A collaborative monitoring system and method for digital transformation process

Abstract

The present invention discloses a digital transformation process collaborative monitoring system and method, which relates to the field of digital transformation monitoring. The method includes: obtaining all data that need to be digitally transformed; performing digital transformation according to the actual business process, analyzing data relationships according to a preset process mining algorithm, building a business process model, performing digital verification and generating a digital verification score; when the digital verification score is greater than a preset threshold, the verification is passed, and the degree of coordination between activities is calculated; performing an abnormal analysis on the real-time data stream, and when an abnormality is identified, marking the data of the abnormal data and the activity whose degree of coordination between the abnormal data and the activity is greater than the threshold, and generating an early warning signal. By constructing a direct correlation relationship matrix and a causal relationship set, the integration of multi-source heterogeneous data and multi-level correlation analysis are realized, and the accuracy and flexibility of calculating the degree of coordination between business activities are comprehensively improved by combining the digital verification score and the dynamic adaptation model.

Description

Technical Field

[0001] The present invention relates to the field of digital transformation monitoring, and in particular to a system and method for collaborative monitoring of a digital transformation process. Background Art

[0002] With the advancement of digital transformation, enterprises and organizations increasingly rely on digital means to optimize business processes in order to improve efficiency, reduce costs and enhance competitiveness. However, in the process of digital transformation of business processes, due to the diversity of data sources, complex business flows and the lack of effective monitoring tools, the following problems are easily caused: data dispersion and lack of relevance. Business data usually comes from multiple heterogeneous systems. There is a lack of effective correlation analysis between data, which makes it difficult to support the optimization of the overall process. In the existing technology, the monitoring methods for business processes often only focus on a single indicator of local data, and cannot effectively evaluate the global coordination of business processes; the model adaptability is insufficient. Traditional business process modeling methods are mostly based on static rules or experience-driven, and it is difficult to dynamically adapt to the complex changes in actual business scenarios. In the existing technical solutions, model verification is often limited to specific scenarios or hypothetical conditions, and it is impossible to accurately capture abnormal events and their impacts in business activities; lack of coordination and real-time evaluation methods. In the current technology, the evaluation method for the coordination between business activities is relatively single, which is difficult to fully reflect the relevance and coordination between activities. The lack of a real-time scoring mechanism makes it difficult to dynamically evaluate the digital transformation effect of business processes. Summary of the invention

[0003] Based on the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a digital transformation process collaborative monitoring system and method to solve the above-mentioned technical problems.

[0004] To achieve the above object, the present invention provides the following technical solution: a collaborative monitoring method for a digital transformation process, comprising:

[0005] Obtain all data that needs to be digitally transformed and divide the data into production data, operational data, and support data according to business levels;

[0006] Carry out digital transformation according to actual business processes, analyze data relationships according to preset process mining algorithms, and build relationship models based on data relationships;

[0007] Building a business process model based on the relationship model, digitally verifying the business process model, and generating a digital verification score;

[0008] When the digital verification score is greater than a preset digital verification score threshold, the business process model is verified and the coordination degree between the activities is calculated;

[0009] Perform anomaly analysis on real-time data streams. When an anomaly is identified, mark the data of activities whose coordination degree between the abnormal data and the activities is greater than the threshold, and generate an early warning signal.

[0010] The present invention is further configured such that the business process is composed of one or more activities, an instance of the activity execution is an event, the event includes an activity name, an identification number, a timestamp and event data, wherein the event data includes event input data and event output data.

[0011] The present invention is further configured to analyze data relationships according to a preset process mining algorithm, including:

[0012] Obtain an original event data set, extract the event log of the business process execution from the original event data set, distinguish the events according to the activity name, identification number, timestamp, event input data and event output data, and obtain an event set , extract all the activities that appear and form an activity set , where the event set Elements in ;

[0013] Define the direct follow-up relationship matrix ,in, To indicate activity The corresponding event directly follows the activity The frequency of the corresponding events, where the activity The corresponding event directly follows the activity The data transmission of the corresponding event refers to: activity The input data of the corresponding event is activity The output data of the corresponding event;

[0014] Iterate over the event collection Event pairs in , get the event set The direct follow-up relationship matrix ;

[0015] According to the direct follow-up relationship matrix The analysis results in a causal relationship set, a parallel relationship set and an irrelevant relationship set.

