A business process management system and method based on data collaboration

Abstract

The present invention discloses a business process management system and method based on data collaboration, which relates to the technical field of business process management. The present invention comprises: S10: determining a business node to be optimized; S20: performing collaborative tracing processing on each business node to be optimized to obtain a risk point of each business node to be optimized; S30: analyzing the business process management sequence of each business node to be optimized; S40: managing the business process of each business node to be optimized. The present invention can quickly analyze the abnormal causes of a business node to be optimized with multiple risk points, further improving the management efficiency of the system for business processes, and can maximize the elimination of risk points existing in the business node to be optimized, and ensure the stable operation of the business process of the business node to be optimized in a short time, further improving the effective management of the import and export business processes by the system.

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 nowadays have opened import and export businesses, which can bring the company's products to the international market. Cross-border supply chain services are a complex and comprehensive system designed 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 consists of several sub-businesses. Since each sub-business is managed by a professional manager, data exchange between the sub-businesses will not be timely, which in turn slows down the progress of import and export business. In addition, when managing the progress of each sub-business, existing managers are unable to quickly analyze the causes of process anomalies in each sub-business, nor can they 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 objectives, the present invention provides the following technical solution: a business process management method based on data collaboration, the method comprising:

[0006] S10: Analyze the real-time business advancement index of each business node based on 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 based on the changes in the real-time business advancement index;

[0007] S20: Based on the influence coefficients between the business nodes to be optimized, trace and coordinate the business nodes to be optimized to obtain the risk points of each business node to be optimized;

[0008] 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;

[0009] S40: managing the service process of each service node to be optimized according to the analysis result of S30.

[0010] Furthermore, the S10 includes:

[0011] S101: Each sub-business included in the import / export business is considered a business node, and business process information and business risk information of each business node are obtained. 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. 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 / export business contract.

[0012] S102: Randomly select a business node and obtain the time Ti at which the i-th business requirement of 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 dataset Mt corresponding to the selected business node at time t, where Mt={y1_t,y2_t,…,yn_t}, where i=1,2,…,n, representing numbering of the business requirements in the selected business node in the order of their actual execution time, n representing the total number of business requirements in the selected business node, yn_t representing the business requirement deviation value corresponding to the n-th business requirement in the selected business node at time t, and t representing the real-time time value;

[0013] 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. Excessive import and export products may cause the importing country to increase tariffs, thereby affecting the business progress of import and export business.

[0014] S103: If U_t shows a stable change over time in the time series, it means that the selected business node is not a business 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 business node is a business node to be optimized at time t.

[0015] Furthermore, the S20 includes:

[0016] S201: The influence coefficients between the service nodes to be optimized are stored in a set N. Two service nodes to be optimized corresponding to the maximum influence coefficients stored in the set N are determined. The two determined service nodes to be optimized are respectively service node A to be optimized and service node B to be optimized. Initial fluctuation time points TA and TB of service node A to be optimized and service node B to be optimized are obtained.

[0017] S202: Calculate the impact lag time between service node A to be optimized and service node B to be optimized based on H_AB=|TA-TB|, input UA_TA into the lag model between service node A to be optimized and service node B to be optimized, and the lag model outputs the theoretical impact lag time G_AB between service node A to be optimized and service node B to be optimized, where UA_TA and UB_TB represent the service advancement indexes of service node A to be optimized and service node B to be optimized at time TA and time TB, respectively.

[0018] 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 time TA. 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 time TA, where 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 is analyzed;

[0019] S204: Delete the maximum value of the impact coefficient stored in set N from set N, repeat the operations of S201-S203, and determine the risk points of each business node to be optimized.

[0020] Furthermore, the S30 includes:

[0021] S301: Risk points are classified according to their risk causes. Risk points with the same risk cause belong to the same category of risk points. Based on the temporal and business distribution of the same category of risk points, the business process management order judgment value of each business node to be optimized is predicted. The specific prediction formula is:

[0022] ;

[0023] 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 the business node to be optimized numbered z does not have 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, when g_zp=1, it means that the business node to be optimized numbered z has 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, 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 base e, and 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 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;

[0024] S302: The service node to be optimized corresponding to maxVp is taken as the first service process management object.

