A multi-business interconnection system for data processing

Abstract

This invention discloses an enterprise multi-business interconnection system for data processing, relating to the field of enterprise business process optimization technology. It includes a value stream definition module, a process analysis module, a requirement backpropagation module, a business interconnection module, a value stream processing module, and a value verification module. Based on enterprise business objectives, this invention determines the end-to-end business value stream and decomposes processes to identify business nodes, states, and collaborative relationships. Then, based on optimization objectives, it deduces the minimum necessary data interconnection scope. Subsequently, it implements data interaction between heterogeneous businesses according to this scope and integrates and processes multi-source data to construct a business interaction view, generating operational performance indicators. Finally, the value verification module quantifies and displays the optimization effect. This invention achieves value-driven interconnection and optimization, effectively improving the targeting and measurability of cross-system business operations.

Description

Technical Field

[0001] This invention relates to the field of enterprise business process optimization technology, specifically to an enterprise multi-business interconnection system for data processing. Background Technology

[0002] Large enterprises or group organizations typically operate multiple heterogeneous business systems, such as ERP, CRM, SCM, and MES, each supporting different business functions.

[0003] Traditional system integration methods, such as point-to-point interfaces, Enterprise Service Bus (ESB), or data platforms, often focus on technical aspects like data connectivity and format conversion. They lack top-level design and closed-loop optimization from a business value creation perspective, leading to broad integration scope, redundant data interaction, unclear responsibilities, and difficulty in directly measuring the actual contribution of integration investment to core business objectives (such as delivery efficiency and cost control). Existing technologies often face challenges in achieving cross-system business process integration, including high integration complexity, slow response to business changes, and an inability to accurately focus on data interconnection needs that critically impact business objectives. Summary of the Invention

[0004] The purpose of this invention is to address the problems of broad integration scope, insufficient value orientation, and difficulty in quantifying optimization effects in existing enterprise multi-business interconnection solutions, and to propose an enterprise multi-business interconnection system for data processing.

[0005] The objective of this invention can be achieved through the following technical solution: an enterprise multi-business interconnection system for data processing, comprising: a value stream definition module, a process parsing module, a requirement backpropagation module, a business interconnection module, a value stream processing module, and a value verification module;

[0006] The value stream definition module determines the end-to-end business value stream based on the company's business objectives;

[0007] The process analysis module decomposes the business value stream, identifying the business nodes, business statuses, and collaborative relationships across business nodes involved in the business value stream.

[0008] The demand-driven module uses the optimization goals of the business value stream as a basis to obtain the minimum necessary scope of data interconnection;

[0009] The business interconnection module enables data interaction between multiple heterogeneous businesses, based on the minimum necessary data interconnection scope.

[0010] The value stream processing module integrates and processes data from multiple business processes, constructs a business interaction view corresponding to the business value stream, and generates operational performance metrics.

[0011] The value verification module analyzes the optimization effect of the business value stream based on the operational performance indicators, makes a judgment based on the optimization effect, and displays the judgment result.

[0012] As a preferred embodiment of the present invention, the specific process of determining the end-to-end business value stream based on enterprise business objectives is as follows:

[0013] Acquire enterprise business objective data, including objective type identifiers and objective parameter information; convert enterprise business objective data into value-oriented parameters based on preset objective mapping rules; match an end-to-end business process template in a pre-set business process template library according to the value-oriented parameters; generate an end-to-end business value stream corresponding to the enterprise business objective based on the business process template and assign it a unique value stream identifier; store the business value stream in a structured data format in the value stream configuration table.

[0014] As a preferred embodiment of the present invention, the process of decomposing the business value stream is as follows:

[0015] Receive the business value stream input structure VF; perform standardized processing on each original business node to generate a process node model, forming a process node model set; construct a process sequence matrix, a business state set, a state trigger relationship matrix, and a data dependency relationship matrix based on the process node model set; comprehensively calculate the cross-node collaboration relationship matrix and output the process parsing result.

[0016] As a preferred embodiment of the present invention, the process of obtaining the minimum necessary data interconnection range is as follows:

[0017] Based on the optimization objective parameter set, a set of process nodes is selected from the set of process node models; the business identifiers of the nodes in the selected set of process nodes are extracted to construct a candidate business set; the candidate business set is extended based on the collaboration relationship matrix to obtain an extended set; the data field set of all nodes in the extended set is extracted to construct a candidate data object set; based on the optimization objective constraints, the minimum subset of data objects is solved from the candidate data object set; a responsibility mapping model is established to detect responsibility conflicts and generate responsibility reconstruction identifier data; finally, the minimum necessary data interconnection range is generated.