[0016] The present invention is further configured to: according to the direct follow-up relationship matrix The analysis results in a causal relationship set, a parallel relationship set, and an irrelevant relationship set, including:

[0017] When the activity The corresponding event directly follows the activity The frequency of the corresponding event is greater than W1, and the activity The corresponding event directly follows the activity When the frequency of the corresponding event is zero, Enter the causal relationship set ;

[0018] When the activity The corresponding event directly follows the activity The frequency of the corresponding event is greater than W2, and the activity The corresponding event directly follows the activity When the frequency of the corresponding event is greater than W2, Input parallel relation set ;

[0019] When the activity The corresponding event directly follows the activity The corresponding event has a frequency of zero and the activity The corresponding event directly follows the activity When the frequency of the corresponding event is zero, Input irrelevant relation set .

[0020] The present invention is further configured to construct a relationship model according to data relationships, including:

[0021] Bringing together activities , causal relationship set , Parallel Relation Set and irrelevant relation set Set as model input;

[0022] Defining a Transformation Set , group the activities together Set as Transformation Collection , where the transformation set Each activity in is set as a node;

[0023] For the causal relationship set , create the first library : ; For parallel relations , create the second library : , define the set of places : ;

[0024] Creating Causal Arcs , , create parallel relationship arcs , , define the comprehensive arc set , ;

[0025] According to the conversion set , library set and the comprehensive arc set Building a relationship model .

[0026] The present invention is further configured to perform digital verification on the business process model to generate a digital verification score, and the calculation logic of the digital verification score is: ,in, Score digital verification, A business process is defined as a set of transformations The number of activities in The business process is not defined to the transformation set The number of activities in For activities Alignment to the relational model The price of the upcoming event Alignment to the relational model The minimum number of modification or insertion and deletion operations required, For the relational model The number of activities in .

[0027] The present invention is further configured such that the calculation logic of the coordination degree between activities is: ,in, For activities and The degree of coordination between For activities and The set of paths between For activities and The path between is the weight of the path, and the calculation logic is: the ratio of the events corresponding to the path to the total number of events, is the total number of activities.

[0028] The present invention also provides a digital transformation process collaborative monitoring system, the system comprising:

[0029] Data acquisition module: acquires all data required for digital transformation and divides the data into production data, operational data, and support data according to business levels;

[0030] Model building module: Carry out digital transformation according to the actual business process, analyze data relationships according to the preset process mining algorithm, and build a relationship model based on the data relationship;

[0031] Model verification module: constructs a business process model based on the relational model, performs digital verification on the business process model, and generates a digital verification score;

[0032] Collaborative calculation module: when the digital verification score is greater than the preset digital verification score threshold, the business process model is verified and the degree of collaboration between the activities is calculated;

[0033] Abnormal warning module: Perform abnormal analysis on real-time data streams. When an abnormality is identified, the data of activities with a coordination degree greater than a threshold will be marked to generate a warning signal.

[0034] The present invention provides a digital transformation process collaborative monitoring system and method. The method obtains all data that need to be digitally transformed, divides the data into production data, operation data and support data according to the business level; performs digital transformation according to the actual business process, analyzes the data relationship according to the preset process mining algorithm, and builds a relationship model according to the data relationship; builds a business process model according to the relationship model, performs digital verification on the business process model, and generates a digital verification score; when the digital verification score is greater than the preset digital verification score threshold, the business process model is verified, and the coordination degree between activities is calculated; anomaly analysis is performed on the real-time data stream, and when anomalies are identified, the abnormal data and the data of the activity whose coordination degree between the activities is greater than the threshold are marked, and an early warning signal is generated, and the beneficial effects produced include:

[0035] 1. Comprehensive data integration and analysis capabilities. By building a direct correlation matrix and a causal relationship set, multi-level correlation analysis of business activity data is achieved, which effectively solves the problem of scattered and isolated data in existing technologies, supports the integration of multi-source data from heterogeneous systems, and ensures the comprehensiveness and accuracy of data analysis;

[0036] 2. Accurate model verification and dynamic adaptation: Through predefined business process models, business activities are digitally verified and digital verification scores are generated, which can dynamically reflect the adaptability of the model to the actual process, overcoming the shortcomings of traditional static modeling methods that cannot adapt to business changes. Digital verification scores not only evaluate the overall process effect, but also correct the model in combination with unused activities and abnormal activities, enhancing the dynamic adaptability of the model;

[0037] 3. Accuracy and flexibility in calculating activity synergy. By calculating synergy, the synergy effect between business activities is quantified, which makes up for the lack of analysis of the relationship between activities in existing methods. By setting multiple thresholds, the direct correlation and parallel relationship of activities are refined, making the synergy evaluation more scientific.

