Business process management system and method based on data collaboration

Abstract

The invention discloses a business process management system and method based on data collaboration, and relates to the technical field of business process management, and the method comprises the steps: S10, determining a to-be-optimized business node; s20, performing traceability cooperative processing on each to-be-optimized service node to obtain a risk buried point of each to-be-optimized service node; s30, analyzing the business process management sequence of each business node to be optimized; and S40, managing the service process of each service node to be optimized. According to the invention, the abnormal reason of the to-be-optimized service node with a plurality of risk burying points can be quickly analyzed, so that the management efficiency of the system on the service process is further improved, and the risk burying points existing in the to-be-optimized service node can be eliminated to the maximum extent; and stable operation of the business process of the business node to be optimized is ensured in a short time, and effective management of the system on the import and export business process is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of business process management, and in particular to a business process management system and method based on data collaboration. Background Art

[0002] In order to expand the market, companies now open import and export businesses, which can push the company's products to the international market. Cross-border supply chain services are a complex and comprehensive system that aims to ensure the smooth progress of international trade processes while optimizing costs, improving efficiency and reducing risks. The cross-border supply chain covers a series of processes from suppliers to end consumers, including product production, logistics, warehousing, order processing, export declaration and other activities.

[0003] Import and export business includes several sub-businesses. Since each sub-business is managed by professional managers, the data interaction between the sub-businesses will not be timely, which will slow down the progress of import and export business. In addition, when the existing managers manage the progress of each sub-business, they cannot quickly analyze the reasons for the abnormality of the process of each sub-business, and cannot clarify the process management order of each sub-business. Summary of the invention

[0004] The purpose of the present invention is to provide a business process management system and method based on data collaboration to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a business process management method based on data collaboration, the method comprising: S10: Analyze the real-time business advancement index of each business node according to the business process information and business risk information of each business node of the import and export business, and determine the business node to be optimized according to the change of the real-time business advancement index; S20: Based on the influence coefficients between the service nodes to be optimized, the service nodes to be optimized are traced and collaboratively processed to obtain the risk points of the service nodes to be optimized; S30: Analyze the business process management sequence of each business node to be optimized based on the distribution of various risk points in the time dimension and business dimension; S40: managing the service process of each service node to be optimized according to the analysis result of S30.

[0006] Further, the S10 includes: S101: Take each sub-business included in the import and export business as a business node, and obtain business process information and business risk information of each business node. The business process information includes the degree to which the business node is actually executed and the time when each business requirement in the business node is actually executed. The business risk information refers to the business requirement deviation value corresponding to the business node when the actual business requirement of the business node deviates from the standard business requirement determined according to the import and export business contract. S102: randomly select a business node, and obtain the time Ti when the i-th business requirement in the selected business node is actually executed, the degree Rt of the actual execution of the selected business node at time t, and the business risk data set Mt corresponding to the selected business node at time t, Mt={y1_t,y2_t,…,yn_t}, where i=1,2,…,n, indicating that each business requirement in the selected business node is numbered in the order of the time when the business requirements are actually executed, n indicates the total number of business requirements in the selected business node, yn_t indicates the business requirement deviation value corresponding to the n-th business requirement in the selected business node at time t, and t indicates the real-time time value; according to Calculate the business advancement index of the selected business node at time t, where Gi represents the minimum value in the standard business requirement range corresponding to the i-th business requirement in the selected business node. When yi_t is within the business requirement deviation range, Pyi_t=0; when yi_t is outside the business requirement deviation range, Pyi_t=|yi_t|. yi_t represents the business requirement deviation value corresponding to the i-th business requirement in the selected business node at time t. Too many import and export products may cause the importing country to increase tariffs, etc., thereby affecting the business progress of import and export business. S103: If U_t shows a stable change over time in the time series, it means that the selected service node is not the service node to be optimized at time t; if U_t shows a fluctuating change over time in the time series, it means that the selected service node is the service node to be optimized at time t.

