who were on duty on that day are accurately identified. Next, the rank and processing capacity information of these employees on duty is extracted from the employee database to provide basic data for the subsequent screening process. The rank and processing capacity information will be used as key parameters to determine the suitability of employees in handling specific exceptions.

[0112] The system will then read the access level involved in the abnormal data and conduct a secondary screening based on the employee's rank. At this point, the determination of the access level is crucial because some abnormalities may require employees with specific permissions to handle them. For example, financial abnormalities may only be handled by senior staff in the finance department. Through this screening, it is ensured that only those employees who have the necessary permissions to handle the abnormality can enter the next screening stage.

[0113] For employees who have passed the secondary screening, the system will analyze each employee's rank and processing capabilities to determine the exceptions they can handle. This analysis takes into account not only the employee's technical capabilities, but also their experience with similar issues to ensure that the most suitable person is selected to handle a specific exception. Ultimately, the system will generate a list of employees who meet the processing requirements and sort them from high to low by processing capabilities. This sorting method ensures that within a limited time, the most capable employees can receive processing notifications first, thereby improving the efficiency and effectiveness of exception handling.

[0114] By systematically extracting attendance records and employee information, companies can accurately identify employees on duty that day and avoid wasting human resources. Subsequently, secondary screening is performed based on access levels and employee ranks to ensure that only employees with corresponding permissions and capabilities participate in exception processing, which not only improves the effectiveness of processing, but also reduces the risk of processing failure due to insufficient permissions.

[0115] In addition, the employee list sorted by processing capability enables the company to respond quickly to various abnormal situations. When an abnormality occurs, notifications can be quickly sent to the most capable employees, thereby reducing response time and improving processing efficiency. Through this refined personnel matching and screening mechanism, when faced with emergencies, the company can respond with higher accuracy and speed, ensuring that abnormalities can be handled promptly and effectively.

[0116] S400, sending a notification to the processing personnel, setting a confirmation feedback mechanism, monitoring the confirmation feedback status, and if no confirmation feedback is received, calling the enterprise camera equipment to collect images of the current office area;

[0117] like Figure 5 As shown, the notification is sent to the processing personnel, and a confirmation feedback mechanism is set to monitor the confirmation feedback status. If no confirmation feedback is received, the enterprise camera equipment is called to collect the current office area image, which specifically includes:

[0118] S410, obtaining the information of the person ranked first in the processing personnel list, and sending a processing notification to the person, wherein the processing notification includes the abnormal content that needs to be processed;

[0119] S420, setting a confirmation feedback mechanism, dividing the abnormal severity scores into segments, and setting a response time for each score segment;

[0120] S430, reading the severity score corresponding to the exception, analyzing the response time required for the severity score, and determining whether a confirmation feedback from the processing personnel is received within the response time after a processing notification is sent to the processing personnel;

[0121] S440, a timeout mechanism is set. If no confirmation is received within the response time, it is marked as no feedback, and the camera equipment in the enterprise is called to collect images of the current office area.

[0122] This step will obtain the information of the first person in the list of screened processing personnel. This step is critical because usually, the first person in the list is the most suitable employee to handle the current exception. Send a processing notification to the employee, including the type of exception to be handled, the description of the exception, and the urgency of the handling. This notification should be sent through multiple channels (such as email, SMS, etc.) to ensure that the information is received in time.

[0123] Next, set up a confirmation feedback mechanism to segment the severity scores of exceptions so that different response times can be set for different levels of exceptions. For example, exceptions with higher severity scores may require confirmation feedback within 15 minutes, while those with lower severity scores may allow longer time (such as 30 minutes). This mechanism is designed to reasonably allocate processing time according to the impact of the exception and ensure that high-priority exceptions are responded to in a timely manner.

[0124] After sending the processing notification, determine whether the confirmation feedback from the processing personnel is received within the set response time. If the confirmation is received within the specified time, the system will record this feedback and start the subsequent processing process; if the confirmation is not received, it will be marked as no feedback and the timeout mechanism will be activated.

[0125] The activation of the timeout mechanism means that the system will call the camera equipment inside the enterprise to collect images of the current office area. At this time, the system will perform face recognition to ensure that the collected information meets privacy compliance requirements and use efficient algorithms to reduce the misrecognition rate. Through this step, the enterprise can monitor the status of relevant personnel in real time while ensuring privacy, so as to effectively handle notifications again.