[0038] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0040] Figure 1 A flowchart of a method for collaborative monitoring of a digital transformation process is shown as an exemplary embodiment of the present invention;

[0041] Figure 2 A schematic diagram of the structure of a digital transformation process collaborative monitoring system is shown as an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0043] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0044] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0045] Embodiment 1

[0046] A collaborative monitoring method for digital transformation process, such as Figure 1 As shown, including:

[0047] Obtain all data that needs to be digitally transformed and divide the data into production data, operational data, and support data according to business levels;

[0048] Carry out digital transformation according to actual business processes, analyze data relationships according to preset process mining algorithms, and build relationship models based on data relationships;

[0049] Building a business process model based on the relationship model, digitally verifying the business process model, and generating a digital verification score;

[0050] When the digital verification score is greater than a preset digital verification score threshold, the business process model is verified and the coordination degree between the activities is calculated;

[0051] Perform anomaly analysis on real-time data streams. When an anomaly is identified, mark the data of activities whose coordination degree between the abnormal data and the activities is greater than the threshold, and generate an early warning signal.

[0052] The present invention is further configured such that the business process is composed of one or more activities, the instance of the activity execution is an event, the event includes an activity name, an identification number, a timestamp and event data, wherein the event data includes event input data and event output data. Specifically, a business process is composed of one or more activities, each activity represents a basic unit in the business process, the specific execution of each activity is defined as an event, and the event is a specific manifestation of the activity execution. Each event contains the following core elements: activity name, which identifies the activity to which the event belongs; identification number, which assigns a unique identification to each event to distinguish different instances; timestamp: records the execution time of the event, supports time series analysis and process tracking; event data, input data: data required for activity execution; output data: data generated after the activity is executed.

[0053] The present invention is further configured to analyze data relationships according to a preset process mining algorithm, including:

[0054] Obtain an original event data set, extract the event log of the business process execution from the original event data set, distinguish the events according to the activity name, identification number, timestamp, event input data and event output data, and obtain an event set , extract all the activities that appear and form an activity set , where the event set Elements in ;

[0055] Define the direct follow-up relationship matrix ,in, To indicate activity The corresponding event directly follows the activity The frequency of the corresponding events, where the activity The corresponding event directly follows the activity The data transmission of the corresponding event refers to: activity The input data of the corresponding event is activity The output data of the corresponding event; specifically, Indicates activity and Activities The direct relationship between them is determined by the following logic: : If the activity The output data of the activity is used as The input data is Reflects the strength of their relationship; : If there is no direct relationship between the two activities.

[0056] Iterate over the event collection Event pairs in , get the event set The direct follow-up relationship matrix ;

[0057] According to the direct follow-up relationship matrix The analysis results in a causal relationship set, a parallel relationship set, and an irrelevant relationship set. Through the construction of an event set, each activity instance in the business process can be analyzed and tracked in a fine-grained manner. Through the input and output data of events, all-round monitoring of activity status and business processes is achieved. Through the direct association relationship matrix, the data flow and intensity between activities are clarified, providing a data-driven foundation for the optimization of business processes. The causal relationship set further reveals the time dependency between activities, laying the foundation for the dynamic analysis of complex processes.

[0058] The present invention is further configured to: according to the direct follow-up relationship matrix The analysis results in a causal relationship set, a parallel relationship set, and an irrelevant relationship set, including:

[0059] When the activity The corresponding event directly follows the activity The frequency of the corresponding event is greater than W1, and the activity The corresponding event directly follows the activity When the frequency of the corresponding event is zero, Enter the causal relationship set ;

[0060] When the activity The corresponding event directly follows the activity The frequency of the corresponding event is greater than W2, and the activity The corresponding event directly follows the activity When the frequency of the corresponding event is greater than W2, Input parallel relation set ;

[0061] When the activity The corresponding event directly follows the activity The corresponding event has a frequency of zero and the activity The corresponding event directly follows the activity When the frequency of the corresponding event is zero, Input irrelevant relation set Specifically, through the analysis of the direct correlation matrix, the relationship between activities is divided into causal relationship set, parallel relationship set and unrelated set to clearly describe the interactive relationship between business activities; the association between activities is reflected by the direct frequency of events, W1 is the lower limit of causal relationship, and activity pairs below this value are usually not considered causally related, W2 is the lower limit of parallel relationship, and activity pairs above this value are generally classified into parallel sets. W1 and W2 are specifically set according to specific scenarios and are not restricted here; by dividing causal, parallel and unrelated sets, the activity interaction logic in the business process is clearly described, which makes up for the defect of insufficient processing of complex relationships in traditional analysis methods.