[0007] Further, the S20 includes: S201: storing the influence coefficients between the service nodes to be optimized in a set N, determining two service nodes to be optimized corresponding to the maximum influence coefficient values ​​stored in the set N, recording that the two service nodes to be optimized are service node A to be optimized and service node B to be optimized, and obtaining initial fluctuation time points TA and TB of service node A to be optimized and service node B to be optimized; S202: Calculate the impact lag time length value between the service node A to be optimized and the service node B to be optimized according to H_AB=|TA-TB|, input UA_TA into the lag model between the service node A to be optimized and the service node B to be optimized, and the lag model outputs the theoretical impact lag time length value G_AB between the service node A to be optimized and the service node B to be optimized, wherein UA_TA and UB_TB represent the service advancement indexes of the service node A to be optimized and the service node B to be optimized at the time TA and TB respectively; S203: According to K_AB=|H_AB-G_AB| / S_AB, the traceability coefficient of the business node B to be optimized is calculated compared with the business node A to be optimized. If K_AB>1, it means that the risk buried point of the business node B to be optimized is not the risk cause of the business node A to be optimized at the TA moment. If 0≤K_AB≤1, it means that the risk buried point of the business node B to be optimized is the risk cause of the business node A to be optimized at the TA moment, wherein S_AB represents the impact lag time error value between the business node A to be optimized and the business node B to be optimized; by judging whether there is a traceability relationship between any two business nodes to be optimized based on the lag impact time between any two business nodes to be optimized, the risk buried point situation of each business node to be optimized can be analyzed; S204: Delete the maximum value of the impact coefficient stored in the set N from the set N, repeat the operations of S201-S203, and determine the risk points of each business node to be optimized.

[0008] Further, the S30 includes: S301: Classify the risk points according to their risk causes. Risk points with the same risk causes belong to the same type of risk points. According to the distribution of the same type of risk points in time and business, predict the business process management order judgment value of each business node to be optimized. The specific prediction formula is: ; Among them, p=1,2,…,q, represents the number corresponding to each business node to be optimized, q represents the total number of business nodes to be optimized, z=1,2,…,q and z≠p, g_zp=0 or g_zp=1, when g_zp=0, it means that there is no risk buried point of the same type as the risk buried point of the business node to be optimized numbered p at time Tp in the business node to be optimized numbered z, when g_zp=1, it means that there is a risk buried point of the same type as the risk buried point of the business node to be optimized numbered p at time Tp in the business node to be optimized numbered z, Tp represents the initial fluctuation time point of the business node to be optimized numbered p, U represents the start execution time point of the import and export business, exp() represents the exponential function with e as the base, Vp represents the business process management order judgment value of the business node to be optimized numbered p; according to the distribution of risk buried points in the time dimension and the business dimension, the first business process management object is determined to ensure the smooth progress of the import and export business in a short time; S302: Take the service node to be optimized corresponding to maxVp as the first service process management object.

[0009] Further, the S40 includes: S401: After performing business process management on the first business process management object, the risk buried point corresponding to the first business process management object at the initial fluctuation time point is deleted from the risk buried points existing in each business node to be optimized; S402: Repeat the operations of S301, S302, and S401 until there are no risk points in the business node to be optimized.

[0010] A business process management system based on data collaboration, the system includes a business node determination module to be optimized, a source tracing collaborative processing module, a business process management and analysis module and a process management module; The service node determination module to be optimized is used to determine the service node to be optimized according to the change of the real-time service advancement index of each service node; The traceability collaborative processing module is used to perform traceability collaborative processing on each service node to be optimized according to the influence coefficient between each service node to be optimized, so as to obtain the risk points of each service node to be optimized; The business process management analysis module is used to analyze the business process management sequence of each business node to be optimized according to the distribution of various risk points in the time dimension and business dimension; The process management module is used to manage the service process of each service node to be optimized.

[0011] Furthermore, the service node determination module to be optimized includes a service information acquisition unit, a service advancement index calculation unit and a determination unit; The business information acquisition unit acquires business process information and business risk information of each business node; The service advancement index calculation unit calculates the real-time service advancement index of the selected service node according to the real-time service requirement deviation value of each service requirement in the selected service node and the standard service requirement range value corresponding to each service requirement in the selected service node; The determination unit determines whether the selected service node is a service node to be optimized according to the type of change of the real-time service advancement index of the selected service node over time in the time series.