[0126] Through a clear confirmation feedback mechanism, enterprises can monitor the response of processing personnel in real time to ensure that each exception can be handled in the shortest possible time. Setting segmented response time helps to reasonably allocate resources according to the severity of the exception and optimize the exception handling process.

[0127] S500, if the person is identified as being within the visible range, a notification of the person's location and handling of the abnormality is sent to other employees closest to the person;

[0128] like Figure 6 As shown, if the person is identified as being within the visible range, a notification of the location and handling of the abnormality is sent to other employees closest to the person, specifically including:

[0129] S510, collecting personnel information from the office area image, identifying the real-time location of the personnel, and determining whether the processing personnel are within the visible range captured by the camera;

[0130] S520, obtaining location information of the processing personnel, including the floor, area and workstation;

[0131] S530, establishing an enterprise office layout model, the model includes all walkable roads, and updating all employee positions in the model in real time, wherein a node in the model represents the position of each employee and the intersection of channels, and an edge represents a walkable road between two points;

[0132] S540, assign a weight to each edge, representing the path length or travel cost, find the shortest path from the processing staff's location to another employee, and calculate the path length:

[0133]

[0134] Among them, d i Indicates the distance of each segment on the path;

[0135] S550, comparing the path lengths, obtaining other employees closest to the processing personnel, and sending a prompt notification to the other employees, wherein the prompt notification includes the person requiring prompting, i.e., the processing personnel, the current location of the processing personnel, and a summary of the abnormal information requiring processing.

[0136] This step collects personnel information from the acquired office area images. This process includes identifying all employees in the image, especially those who need to handle exceptions, to determine their real-time location. The system will analyze the images captured by the camera and use facial recognition or other image processing technologies to determine whether the processing personnel are within the visual range.

[0137] Next, the system will obtain the specific location information of the processing personnel, including the floor, area and workstation they are in. This step is crucial because knowing the employee’s accurate location will help with subsequent notification and processing processes.

[0138] The system will then build a model of the corporate office layout. The model will include all walkable roads, passages, and connections between various areas. In the model, each node represents the location of an employee or the intersection of passages, while an edge represents a walkable path between two points. In this way, the system can update the location of all employees in real time, ensuring that the model always reflects the current office environment.

[0139] Each edge is assigned a weight, which represents the path length or travel cost. Using this model, the system calculates the shortest path to other employees from the current processing personnel's location, ensures that the shortest travel path between the processing personnel and other notifiable employees is found, and calculates the path length.

[0140] Finally, the system will compare the lengths of each path to find the other employee closest to the processing person. Once the nearest employee is confirmed, the system will send a prompt notification to the employee. The notification content includes the name of the person who needs to be processed, the current location of the processing person, and a summary of the exception information to be processed. The purpose of this is to ensure that the employee can quickly understand the task and take appropriate measures as soon as possible.

[0141] By collecting personnel information from office area images, the system can quickly identify the actual location of the processing personnel and determine the optimal notification strategy. This real-time monitoring method ensures that the company can take immediate action when faced with sudden abnormalities and reduce response time.

[0142] Building a corporate office layout model and updating employee locations in real time gives the system greater flexibility in handling notifications. By calculating the shortest path, the system can ensure that notifications are delivered to the most appropriate employees in the most effective way, reducing communication delays caused by geographic location. This optimization not only improves work efficiency, but also increases employees' participation and sense of urgency in exception handling.

[0143] In addition, this location-based notification strategy can make full use of team resources, ensuring that the right person can be quickly found to handle specific exceptions at critical moments, avoiding waste of resources. With clear notification content, recipients can quickly understand the importance of the task, reducing misunderstandings and delays in information transmission.

[0144] S600, if the person is not found within the visible range, the system will slide down in sequence to match the next qualified processing person and repeat the notification process, and continue monitoring until the confirmation feedback from the person is obtained.

[0145] Since the system can match processing personnel in descending order, enterprises can quickly find alternative processing personnel when dealing with sudden abnormalities, ensuring that processing work can proceed continuously and reducing the risk of delays caused by lack of personnel.