[0062] The present invention is further configured to construct a relationship model according to data relationships, including:

[0063] Bringing together activities , causal relationship set , Parallel Relation Set and irrelevant relation set Set as model input;

[0064] Defining a Transformation Set , group the activities together Set as Transformation Collection , where the transformation set Each activity in is set as a node;

[0065] For the causal relationship set , create the first library : ; For parallel relations , create the second library : , define the set of places : Specifically, place sets are used to represent the relationship between activities in business process modeling. Activity nodes are connected through places to reflect the causal relationship, parallel relationship and state change of activities, and can clearly describe the dynamic behavior of the process.

[0066] Creating Causal Arcs , , create parallel relationship arcs , , define the comprehensive arc set , ; Specifically, the causal arc indicates that there is a logical sequential dependency between activities, indicating that the completion of one activity is a necessary condition for the start of another activity; the parallel arc indicates that two activities have a mutual dependency; the comprehensive arc set is a set of causal arcs and parallel arcs, which represents the direct relationship between all activities in the business process;

[0067] According to the conversion set , library set and the comprehensive arc set Building a relationship model Specifically, by dividing into causal, parallel and unrelated sets, all possible association modes of activities in the business process are fully described. The hierarchical structure of transformation sets and place sets provides a clear logical framework for relational modeling of complex processes.

[0068] The present invention is further configured to perform digital verification on the business process model to generate a digital verification score, and the calculation logic of the digital verification score is: ,in, Score digital verification, A business process is defined as a set of transformations The number of activities in The business process is not defined to the transformation set The number of activities in For activities Alignment to the relational model The price of the upcoming event Alignment to the relational model The minimum number of modification or insertion and deletion operations required, For the relational model Specifically, by analyzing the deviation between the activity set defined by the model and the actual activity data, the accuracy of the business process model is evaluated, the part of the activity data that is not correctly associated or matched by the process model is detected, and the accuracy of the process model is determined. The above calculation logic provides a clear scoring indicator to quantify the matching degree between the business process model and the actual activity data, which helps to quickly evaluate the model's coverage capability and optimization needs.

[0069] The present invention is further configured such that the calculation logic of the coordination degree between activities is: ,in, For activities and The degree of coordination between For activities and The set of paths between For activities and The path between is the weight of the path, and the calculation logic is: the ratio of the events corresponding to the path to the total number of events, is the total number of activities. Specifically, the above calculation logic calculates two activities and The degree of coordination between , which is used to quantify the collaborative effect or dependency between activities; the synergy is determined by the path weight The weighted average is calculated by considering all possible path sets , integrate the synergistic relationship of multiple activities, and make the results consistent and comparable among all activities through normalization;

[0070] Anomaly analysis is performed on real-time data streams. When anomalies are identified, the data of activities whose synergy between the abnormal data and the activities is greater than the threshold is marked to generate an early warning signal. Specifically, anomaly analysis on real-time data streams is a prior art and will not be elaborated here. Through synergy screening, activities related to abnormal data can be accurately identified, which helps prevent the spread of anomalies and quickly troubleshoot the root cause of the problem.

[0071] Embodiment 2

[0072] See also Figure 2 , the exemplary digital transformation process collaborative monitoring system includes:

[0073] Data acquisition module: acquires all data required for digital transformation and divides the data into production data, operational data, and support data according to business levels;

[0074] Model building module: Carry out digital transformation according to the actual business process, analyze data relationships according to the preset process mining algorithm, and build a relationship model based on the data relationship;

[0075] Model verification module: constructs a business process model based on the relational model, performs digital verification on the business process model, and generates a digital verification score;

[0076] Collaborative calculation module: when the digital verification score is greater than the preset digital verification score threshold, the business process model is verified and the degree of collaboration between the activities is calculated;

[0077] Abnormal warning module: Perform abnormal analysis on real-time data streams. When an abnormality is identified, the data of activities with a coordination degree greater than a threshold will be marked to generate a warning signal.