[0012] Furthermore, the traceability collaborative processing module includes an initial fluctuation time acquisition unit, a theoretical impact lag time prediction unit and a traceability coefficient calculation unit; The initial fluctuation time acquisition unit selects whether to acquire the initial fluctuation time of each service node to be optimized according to the influence coefficient between each service node to be optimized; The theoretical impact lag time prediction unit predicts the theoretical impact lag time length value between the service node to be optimized A and the service node to be optimized B by inputting the impact lag time length value between the service node to be optimized A and the service node to be optimized B into the lag model between the service node to be optimized A and the service node to be optimized B; The traceability coefficient calculation unit calculates the traceability coefficient of the business node B to be optimized compared with the business node A to be optimized according to the constructed calculation model, and analyzes the risk points of the business node A to be optimized and the business node B to be optimized according to the calculation results.

[0013] Further, the business process management analysis module includes a business process management sequence judgment value prediction unit and a first business process management object determination unit; The business process management order judgment value prediction unit predicts the business process management order judgment value of each business node to be optimized according to the initial fluctuation time of each business node to be optimized and the existence of the same type of risk buried points between each business node to be optimized; The first business process management object determination unit uses the to-be-optimized business node corresponding to the maximum business process management order judgment value as the first business process management object.

[0014] Furthermore, after performing business process management on the first business process management object, the process management module deletes the risk points corresponding to the first business process management object at the initial fluctuation time point from the risk points existing in each business node to be optimized, and then executes the business process management analysis module and the process management module again until there are no risk points in the business nodes to be optimized.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention calculates the real-time business deviation value of each business requirement of each business node according to the business process information and business risk information of each business node, and predicts the real-time business promotion index of each business node based on the calculation result to realize the judgment of the business node to be optimized.

[0016] 2. The present invention analyzes the traceability of each business node to be optimized through the impact lag time between the business nodes to be optimized, thereby improving the collaborative processing capability of business data, and analyzes the risk points of each business node to be optimized based on the analysis results. This process can quickly analyze the abnormal causes of the business nodes to be optimized with multiple risk points, further improving the system's management efficiency of business processes.