[0018] As a preferred embodiment of the present invention, the specific process for data interaction among multiple heterogeneous services is as follows:

[0019] Input the minimum interconnection constraint range; obtain the data interaction request Req; perform business system validity determination and data object validity determination; read data V from the source business program. src ; Perform data structure adaptation V std If the interaction involves restructuring of rights and responsibilities, add constraint markers; V stdWrite to the target business program and generate an interaction log.

[0020] As a preferred embodiment of the present invention, the process of constructing a business interaction view corresponding to the business value stream is as follows:

[0021] Using the business value stream structure VF as input, construct a directed graph G. VF The process involves calculating the node topology sequence Seq; identifying the business sets associated with each node; collecting node operation data and performing time standardization and structure alignment to obtain the node-aligned operation dataset; establishing business object-level associations for adjacent node data based on the node relationship set; constructing a business interaction view; and calculating node operation status and value stream operation performance indicators.

[0022] As a preferred embodiment of the present invention, the optimization effect of the business value stream is analyzed based on operational performance indicators, specifically as follows:

[0023] The difference between the current performance indicator and the baseline indicator is calculated to obtain the change in performance; the change in performance is normalized and standardized to obtain a standardized difference vector; the change in performance is weighted and summed based on a preset weight vector to obtain the optimization effect value.

[0024] In a preferred embodiment of the present invention, the determination result is displayed, and the process is as follows:

[0025] When the optimization effect value is greater than 0, it means that the overall operation effect of the business value stream has improved compared with the baseline state; when the optimization effect value is equal to 0, it means that the operation effect of the business value stream has not changed; when the optimization effect value is less than 0, it means that the operation effect of the business value stream has decreased compared with the baseline state; and the results of the above comparisons will be displayed through the corresponding display device.

[0026] Compared with the prior art, the beneficial effects of the present invention are:

[0027] 1. This invention, through value stream definition, process analysis, and demand-driven analysis, decomposes and maps high-level business objectives layer by layer into specific and minimum necessary data interconnection scopes. It overcomes the drawbacks of traditional integration that is merely connecting for the sake of connecting, and ensures that every data interaction directly serves the measurable business value optimization goal, thereby improving the accuracy of interconnection and return on investment.

[0028] 2. This invention enables data interaction and view construction for heterogeneous businesses. Through the value verification module, it quantifies and analyzes the operational effects and determines the optimization results, forming a complete closed loop of definition, parsing, interconnection, processing, and verification. This allows the system to dynamically analyze and guide whether to expand or adjust the interconnection strategy based on actual operational effect data, and has the ability to adapt to business changes and continuously optimize. Attached Figure Description

[0029] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0030] Figure 1 This is a schematic diagram of the principle of the present invention. Detailed Implementation

[0031] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0032] It should be understood that the terms “comprising” and “including” used in this disclosure and claims indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0033] It should also be understood that the terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the disclosure. As used in this disclosure and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0034] Please see Figure 1 As shown, an enterprise multi-business interconnection system for data processing includes: a value stream definition module, a process parsing module, a requirement backpropagation module, a business interconnection module, a value stream processing module, and a value verification module.

[0035] Among them, the value stream definition module determines at least one end-to-end business value stream based on the company's business objectives;

[0036] The process analysis module decomposes the business value stream, identifying the business nodes, business statuses, and collaborative relationships across business nodes involved in the business value stream.

[0037] The demand-backward derivation module uses the optimization goals of the business value stream as a basis to reverse-engineer the business systems that need to be connected, the data objects that need to be aligned, and the business responsibilities that need to be restructured in the business value stream, thereby obtaining the minimum necessary scope of data interconnection.

[0038] The business interconnection module performs data interaction with multiple heterogeneous businesses according to the minimum necessary data interconnection scope. The data interaction includes at least one of business status data, business event data, or master data.

[0039] The value stream processing module integrates and processes data from multiple business processes, constructs a business interaction view corresponding to the business value stream, and generates operational performance metrics.

[0040] The value verification module analyzes the optimization effect of the business value stream based on operational performance indicators, and determines whether to expand business interconnection based on the optimization effect.