[0146] Figure 7 The structural block diagram of the enterprise management data governance system based on big data provided by the present invention is as follows: Figure 7 As shown, including:

[0147] The data integration and anomaly identification module 100 is used to integrate data from various systems and perform data analysis, automatically identify potential anomalies, and clarify the source and nature of the anomalies;

[0148] The exception handling responsibility and classification module 200 is used to establish a responsibility list of the exception handling department, classify the exception status, and determine the exception level;

[0149] The attendance status and personnel matching module 300 is used to obtain the attendance status of all employees in the enterprise on that day, and match them using the employee database based on the attendance status and the corresponding relationship between the internal rank and processing capability of the enterprise to screen out processing personnel that meet the processing requirements;

[0150] The notification and confirmation feedback module 400 is used to send notifications to processing personnel, set up a confirmation feedback mechanism, monitor the confirmation feedback status, and call the enterprise camera equipment to collect images of the current office area if no confirmation feedback is received;

[0151] The visual range positioning and notification module 500 is used to send a location notification and handle anomaly notification to other employees closest to the person if the person is identified as being within the visual range;

[0152] The sequence-down monitoring module 600 is used to, if the person is not found within the visible range, to perform sequence-down, match the next qualified processing person and repeat the notification process, and continue monitoring until the confirmation feedback from the person is obtained.

[0153] like Figure 8 As shown, the data integration and anomaly identification module 100 includes:

[0154] The data source integration and extraction unit 110 is used to determine the data sources that need to be integrated, extract the required data from each system, and integrate them in a unified format;

[0155] The anomaly detection model building unit 120 is used to build an anomaly detection model, input the integrated data into the established anomaly detection model, and identify potential abnormal data.

[0156] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0157] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0158] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0159] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0161] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0162] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A data governance method for enterprise management based on big data, characterized in that: The method comprises: Integrate data from various systems and perform data analysis to automatically identify potential anomalies and clarify the source and nature of anomalies; Establish a list of responsibilities for the exception handling department, classify the exception status, and determine the exception level; Obtain the attendance status of all employees in the company on that day, and based on the attendance status, combine the corresponding relationship between the internal rank and processing capacity of the company, use the employee database for matching, and screen out processing personnel who meet the processing requirements; Send notifications to processing personnel, set up a confirmation feedback mechanism, monitor the confirmation feedback status, and call the enterprise camera equipment to collect images of the current office area if no confirmation feedback is received; If the person is identified as being within the visible range, a notification of the person's location and handling of the abnormality is sent to other employees closest to the person; If the person is not found within the visible range, the system will scroll down in order to match the next qualified person and repeat the notification process, and continue monitoring until confirmation feedback from the person is obtained.

2. The method according to claim 1, characterized in that The above mentioned integration of data from various systems and data analysis to automatically identify potential anomalies and clarify the source and nature of the anomalies include: Determine the data sources that need to be integrated, extract the required data from each system, and integrate them in a unified format; Build an anomaly detection model and input the integrated data into the established anomaly detection model to identify potential abnormal data.

3. The method according to claim 2, characterized in that The anomaly detection model is constructed, and the integrated data is input into the established anomaly detection model to identify potential abnormal data and mark the type and degree of the anomaly, specifically: Build an anomaly detection model to measure the degree of anomaly Z of the data point: Among them, X is the feature value to be detected, μ is the mean of the feature, and σ is the standard deviation of the feature; Determine the value of the abnormality level Z. If Z>3 or Z<3, it is determined that the feature value has a potential abnormality and an abnormality report is generated.

4. The method according to claim 3, characterized in that The above mentioned establishment of a responsibility list of the exception handling department, classification of the abnormal status, and determination of the abnormal level include: Obtain the departments responsible for handling various exceptions and their responsibilities; For the identified potential abnormal data, identify the type and degree of the abnormality; Classify the identified anomalies, determine the source of the anomaly, determine the system to which the anomaly is sent by analyzing the anomaly data field, clarify the nature of the identified anomaly, and determine the urgency level of the anomaly.

5. The method according to claim 4, characterized in that The method of identifying potential abnormal data, marking the type and degree of the abnormality, and classifying the identified abnormality is specifically as follows: Analyze the weight of each abnormality type in the abnormality report, and obtain the abnormality type with the highest weight as the abnormality type corresponding to the abnormality. Specifically: Where t represents the feature word, i.e. the anomaly type, d represents the document describing the anomaly, i.e. the anomaly report, N represents the total number of documents, DF(t) represents the number of documents containing the anomaly type t, and TF_IDF(t,d) represents the weight of the anomaly type t in the anomaly report d. The higher the value, the more representative the anomaly type t is in the anomaly report d. Severity scores are calculated based on the anomaly type and impact: Among them, ω i Represents the weight of the anomaly type, x i The feature value representing the current anomaly.