[0025] Furthermore, the S40 includes:

[0026] 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;

[0027] S402: Repeat the operations of S301, S302, and S401 until there are no risk points in the business node to be optimized.

[0028] A business process management system based on data collaboration, the system includes a business node determination module to be optimized, a traceability collaborative processing module, a business process management and analysis module, and a process management module;

[0029] The service node to be optimized determination module 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;

[0030] The traceability collaborative processing module is used to perform traceability collaborative processing on each business node to be optimized based on the influence coefficient between each business node to be optimized, and obtain the risk points of each business node to be optimized;

[0031] 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;

[0032] The process management module is used to manage the service process of each service node to be optimized.

[0033] Furthermore, the service node to be optimized determination module includes a service information acquisition unit, a service advancement index calculation unit and a determination unit;

[0034] The business information acquisition unit acquires business process information and business risk information of each business node;

[0035] 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;

[0036] The determining unit determines whether the selected service node is a service node to be optimized according to a change type of the real-time service advancement index of the selected service node over time in a time series.

[0037] 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;

[0038] 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;

[0039] 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;

[0040] 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 based on 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 based on the calculation results.

[0041] Furthermore, 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;

[0042] The business process management order judgment value prediction unit predicts the business process management order judgment value of each business node to be optimized based on 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;

[0043] The first business process management object determining unit takes the to-be-optimized business node corresponding to the maximum business process management order judgment value as the first business process management object.

[0044] 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 node to be optimized.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The present invention calculates the real-time business deviation value of each business requirement of each business node based on the business process information and business risk information of each business node. Based on the calculation results, the real-time business promotion index of each business node is predicted to realize the judgment of the business node to be optimized.

[0047] 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 analyzing 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 the business process.

[0048] 3. The present invention determines the first business process management object according to the distribution of various risk points in the time dimension and business dimension, and ensures that after the business process management of the first business process management object is performed, the risk points existing in the business node to be optimized can be eliminated to the maximum extent, and the stable progress of the business process of the business node to be optimized can be guaranteed in a short time, further improving the system's effective management of import and export business processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of the workflow of a business process management system and method based on data collaboration of the present invention;

[0050] Figure 2 This is a schematic diagram of the working principle structure of a business process management system and method based on data collaboration of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0052] 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:

[0053] S10: Analyze the real-time business advancement index of each business node based on 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 based on the changes in the real-time business advancement index;

[0054] The S10 includes:

[0055] S101: Each sub-business included in the import and export business is considered as a business node. The import and export business includes the arrival management sub-business, the outbound management sub-business, the shipment management sub-business, and the funds collection and payment sub-business. The business process information and business risk information of each business node are obtained. The business process information includes the degree of actual execution of the business node and the actual execution time of each business requirement in the business node. 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 = actual business requirement value - min (standard business requirement range value), where min (standard business requirement range value) > 0.

[0056] 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 dataset 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;

[0057] 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|. 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. The business requirement deviation range = [0, max(standard business requirement range value) - min(standard business requirement range value)], where max represents the maximum value symbol and min represents the minimum value symbol.

[0058] S103: If U_t shows a stable change over time in the time series, it means that the selected service node is not a 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 a service node to be optimized at time t. Stable change means that the difference between the service promotion index of the selected service node at the next moment and the service promotion index of the selected service node at the previous moment is only greater than or equal to 0. Fluctuating change means that the difference between the service promotion index of the selected service node at the next moment and the service promotion index of the selected service node at the previous moment 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 time.

[0059] S20: Based on the influence coefficients between the business nodes to be optimized, trace and coordinate the business nodes to be optimized to obtain the risk points of each business node to be optimized;

[0060] The S20 includes:

[0061] S201: The influence coefficients between the service nodes to be optimized are stored in a set N. Two service nodes to be optimized corresponding to the maximum influence coefficients stored in the set N are determined. The two determined service nodes to be optimized are recorded as service node A to be optimized and service node B to be optimized. Initial fluctuation time points TA and TB of service node A to be optimized and service node B to be optimized are obtained. 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.