[0041] The enterprise's business objectives define an end-to-end business value stream, specifically:

[0042] Enterprise business target data is obtained through external interfaces. The enterprise business target data includes target type identifiers and target parameter information. The target type includes at least one of the following: revenue growth target, cash flow improvement target, cost control target, or delivery efficiency improvement target.

[0043] Based on preset target mapping rules, enterprise business target data is converted into value-oriented parameters. These value-oriented parameters describe the types of business outcomes directly related to the business targets. These value-oriented parameters include, but are not limited to, value output type parameters, value realization cycle parameters, and value realization node parameters.

[0044] In the pre-built business process template library, an end-to-end business process template is matched based on the value-oriented parameters. The business process template describes the complete business process across multiple business stages.

[0045] The business process template includes multiple sequentially arranged process nodes, each corresponding to at least one business state.

[0046] Based on the business process template, an end-to-end business value stream corresponding to the enterprise's business objectives is generated, and a unique value stream identifier is assigned to each business value stream.

[0047] The end-to-end business value stream describes the complete business chain from the business initiation event to the final value realization event.

[0048] The end-to-end business value stream is stored in a value stream configuration table in the form of structured data. The configuration table includes, but is not limited to: value stream identifier, process node identifier and their sequential relationship, and the business status definition corresponding to the process node.

[0049] The specific process of decomposing the business value stream is as follows:

[0050] The received business value stream input data is defined as a structure VF, as follows:

[0051] VF=⟨ID,N ode R ela > Where: ID is a unique identifier for the business value stream; N ode R is a list of the original business nodes contained in the business value stream. ela This is the raw relational data that describes the sequential relationships between business nodes.

[0052] Each original process node n i ∈N ode , defined as: n i =⟨id i sys i order i StateDef i DataDef i >;where: id i For identifying business nodes; sys i This is the service identifier to which the service node belongs; order i This is the sequential number of the business node in the business value stream; StateDef i Define a set of business states for this business node; DataDef i Define a set of data fields generated by this business node.

[0053] For each original business node n i Perform standardized processing to generate a process node model pn i The normalization processing function is defined as: pn i =Normalize(n i ), where: pn i =⟨id i sys i seq i StateSet i DataSet i >;seq i Indicates the execution order of the process; StateSet i A standardized set of business states; DataSet i This is a standardized set of data fields.

[0054] All process node models constitute the process node model set: PN={pn1, pn2, ..., pn k}, where k is the maximum index number of the process node model.

[0055] Based on the process node model set PN, construct the process sequence matrix O. ij : ; where: O ij =1 indicates process node pn i In terms of execution order, it precedes process node pn. j .

[0056] For any process node model pn i Its business state set is defined as: StateSet i ={s i1 s i2 , ..., s im}, where each business state s ij Defined as: s ij =⟨state_code ij condition ij > where: state_code ij For status indicators; condition ij The state determination condition is a logical expression.

[0057] At time t, the process node pn is determined by the state determination function. i Current state: CurrentState(pn) i ,t)=s ij condition ij (t)=true; if multiple conditions are met simultaneously, then a unique state is selected according to the preset priority rule.

[0058] For any two process node models pn i and pn j In satisfying O ij Given that =1, calculate the state triggering relation matrix T. ij : The state trigger function is defined as follows: , where t ik For state s ik Effective date; pn, a process node model j The state in The effective time; Δt is the preset time threshold.

[0059] Next, the data dependency matrix D between the nodes in the computation process. ij :

[0060] When D ij When =1, it indicates process node pn j State calculation depends on process node pn iThe data provided; where Φ indicates: determining whether two nodes share data.

[0061] Comprehensive process sequence matrix O ij State trigger matrix T ij and the data dependency matrix D ij Calculate the cross-node collaboration matrix C ij : When C ij When =1, then process node pn is determined. i With process node pn j There are collaborative relationships between cross-business nodes.

[0062] The following data will be output as the process parsing result: ParseResult = ⟨PN, StateSet, C ij >; where StateSet represents the mapping relationship between nodes and business states.

[0063] The demand reverse engineering module uses the optimization goals of the business value stream as the basis for its analysis process as follows:

[0064] The process parsing result is used as input data, and the input is represented as: ParseResult = ⟨PN, StateSet, C ij >; It also receives the optimization target parameter set G of the business value stream; and defines the optimization target of the business value stream as the target parameter vector: G = ⟨gt, gm, gth>, where: gt is the target type identifier; gm is the indicator identifier to be optimized; and gth is the target constraint threshold.