[0017] 3. The present invention determines the first business process management object according to the distribution of various risk points in the time dimension and the business dimension, and ensures that after the business process management of the first business process management object, the risk points existing in the business nodes to be optimized can be eliminated to the maximum extent, and the stable progress of the business processes of the business nodes to be optimized can be ensured in a short time, further improving the system's effective management of import and export business processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a workflow of a business process management system and method based on data collaboration of the present invention; Figure 2 The present invention is a schematic diagram of the working principle structure of a business process management system and method based on data collaboration. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] like Figure 1-Figure 2 As shown, the present invention provides a business process management system and method technical solution based on data collaboration, a business process management method based on data collaboration, the method comprising: S10: Analyze the real-time business advancement index of each business node according to the business process information and business risk information of each business node of the import and export business, and determine the business node to be optimized according to the change of the real-time business advancement index; S10 includes: S101: Take each sub-business included in the import and export business as a business node. The import and export business includes the arrival management sub-business, the outbound management sub-business, the delivery management sub-business, the fund collection and payment sub-business, etc., and obtain the business process information and business risk information of each business node. The business process information includes the degree to which the business node is actually executed and the time when each business requirement in the business node is actually executed. The business risk information refers to the business requirement deviation value corresponding to the business node when the actual business requirement of the business node deviates from the standard business requirement determined according to the import and export business contract. The business requirement deviation value = the actual business requirement value - min (standard business requirement range value), min (standard business requirement range value)> 0; S102: Randomly select a service node, calculate the time Ti when the i-th service requirement in the selected service node is actually executed, and the degree Rt of the actual execution of the selected service node at time t. , Q t-Ti Indicates the judgment value of whether the i-th business requirement in the selected business node is fully executed, Q t-Ti =0 or Q t-Ti =1, when t-Ti≥di, Q t-Ti =1, when t-Ti<di, Q t-Ti =0, di represents the time required for the i-th business requirement in the selected business node to be fully executed, and the business risk data set Mt corresponding to the selected business node at time t is obtained, Mt={y1_t,y2_t,…,yn_t}, where i=1,2,…,n, represents numbering of each business requirement in the selected business node according to the order of the actual execution time of the business requirements, n represents the total number of business requirements in the selected business node, yn_t represents the business requirement deviation value corresponding to the n-th business requirement in the selected business node at time t, and t represents the real-time time value; according to The business advancement index of the selected business node at time t is calculated, where Gi represents the minimum value in the standard business requirement range corresponding to the i-th business requirement in the selected business node, when yi_t is within the business requirement deviation range, Pyi_t=0, when yi_t is outside the business requirement deviation range, Pyi_t=|yi_t|, U_t represents the business advancement index of the selected business node at time t, yi_t represents the business requirement deviation value corresponding to the i-th business requirement in the selected business node at time t, business requirement deviation range=[0,max(standard business requirement range value)-min(standard business requirement range value)], max represents the maximum value symbol, and min represents the minimum value symbol; S103: If U_t shows a stable change over time in the time series, it means that the selected service node is not the service node to be optimized at time t; if U_t shows a fluctuating change over time in the time series, it means that the selected service node is the service node to be optimized at time t; the stable change means that the difference between the service promotion index of the selected service node at the next time and the service promotion index of the selected service node at the previous time is only greater than or equal to 0; the fluctuating change means that the difference between the service promotion index of the selected service node at the next time and the service promotion index of the selected service node at the previous time is greater than or equal to 0, and less than 0; the time series index values ​​U_t are arranged in the order of their occurrence; S20: Based on the influence coefficients between the service nodes to be optimized, the service nodes to be optimized are traced and collaboratively processed to obtain the risk points of the service nodes to be optimized; The S20 includes: S201: storing the influence coefficients between the service nodes to be optimized in a set N, determining two service nodes to be optimized corresponding to the maximum influence coefficient values ​​stored in the set N, recording that the two service nodes to be optimized are service node A to be optimized and service node B to be optimized, and obtaining initial fluctuation time points TA and TB of service node A to be optimized and service node B to be optimized, where the initial fluctuation time refers to the time when the service advancement index of the service node to be optimized begins to fluctuate over time in the time series; S202: Calculate the impact lag time length value between the service node A to be optimized and the service node B to be optimized according to H_AB=|TA-TB|, input UA_TA into the lag model between the service node A to be optimized and the service node B to be optimized, and the lag model outputs the theoretical impact lag time length value G_AB between the service node A to be optimized and the service node B to be optimized, wherein UA_TA and UB_TB represent the business advancement indexes of the service node A to be optimized and the service node B to be optimized at the time TA and TB respectively, the service node B to be optimized is the associated service node to be optimized of the service node A to be optimized, and the lag model calculates the business advancement index of the service node A to be optimized at each time. The service advancement index and the impact lag time length between the service node to be optimized and the associated service node to be optimized are used as training data to train the linear regression model Y=w*X+b, where w represents the weight of the linear regression model and b represents the bias of the linear regression model. For example, when the service advancement index of the service node C to be optimized fluctuates over time in the time series, the service advancement index of the service node D to be optimized also fluctuates over time in the time series, and the initial fluctuation time of the service node C to be optimized is before the initial fluctuation time of the service node D to be optimized, then the service node D to be optimized is called the associated service node to be optimized of the service node C to be optimized; S203: According to K_AB=|H_AB-G_AB| / S_AB, the traceability coefficient of the business node B to be optimized is calculated compared with the business node A to be optimized. If K_AB>1, it means that the risk buried point of the business node B to be optimized is not the risk cause of the business node A to be optimized at the TA moment. If 0≤K_AB≤1, it means that the risk buried point of the business node B to be optimized is the risk cause of the business node A to be optimized at the TA moment, and the risk buried point of the business node A to be optimized is the risk cause at the TA moment, wherein S_AB represents the impact lag time error value between the business node A to be optimized and the business node B to be optimized, and K_AB represents the traceability coefficient of the business node B to be optimized compared with the business node A to be optimized; S204: Delete the maximum value of the impact coefficient stored in the set N from the set N, repeat the operations of S201-S203, and determine the risk points of each service node to be optimized; S30: Analyze the business process management sequence of each business node to be optimized based on the distribution of various risk points in the time dimension and business dimension; S30 includes: S301: Classify the risk points according to their risk causes. Risk points with the same risk causes belong to the same type of risk points. According to the distribution of the same type of risk points in time and business, predict the business process management order judgment value of each business node to be optimized. The specific prediction formula is: ; Wherein, p=1,2,…,q, represents the number corresponding to each business node to be optimized, q represents the total number of business nodes to be optimized, z=1,2,…,q and z≠p, g_zp=0 or g_zp=1, when g_zp=0, it means that there is no risk point of the same type as the risk point of the business node to be optimized numbered p at time Tp in the business node to be optimized numbered z, when g_zp=1, it means that there is a risk point of the same type as the risk point of the business node to be optimized numbered p at time Tp in the business node to be optimized numbered z, Tp represents the initial fluctuation time point of the business node to be optimized numbered p, U represents the start execution time point of the import and export business, exp() represents the exponential function with e (e=2.73) as the base, Vp represents the judgment value of the business process management order of the business node to be optimized numbered p; S302: taking the service node to be optimized corresponding to maxVp as the first service process management object; S40: managing the service process of each service node to be optimized according to the analysis result of S30; S40 includes: S401: After performing business process management on the first business process management object, the risk buried point corresponding to the first business process management object at the initial fluctuation time point is deleted from the risk buried points existing in each business node to be optimized; S402: Repeat the operations of S301, S302, and S401 until there are no risk points in the business node to be optimized.