6. The method according to claim 5, characterized in that The method of obtaining the attendance status of all employees in the enterprise on that day, and matching them with the employee database based on the attendance status and the corresponding relationship between the internal rank and processing capability of the enterprise, and screening out processing personnel who meet the processing requirements, specifically includes: Extract the attendance records of the day from the attendance system, screen the currently on-duty employees, and extract the rank and processing capacity information of the currently on-duty employees from the employee database; Read the access level involved in this abnormal data, and conduct a secondary screening of the current employees based on the access level and employee rank; For the remaining employees after the second screening, determine the exceptions that can be handled by employees of each rank and processing capability information, and accordingly select a list of all handlers who are eligible to handle this exception from the remaining employees after the second screening, and the list is sorted from high to low in terms of processing capability.

7. The method according to claim 6, characterized in that The notification is sent to the processing personnel, and a confirmation feedback mechanism is set up to monitor the confirmation feedback status. If no confirmation feedback is received, the enterprise camera equipment is called to collect the current office area image, which specifically includes: Obtaining the information of the first person in the processing personnel list, and sending a processing notification to the first person, wherein the processing notification includes the abnormal content that needs to be processed; Set up a confirmation feedback mechanism, divide the abnormal severity scores into segments, and set a response time for each score segment; Read the severity score corresponding to the exception, analyze the response time required for the severity score, and determine whether confirmation feedback from the handler is received within the response time after sending the handling notification to the handler; Set a timeout mechanism. If no confirmation is received within the response time, it will be marked as no feedback, and the camera equipment in the enterprise will be called to collect images of the current office area.

8. The method according to claim 7, characterized in that If the person is identified as being within the visible range, a notification of the person's location and abnormal handling is sent to other employees closest to the person, specifically including: Collect personnel information from office area images, identify personnel's real-time location, and determine whether the processing personnel are within the visible range captured by the camera; Obtain the location information of the processing personnel, including the floor, area and workstation; Establish an enterprise office layout model, which includes all walkable roads, and update all employee positions in real time in the model. The nodes in the model represent the position of each employee, the intersection of the passages, and the edges represent the walkable roads between two points. Assign a weight to each edge, representing the path length or travel cost, find the shortest path from the processing worker's location to another worker, and calculate the path length: Among them, d i Indicates the distance of each segment on the path; Compare the path lengths, obtain other employees closest to the processing personnel, and send a prompt notification to the other employees, wherein the prompt notification includes the person who needs to be prompted, that is, the processing personnel, the current location of the processing personnel, and a summary of the abnormal information that needs to be processed.

9. An enterprise management data governance system based on big data, characterized in that: The system comprises: Data integration and anomaly identification module, which is used to integrate data from various systems and perform data analysis, automatically identify potential anomalies, and clarify the source and nature of anomalies; The exception handling responsibility and classification module is used to establish a responsibility list for the exception handling department, classify the exception status, and determine the exception level; The attendance status and personnel matching module is used to obtain the attendance status of all employees in the enterprise on that day, and based on the attendance status, combined with the corresponding relationship between the internal rank and processing capacity of the enterprise, use the employee database for matching, and screen out processing personnel who meet the processing requirements; The notification and confirmation feedback module is used to send notifications to processing personnel, set up a confirmation feedback mechanism, monitor the confirmation feedback status, and call the enterprise camera equipment to collect images of the current office area if no confirmation feedback is received; The visual range positioning and notification module is used to send location and abnormal handling notifications to other employees closest to the person if the person is identified as being within the visual range; The sequence-down monitoring module is used to slide down in sequence if the person is not found within the visible range, match the next qualified processing person and repeat the notification process, and continue monitoring until the confirmation feedback from the person is obtained.

10. The system according to claim 8, characterized in that The data integration and anomaly identification module includes: The data source integration and extraction unit is used to determine the data sources that need to be integrated, extract the required data from each system, and integrate them in a unified format; The anomaly detection model building unit is used to build an anomaly detection model, input the integrated data into the established anomaly detection model, and identify potential abnormal data.

Images