[0065] Based on the optimization objective parameter G, the process node set KPN is selected from the process node model set PN: The influence determination function is defined as follows: .

[0066] For the process node set KPN, extract the identifier of its respective business system to generate a candidate business set SYS, i.e.: SYS = {sys i |pn i ∈KPN}.

[0067] Based on the cross-node collaboration relationship matrix Cij, the candidate business set is expanded by dependency to generate an expanded set SYS′, i.e.: SYS′=SYS∪{sys j ∣∃pn i ∈KPN, C ij =1}.

[0068] For each process node pn in the extended set SYS′ i Extract its data field collection DataSeti Construct a set of candidate data objects : .

[0069] Based on the optimization objective constraint gth, the minimum subset DO of candidate data objects is found from the set DO. min , so that: Metric(DO min )≤gth; where Metric(·) is a function that calculates business metrics based on the data object.

[0070] Next, model pn for each process node. i Establish a responsibility mapping model: Resp(pn) i )=⟨role i sysi, state_range i >, where: role i This is the role identifier responsible for handling the state of this node; state_range i The set of states for which this role is responsible.

[0071] Based on the synergy matrix C ij The detection of conflicts of authority and responsibility, namely: When Conflict(pn) i pn j When )=1, the responsibility restructuring identifier data RespAdjust is generated.

[0072] Based on the extended set SYS′ and the minimum data object set DO min And reconstruct the identification data of rights and responsibilities, and generate the minimum necessary data interconnection scope, namely: IR min =⟨SYS′,DO min ,RespAdjust> where: RespAdjust represents the set of rights and responsibilities that need to be restructured.

[0073] The specific process for data interaction between multiple heterogeneous services is as follows:

[0074] The input is the result of the minimum interconnection constraint: IR min =⟨SYS′,DO min ,RespAdjust>; Get data interaction request Req=⟨s src , s tgt , d o >Where: s src For the source business program; s tgt For the target business procedure; d o The data object to be interacted with; s src Belongs to SYS; s tgtBelongs to SYS, d o Belongs to DO;

[0075] Using the legality determination function F sys (·) is used to make a judgment, that is: ;

[0076] Then, based on the object validity determination function F data (·) is used to make a judgment, that is: ;

[0077] The specific process of data interaction between multiple heterogeneous services:

[0078] Based on data interaction request R eq Perform legality checks on both the source and target business procedures: F sys (s src )∧F sys (s tgt When )=1, the formula holds true;

[0079] After the range validation passes, perform a data object validity check on the data object: F data (d o If the result is 1, subsequent data interaction operations are allowed; otherwise, the process is terminated.

[0080] From the source business program s src Retrieving data object d from o The current data value is denoted as: V src =R ead (s src d o ), where R ead (·) indicates a data read operation, V std Standardized data values ​​that are identifiable to the target;

[0081] For data structure differences between heterogeneous business processes, perform adaptation processing on data values: V std =Adapt(V src ), where: Adapt(·) is the data structure adaptation function;

[0082] Before writing data, a data judgment interaction is performed to determine whether the set of responsibility adjustment identifiers is matched, i.e.: (s src s tgt )∈RespAdjust, when the above conditions are met, a constraint identifier is attached to the data interaction to restrict the triggering of subsequent processing flows.

[0083] Standardized data value V std Write to the target business program: Write(s src stgt This completes the data interaction between the source and target business programs and generates a data interaction record corresponding to the data interaction request: Log=⟨s src , s tgt , d o , status>; where status represents the result of the data interaction execution;

[0084] The data from multiple business processes is integrated and processed in the following way:

[0085] Using the business value stream structure VF as input; for the input node set N ode Relationship set R with nodes ela Perform analysis and construct a directed graph G of value stream nodes. VF :G VF =(N ode R ela );

[0086] Based on a directed graph, the topological order of nodes is calculated using the formula Seq=TopoSort(G VF The processing order of nodes is obtained by Seq.

[0087] For each business node n in the value stream j1 ∈N ode Identify the corresponding set of business systems:

[0088] SYS(n j1 )={s|s participating nodes n j1 The business processing involves identification based on a pre-configured mapping between nodes and the business system, without altering the business value stream structure (VF) itself.

[0089] Using node nj1 as the granularity, from its associated service set SYS(n j1 In this context, the collection of data objects generated by the running acquisition node includes:

[0090] DO(n j1 )={d o ∣d o At node n j1 During execution, the data object collection is a derived result and is not stored as a VF field in the business value stream structure.