[0062] S202: Calculate the impact lag time length 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 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 time TA and TB respectively, and the service node B to be optimized is the associated service node to be optimized of the service node A to be optimized. The lag model calculates the business advancement index of the service node A to be optimized at each time by The business advancement index and the influence lag time between the business node to be optimized and its associated business nodes to be optimized are used as training data to train a 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 business advancement index of the business node to be optimized C fluctuates over time in the time series, the business advancement index of the business node to be optimized D also fluctuates over time in the time series. If the initial fluctuation time of the business node to be optimized C is before the initial fluctuation time of the business node to be optimized D, then the business node to be optimized D is called the associated business node to be optimized of the business node to be optimized C.

[0063] S203: Calculate the traceability coefficient of the business node B to be optimized compared to the business node A to be optimized according to K_AB=|H_AB-G_AB| / S_AB. 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 time TA. 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 time TA, and the risk buried point of the business node A to be optimized is the risk cause at time TA. 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, and K_AB represents the traceability coefficient of the business node B to be optimized compared to the business node A to be optimized.

[0064] S204: Delete the maximum value of the impact coefficient stored in set N from set N, and repeat the operations of S201-S203 to determine the risk points of each business node to be optimized;

[0065] 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;

[0066] The S30 includes:

[0067] S301: Risk points are classified according to their risk causes. Risk points with the same risk cause belong to the same category of risk points. Based on the temporal and business distribution of the same category of risk points, the business process management order judgment value of each business node to be optimized is predicted. The specific prediction formula is:

[0068] ;

[0069] 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 the business node to be optimized numbered z does not have 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. When g_zp=1, it means that the business node to be optimized numbered z has 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. 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 base e (e=2.73), and Vp represents the judgment value of the business process management order of the business node to be optimized numbered p.

[0070] S302: The service node to be optimized corresponding to maxVp is used as the first service process management object;

[0071] S40: managing the business processes of each business node to be optimized based on the analysis results of S30;

[0072] S40 includes:

[0073] 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;

[0074] S402: Repeat the operations of S301, S302, and S401 until there are no risk points in the business node to be optimized.

[0075] A business process management system based on data collaboration, the system includes a business node determination module to be optimized, a traceability collaborative processing module, a business process management and analysis module, and a process management module;

[0076] The service node determination module to be optimized is used to determine the service nodes to be optimized based on the changes in the real-time service advancement index of each service node;

[0077] The module for determining the business nodes to be optimized includes a business information acquisition unit, a business advancement index calculation unit, and a determination unit;

[0078] The business information acquisition unit acquires the business process information and business risk information of each business node;

[0079] 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;

[0080] The determination unit determines whether the selected business node is a business node to be optimized based on the change type of the real-time business advancement index of the selected business node over time in the time series, and the change type includes stable change and fluctuating change;

[0081] The traceability collaborative processing module is used to perform traceability collaborative processing on each business node to be optimized based on the influence coefficient between the business nodes to be optimized, and obtain the risk points of each business node to be optimized;

[0082] 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;

[0083] 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;

[0084] The theoretical impact lag time prediction unit predicts the theoretical impact lag time length between the service node to be optimized A and the service node to be optimized B by inputting the impact lag time length 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;

[0085] The traceability coefficient calculation unit calculates the traceability coefficient of the business node B to be optimized compared to the business node A to be optimized based on the constructed calculation model. Based on the calculation results, the unit analyzes the risk points of the business nodes A and B to be optimized.

[0086] 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;

[0087] 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;

[0088] The business process management sequence judgment value prediction unit predicts the business process management sequence judgment value of each business node to be optimized based on the initial fluctuation time of each business node to be optimized and the existence of the same type of risk buried points between the business nodes to be optimized;

[0089] The first business process management object determination unit takes the business node to be optimized corresponding to the maximum business process management order judgment value as the first business process management object;

[0090] The process management module is used to manage the business processes of each business node to be optimized;

[0091] After the process management module performs business process management on the first business process management object, it 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. Then, the business process management analysis module and the process management module are executed again until there are no risk points in the business node to be optimized.

[0092] Example 1: Assume that the impact lag time length between the service node to be optimized A and the service node to be optimized B is H_AB = 3 hours, the lag model outputs a theoretical impact lag time length G_AB = 4 hours between the service node to be optimized A and the service node to be optimized B, and the impact lag time error S_AB = 0.5 hours between the service node to be optimized A and the service node to be optimized B. Then, the traceability coefficient of the service node to be optimized B compared to the service node to be optimized A is:

[0093] K_AB=|H_AB-G_AB| / S_AB=|3-4| / 0.5=2;

[0094] The traceability coefficient of the service node B to be optimized compared to the service node A to be optimized is 2;

[0095] 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.