[0021] A business process management system based on data collaboration, the system includes a business node determination module to be optimized, a source tracing collaborative processing module, a business process management and analysis module and a process management module; The service node determination module to be optimized is used to determine the service nodes to be optimized according to the changes in the real-time service advancement index of each service node; The module for determining the service nodes to be optimized includes a service information acquisition unit, a service advancement index calculation unit and a determination unit; The business information acquisition unit acquires the business process information and business risk information of each business node; The business advancement index calculation unit calculates the real-time business advancement index of the selected business node according to the real-time business requirement deviation value of each business requirement in the selected business node and the standard business requirement range value corresponding to each business requirement in the selected business node; The determination unit determines whether the selected service node is a service node to be optimized according to the change type of the real-time service advancement index of the selected service node in the time series over time, and the change type includes stable change and fluctuating change; The traceability collaborative processing module is used to perform traceability collaborative processing on each business node to be optimized according to the influence coefficient between each business node to be optimized, and obtain the risk points of each business node to be optimized; The traceability collaborative processing module includes an initial fluctuation time acquisition unit, a theoretical impact lag time prediction unit, and a traceability coefficient calculation unit; The initial fluctuation time acquisition unit selects whether to acquire the initial fluctuation time of each service node to be optimized according to the influence coefficient between each service node to be optimized; The theoretical impact lag time prediction unit predicts the theoretical impact lag time length value between the service node to be optimized A and the service node to be optimized B by inputting the impact lag time length value between the service node to be optimized A and the service node to be optimized B into the lag model between the service node to be optimized A and the service node to be optimized B; The traceability coefficient calculation unit calculates the traceability coefficient of the business node B to be optimized compared with the business node A to be optimized according to the constructed calculation model, and analyzes the risk points of the business node A to be optimized and the business node B to be optimized according to the calculation results; The business process management analysis module is used to analyze the business process management sequence of each business node to be optimized based on the distribution of various risk points in the time dimension and business dimension; The business process management analysis module includes a business process management sequence judgment value prediction unit and a first business process management object determination unit; The business process management sequence judgment value prediction unit predicts the business process management sequence judgment value of each business node to be optimized according to the initial fluctuation time of each business node to be optimized and the existence of the same type of risk buried points between each business node to be optimized; The first business process management object determination unit takes the business node to be optimized corresponding to the maximum value of the business process management order judgment value as the first business process management object; The process management module is used to manage the business process of each business node to be optimized; After the process management module performs business process management on the first business process management object, the risk points corresponding to the first business process management object at the initial fluctuation time point are deleted from the risk points existing in each business node to be optimized. Thereafter, the business process management analysis module and the process management module are executed again until there are no risk points in the business nodes to be optimized.

[0022] Example 1: Assuming that the impact lag time length value H_AB between the service node to be optimized and the service node to be optimized is 3 hours, the lag model outputs the theoretical impact lag time length value G_AB between the service node to be optimized and the service node to be optimized is 4 hours, and the impact lag time error value S_AB between the service node to be optimized and the service node to be optimized is 0.5 hours, then the traceability coefficient of the service node to be optimized B compared to the service node to be optimized A is: K_AB=|H_AB-G_AB| / S_AB=|3-4| / 0.5=2; Then the traceability coefficient of the service node B to be optimized compared with the service node A to be optimized is 2; Since K_AB>1, it means that the risk point of the service node B to be optimized is not the risk cause of the service node A to be optimized at time TA.