[0091] For each node nj1, retrieve runtime data from the corresponding business program: The collected data is then processed using time standardization to form a node-level runtime dataset Dstd(n). j1 ): Dstd(n j1=TimeAlign(D(n j1 ));

[0092] Based on the formed node-level running dataset Dstd(n) j1 For differences in data structure output across different program segments, perform structure adaptation and semantic alignment on node-level data: Dnorm(n j1 =Normalize(Dstd(n j1 )); Dnorm(n j1 Run the dataset to align the nodes;

[0093] Based on the node relationship set R ela To perform business object-level association on the data of adjacent nodes, specifically:

[0094] Get R ela Any pair of nodes (n) i1 n j1 ), indicating that the business object is allowed to access node n i1 Flow to node n j1 .

[0095] Obtain node n respectively i1 With node n j1 The business processing record dataset on the data is denoted as D. i1 With D j1 The business processing record includes at least a unique identifier for the business object and a corresponding processing time or processing order identifier.

[0096] When node pair (n) is detected i1 n j1 ) belongs to the node relation set R ela When, then determine node n i1 With node n j1 It is an adjacent node in the business value stream and triggers data association processing for that adjacent node pair.

[0097] For a given pair of adjacent nodes (n) i1 n j1 ), at node (n i1 n j1 Dataset D i1 With node n j1 Dataset D j1 In this process, matching is performed based on the unique identifier of the business object, specifically including:

[0098] For node n i1 Any business processing record r on ik The record rik includes at least a first processing identifier T. i1The first processing identifier is used to characterize the business object at node n. i1 The time point or position in the processing sequence where the business process was completed;

[0099] For node n j1 Above and record r ik Business processing records r with the same unique identifier for the same business object j1 Record r j1 At least includes the second processing identifier T j1 The second processing identifier is used to characterize the business object at node n. j1 The time point or position in the processing sequence where the business process was completed.

[0100] By comparing the first processing identifier T i1 With the second processing identifier T j1 Determine if the business object is at node n i1 With node n j1 The processing order between them specifically includes: when the first processing identifier T i1 Earlier than the second processing identifier T j1 When that happens, it is determined that the business object is at node n. i1 The processing occurs at node n j1 Previously, the time sequence constraints of node processing in the business process were satisfied; when the first processing identifier T... i1 With the second processing identifier T j1 When they are equal, it is determined that the processing of the business object on the two nodes belongs to the same processing stage, and is also considered to meet the processing order requirements between nodes; when the first processing identifier T i1 Later than the second processing identifier T j1 When determining node n i1 With node n j1 The processing records do not meet the sequential constraints of the business process, and the business object-level association is not established. Only when the first processing identifier T... i1 No later than the second processing identifier T j1 Only then will the record r be saved. ik With record r j1 Node n is established based on the consecutive processing records of the same business object on adjacent nodes. i1 With node n j1 The relationship between Link(n) i1 n j1 ).

[0101] Based on the node-aligned running dataset and its relationships, a business interaction view View(VF.ID) corresponding to the value stream VF.ID is constructed, that is: View(VF.ID) = ⟨NodeView, RelaView>, where: NodeView is the running data of each node; RelaView is the representation of the data interaction and flow relationship between nodes.

[0102] The NodeView consists of multiple node view units, each node view unit corresponding one-to-one with a node in the business value stream; for any node n i1 Its corresponding node view unit NodeView(n i1 Includes: based on node n i1 The number of business processes, the distribution of processing status, or the indicators of processing results obtained from the statistical analysis of the running dataset; the set of business objects entering and leaving the node, determined by the business object-level association; node n i1 The position identifier in the processing sequence of the business value stream.

[0103] RelaView is based on the node relationship set R ela and business object-level associations LinkLink(n) i1 n j1 Construct: For any node relationship pair (n) i1 n j1 When a corresponding business object-level association exists, Link(n) i1 n j1 When creating a node n in ReleaseView, ... i1 With node n j1 The interaction relationships between them are represented, specifically including: from node n i1 Flow to node n j1 The number of business objects, the number of business objects at node n i1 With node n j1 Information on processing time and direction of business flow between nodes.