[0096] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A business process management method based on data collaboration, characterized by: The method comprises: S10: Analyze the real-time business advancement index of each business node based on 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 based on the changes in the real-time business advancement index; The S10 includes: S101: Each sub-business included in the import / export business is considered a business node, and business process information and business risk information of each business node are obtained. 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. 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 / export business contract. S102: Randomly select a business node and obtain the time Ti at which the i-th business requirement of 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 dataset Mt corresponding to the selected business node at time t, where Mt={y1_t,y2_t,…,yn_t}, where i=1,2,…,n, representing numbering of the business requirements in the selected business node in the order of their actual execution time, n representing the total number of business requirements in the selected business node, yn_t representing the business requirement deviation value corresponding to the n-th business requirement in the selected business node at time t, and t representing 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|, where 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 a 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 a service node to be optimized at time t; S20: Based on the influence coefficients between the business nodes to be optimized, trace and coordinate the business nodes to be optimized to obtain the risk points of each business node to be optimized; The S20 includes: S201: The influence coefficients between the service nodes to be optimized are stored in a set N. Two service nodes to be optimized corresponding to the maximum influence coefficients stored in the set N are determined. The two determined service nodes to be optimized are respectively service node A to be optimized and service node B to be optimized. Initial fluctuation time points TA and TB of service node A to be optimized and service node B to be optimized are obtained. S202: Calculate the impact lag time between service node A to be optimized and service node B to be optimized based on H_AB=|TA-TB|, input UA_TA into the lag model between service node A to be optimized and service node B to be optimized, and the lag model outputs the theoretical impact lag time G_AB between service node A to be optimized and service node B to be optimized, where UA_TA and UB_TB represent the service advancement indexes of service node A to be optimized and service node B to be optimized at time TA and time TB, respectively. S203: Calculate the traceability coefficient of the service node B to be optimized compared to the service node A to be optimized according to K_AB=|H_AB-G_AB| / S_AB. If K_AB>1, it means that the risk buried 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. If 0≤K_AB≤1, it means that the risk buried point of the service node B to be optimized is the risk cause of the service node A to be optimized at time TA. S_AB represents the impact lag time error between the service nodes A to be optimized and the service nodes B to be optimized. S204: Delete the maximum value of the impact coefficient stored in set N from set N, and repeat the operations of S201-S203 to determine the risk points of each business 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; S40: managing the service process of each service node to be optimized according to the analysis result of S30.

2. The data collaboration-based business process management method according to claim 1, characterized in that: The S30 includes: S301: Risk points are classified according to their risk causes. Risk points with the same risk cause belong to the same category of risk points. Based on the temporal and business distribution of the same category of risk points, the business process management order judgment value of each business node to be optimized is predicted. 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 the business node to be optimized numbered z does not have 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, when g_zp=1, it means that the business node to be optimized numbered z has 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, 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 base e, and Vp represents the business process management order judgment value of the business node to be optimized numbered p; S302: The service node to be optimized corresponding to maxVp is taken as the first service process management object.

3. The data collaboration-based business process management method according to claim 2, 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.

4. A data-collaborative business process management system applied to the data-collaborative business process management method according to any one of claims 1 to 3, characterized in that: The system includes a business node determination module to be optimized, a traceability collaborative processing module, a business process management and analysis module, and a process management module; The service node to be optimized determination module 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 business node to be optimized based on the influence coefficient between each business node to be optimized, and obtain the risk points of each business 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 based on 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.

5. The business process management system based on data collaboration according to claim 4, 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 determining unit determines whether the selected service node is a service node to be optimized according to a change type of the real-time service advancement index of the selected service node over time in a time series.

6. The business process management system based on data collaboration according to claim 5, 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 based on 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 based on the calculation results.

7. The data collaboration-based business process management system according to claim 6, 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 based on 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 determining unit takes the to-be-optimized business node corresponding to the maximum business process management order judgment value as the first business process management object.

8. The business process management system based on data collaboration according to claim 7, 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 node to be optimized.

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