[0023] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A business process management method based on data collaboration, characterized in that: The method comprises: S10: Analyze the real-time business advancement index of each business node according to the business process information and business risk information of each business node of the import and export business, and determine the business node to be optimized according to the change of the real-time business advancement index; S20: Based on the influence coefficients between the service nodes to be optimized, the service nodes to be optimized are traced and collaboratively processed to obtain the risk points of the service nodes to be optimized; S30: Analyze the business process management sequence of each business node to be optimized based on the distribution of various risk points in the time dimension and business dimension; S40: managing the service process of each service node to be optimized according to the analysis result of S30.

2. A business process management method based on data collaboration according to claim 1, characterized in that: The S10 includes: S101: Take each sub-business included in the import and export business as a business node, and obtain business process information and business risk information of each business node. The business process information includes the degree to which the business node is actually executed and the time when each business requirement in the business node is actually executed. The business risk information refers to the business requirement deviation value corresponding to the business node when the actual business requirement of the business node deviates from the standard business requirement determined according to the import and export business contract. S102: randomly select a business node, and obtain the time Ti when the i-th business requirement in the selected business node is actually executed, the degree Rt of the actual execution of the selected business node at time t, and the business risk data set Mt corresponding to the selected business node at time t, Mt={y1_t,y2_t,…,yn_t}, where i=1,2,…,n, indicating that each business requirement in the selected business node is numbered in the order of the time when the business requirements are actually executed, n indicates the total number of business requirements in the selected business node, yn_t indicates the business requirement deviation value corresponding to the n-th business requirement in the selected business node at time t, and t indicates the real-time time value; according to The business advancement index of the selected business node at time t is calculated, where Gi represents the minimum value in the standard business requirement range corresponding to the i-th business requirement in the selected business node. When yi_t is within the business requirement deviation range, Pyi_t=0; when yi_t is outside the business requirement deviation range, Pyi_t=|yi_t|, and yi_t represents the business requirement deviation value corresponding to the i-th business requirement in the selected business node at time t. S103: If U_t shows a stable change over time in the time series, it means that the selected service node is not the service node to be optimized at time t; if U_t shows a fluctuating change over time in the time series, it means that the selected service node is the service node to be optimized at time t.

3. A business process management method based on data collaboration according to claim 2, characterized in that: The S20 includes: S201: storing the influence coefficients between the service nodes to be optimized in a set N, determining two service nodes to be optimized corresponding to the maximum influence coefficient values ​​stored in the set N, recording that the two service nodes to be optimized are service node A to be optimized and service node B to be optimized, and obtaining initial fluctuation time points TA and TB of service node A to be optimized and service node B to be optimized; S202: Calculate the impact lag time length value between the service node A to be optimized and the service node B to be optimized according to H_AB=|TA-TB|, input UA_TA into the lag model between the service node A to be optimized and the service node B to be optimized, and the lag model outputs the theoretical impact lag time length value G_AB between the service node A to be optimized and the service node B to be optimized, wherein UA_TA and UB_TB represent the service advancement indexes of the service node A to be optimized and the service node B to be optimized at the time TA and TB respectively; S203: According to K_AB=|H_AB-G_AB| / S_AB, the traceability coefficient of the business node B to be optimized is calculated compared with the business node A to be optimized. If K_AB>1, it means that the risk buried point of the business node B to be optimized is not the risk cause of the business node A to be optimized at the TA time. If 0≤K_AB≤1, it means that the risk buried point of the business node B to be optimized is the risk cause of the business node A to be optimized at the TA time. Among them, S_AB represents the impact lag time error value between the business node A to be optimized and the business node B to be optimized; S204: Delete the maximum value of the impact coefficient stored in the set N from the set N, repeat the operations of S201-S203, and determine the risk points of each business node to be optimized.