[0078] It should be noted that the digital transformation process collaborative monitoring system provided by the above embodiment and the digital transformation process collaborative monitoring method provided by the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs operations has been described in detail in the method embodiment and will not be repeated here. In actual applications, the digital transformation process collaborative monitoring system provided by the above embodiment can distribute the above functions to different functional modules as needed, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0079] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0080] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0081] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0082] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0083] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0084] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0085] In the several embodiments provided in the present application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0086] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0087] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0088] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0089] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A collaborative monitoring method for a digital transformation process, characterized in that: include: Obtain all data that needs to be digitally transformed and divide the data into production data, operational data, and support data according to business levels; Carry out digital transformation according to actual business processes, analyze data relationships according to preset process mining algorithms, and build relationship models based on data relationships; Analyze data relationships according to a preset process mining algorithm, including: obtaining an original event data set, extracting event logs of business process execution from the original event data set, distinguishing events according to activity name, identification number, timestamp, event input data, and event output data, and obtaining an event set , extract all the activities that appear and form an activity set , where the event set Elements in ; Define the direct follow-up relationship matrix ; Traverse the event collection Event pairs in , get the event set The direct follow-up relationship matrix ; According to the direct follow-up relationship matrix Analyze and obtain causal relationship sets, parallel relationship sets, and irrelevant relationship sets; build a relationship model based on data relationships, including: , causal relationship set , Parallel Relation Set and irrelevant relation set Set as model input; define a set of transformations , group the activities together Set as Transformation Collection , where the transformation set Each activity in is set as a node; for the causal relationship set , create the first library : ; For parallel relations , create the second library : , define the set of places : ; Create causal arcs , , create parallel relationship arcs , , define the comprehensive arc set , ; According to the conversion set , library set and the comprehensive arc set Building a relationship model ; Building a business process model based on the relationship model, digitally verifying the business process model, and generating a digital verification score; When the digital verification score is greater than a preset digital verification score threshold, the business process model is verified and the coordination degree between the activities is calculated; Perform anomaly analysis on real-time data streams. When an anomaly is identified, mark the data of activities whose coordination degree between the abnormal data and the activities is greater than the threshold, and generate an early warning signal.

2. A collaborative monitoring method for a digital transformation process according to claim 1, characterized in that: The business process is composed of one or more activities, and the instance of the activity execution is an event. The event includes an activity name, an identification number, a timestamp and event data, wherein the event data includes event input data and event output data.

3. A collaborative monitoring method for a digital transformation process according to claim 1, characterized in that: According to the direct follow-up relationship matrix The analysis results in a causal relationship set, a parallel relationship set, and an irrelevant relationship set, including: When the activity The corresponding event directly follows the activity The frequency of the corresponding event is greater than W1, and the activity The corresponding event directly follows the activity When the frequency of the corresponding event is zero, Enter the causal relationship set ; When the activity The corresponding event directly follows the activity The frequency of the corresponding event is greater than W2, and the activity The corresponding event directly follows the activity When the frequency of the corresponding event is greater than W2, Input parallel relation set ; When the activity The corresponding event directly follows the activity The corresponding event has a frequency of zero and the activity The corresponding event directly follows the activity When the frequency of the corresponding event is zero, Input irrelevant relation set .

4. A collaborative monitoring method for a digital transformation process according to claim 3, characterized in that: The business process model is digitally verified to generate a digital verification score. The calculation logic of the digital verification score is: ,in, Score digital verification, A business process is defined as a set of transformations The number of activities in For business processes that are not defined to a transition set The number of activities in For activities Alignment to the relational model The price of the upcoming event Alignment to the relational model The minimum number of modification or insertion and deletion operations required, For the relational model The number of activities in .

5. A collaborative monitoring method for a digital transformation process according to claim 4, characterized in that: The calculation logic of the synergy between activities is: ,in, For activities and The degree of coordination between For activities and The set of paths between For activities and The path between is the weight of the path, and the calculation logic is: the ratio of the events corresponding to the path to the total number of events.

6. A digital transformation process collaborative monitoring system, used to implement a digital transformation process collaborative monitoring method according to any one of claims 1 to 5, characterized in that: include: Data acquisition module: acquires all data required for digital transformation and divides the data into production data, operational data, and support data according to business levels; Model building module: Carry out digital transformation according to the actual business process, analyze data relationships according to the preset process mining algorithm, and build a relationship model based on the data relationship; Model verification module: constructs a business process model based on the relational model, performs digital verification on the business process model, and generates a digital verification score; Collaborative calculation module: when the digital verification score is greater than the preset digital verification score threshold, the business process model is verified and the degree of collaboration between the activities is calculated; Abnormal warning module: Perform abnormal analysis on real-time data streams. When an abnormality is identified, the data of activities with a coordination degree greater than a threshold will be marked to generate a warning signal.

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