4. The business process management method based on data collaboration according to claim 3 is characterized in that: The S30 includes: S301: Classify the risk points according to their risk causes. Risk points with the same risk causes belong to the same type of risk points. According to the distribution of the same type of risk points in time and business, predict the business process management order judgment value of each business node to be optimized. The specific prediction formula is: ； Among them, p=1,2,…,q, represents the number corresponding to each business node to be optimized, q represents the total number of business nodes to be optimized, z=1,2,…,q and z≠p, g_zp=0 or g_zp=1, when g_zp=0, it means that there is no risk buried point of the same type as the risk buried point of the business node to be optimized numbered p at time Tp in the business node to be optimized numbered z, when g_zp=1, it means that there is a risk buried point of the same type as the risk buried point of the business node to be optimized numbered p at time Tp in the business node to be optimized numbered z, Tp represents the initial fluctuation time point of the business node to be optimized numbered p, U represents the start execution time point of the import and export business, exp() represents the exponential function with e as the base, Vp represents the business process management order judgment value of the business node to be optimized numbered p; S302: Take the service node to be optimized corresponding to maxVp as the first service process management object.

5. A business process management method based on data collaboration according to claim 4, characterized in that: The S40 includes: S401: After performing business process management on the first business process management object, the risk buried point corresponding to the first business process management object at the initial fluctuation time point is deleted from the risk buried points existing in each business node to be optimized; S402: Repeat the operations of S301, S302, and S401 until there are no risk points in the business node to be optimized.

6. A business process management system based on data collaboration applied to the business process management method based on data collaboration according to any one of claims 1 to 5, characterized in that: The system includes a business node determination module to be optimized, a source tracing collaborative processing module, a business process management and analysis module, and a process management module; The service node determination module to be optimized is used to determine the service node to be optimized according to the change of the real-time service advancement index of each service node; The traceability collaborative processing module is used to perform traceability collaborative processing on each service node to be optimized according to the influence coefficient between each service node to be optimized, so as to obtain the risk points of each service node to be optimized; The business process management analysis module is used to analyze the business process management sequence of each business node to be optimized according to the distribution of various risk points in the time dimension and business dimension; The process management module is used to manage the service process of each service node to be optimized.

7. The business process management system based on data collaboration according to claim 6 is characterized in that: The service node determination module to be optimized includes a service information acquisition unit, a service advancement index calculation unit and a determination unit; The business information acquisition unit acquires business process information and business risk information of each business node; The service advancement index calculation unit calculates the real-time service advancement index of the selected service node according to the real-time service requirement deviation value of each service requirement in the selected service node and the standard service requirement range value corresponding to each service requirement in the selected service node; The determination unit determines whether the selected service node is a service node to be optimized according to the type of change of the real-time service advancement index of the selected service node over time in the time series.

8. The business process management system based on data collaboration according to claim 7 is characterized in that: The traceability collaborative processing module includes an initial fluctuation time acquisition unit, a theoretical impact lag time prediction unit and a traceability coefficient calculation unit; The initial fluctuation time acquisition unit selects whether to acquire the initial fluctuation time of each service node to be optimized according to the influence coefficient between each service node to be optimized; The theoretical impact lag time prediction unit predicts the theoretical impact lag time length value between the service node to be optimized A and the service node to be optimized B by inputting the impact lag time length value between the service node to be optimized A and the service node to be optimized B into the lag model between the service node to be optimized A and the service node to be optimized B; The traceability coefficient calculation unit calculates the traceability coefficient of the business node B to be optimized compared with the business node A to be optimized according to the constructed calculation model, and analyzes the risk points of the business node A to be optimized and the business node B to be optimized according to the calculation results.

9. The business process management system based on data collaboration according to claim 8, characterized in that: The business process management analysis module includes a business process management sequence judgment value prediction unit and a first business process management object determination unit; The business process management order judgment value prediction unit predicts the business process management order judgment value of each business node to be optimized according to the initial fluctuation time of each business node to be optimized and the existence of the same type of risk buried points between each business node to be optimized; The first business process management object determination unit uses the to-be-optimized business node corresponding to the maximum business process management order judgment value as the first business process management object.

10. The business process management system based on data collaboration according to claim 9, characterized in that: After performing business process management on the first business process management object, the process management module deletes the risk points corresponding to the first business process management object at the initial fluctuation time point from the risk points existing in each business node to be optimized, and then executes the business process management analysis module and the process management module again until there are no risk points in the business nodes to be optimized.

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