[0104] For each node n j1 ∈N ode The node running state (State(n)) is calculated based on its node-aligned running dataset. j1 ): State(n j1 )=f(D norm (n j1 ); where State(n) j1 This includes runtime information such as processing time, number of processes, and abnormal states; the form of the f(·) function is: State(n j1 )=⟨T avg (nj1 ), Q(n) j1 ), E(n j1 )>;

[0105] The T avg (n j1 The average processing time per node is denoted as . ,

[0106] N j1 For the statistical period, at node n j1 The number of business objects that have been processed. These represent the k-th business object at node n. j1 The start time and end time of the processing.

[0107] The Q(n) j1 ) represents the number of nodes to process: Q(n) j1) =N j1 .

[0108] The E(n) j1 ) represents an indicator of abnormal node status: ,in: For node n j1 The number of business objects marked as being in an abnormal handling state;

[0109] Based on the node topology order and node operating status, the entire value stream is aggregated and calculated to generate a business value stream performance metric, Metric(VF.ID), which is: Metric(VF.ID) = Aggregate(State(n 11 ), State(n 21 ), ...,State(n) m1 )); where Aggregate(·) is a preset aggregation function.

[0110] The analysis of the optimization effect of the business value stream based on operational performance indicators is as follows:

[0111] For the same metric item, calculate the difference between the current performance metric and the baseline metric: ΔMetric = Metric cur -Metric base Where ΔMetric represents the change in the operational performance of the business value stream before and after optimization; Metric cur Metrics are the current performance indicators. base This serves as the baseline indicator.

[0112] Based on the change in operational effectiveness ΔMetric before and after optimization of the business value stream, the optimization effect is quantified to obtain the optimization effect value Effect(VF.ID): Specifically:

[0113] In this embodiment, the change in the operational performance of the business value stream before and after optimization, ΔMetric, is defined as: ΔMetric = ⟨ΔT, ΔQ, ΔE, where: ΔT is the change in the overall processing time of the value stream, ΔQ is the change in the overall processing capacity of the value stream, and ΔE is the change in the overall anomaly level of the value stream.

[0114] The changes in the operational effectiveness of the business value stream before and after optimization are normalized in direction to obtain the normalized difference vector ΔMetric: ΔMetric′=⟨−ΔT, ΔQ, −ΔE>;

[0115] The normalized difference vector is standardized to obtain the standardized difference vector. : T base The baseline for value processing time; T base This serves as the baseline for overall value stream processing capabilities; T base This serves as the baseline for the overall anomaly level of the value stream.

[0116] Based on the preset weight vector W = ⟨wT, wQ, wE>, the standardized difference vector is weighted and summed to obtain the numerical value of the optimization effect. : , where w T w Q w E The preset weighting coefficients are used; and w T +w Q +w E =1.

[0117] When the optimization effect value Effect(VF.ID) > 0, it indicates that the overall operation effect of the business value stream has improved compared to the baseline state; when the optimization effect value Effect(VF.ID) = 0, it indicates that the operation effect of the business value stream has not changed significantly; when the optimization effect value Effect(VF.ID) < 0, it indicates that the operation effect of the business value stream has decreased compared to the baseline state; and the results of the above comparisons are displayed through the corresponding display device.

[0118] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A multi-business interconnection system for data processing, comprising: The module comprises a value stream definition module, a process analysis module, a requirement reverse engineering module, a business interconnection module, a value stream processing module, and a value verification module; its features are: The value stream definition module determines the end-to-end business value stream based on the company's business objectives; The process analysis module decomposes the business value stream, identifying the business nodes, business statuses, and collaborative relationships across business nodes involved in the business value stream. The demand-driven module uses the optimization goals of the business value stream as a basis to obtain the minimum necessary scope of data interconnection; The business interconnection module enables data interaction between multiple heterogeneous businesses, based on the minimum necessary data interconnection scope. The value stream processing module integrates and processes data from multiple business processes, constructs a business interaction view corresponding to the business value stream, and generates operational performance metrics. The value verification module analyzes the optimization effect of the business value stream based on the operational performance indicators, judges the optimization effect, and displays the judgment results.

2. The enterprise multi-service interconnection system for data processing according to claim 1, characterized in that, The specific process of determining the end-to-end business value stream based on corporate business objectives is as follows: Acquire enterprise business objective data, including objective type identifiers and objective parameter information; convert enterprise business objective data into value-oriented parameters based on preset objective mapping rules; match an end-to-end business process template in a pre-set business process template library according to the value-oriented parameters; generate an end-to-end business value stream corresponding to the enterprise business objective based on the business process template and assign it a unique value stream identifier; store the business value stream in a structured data format in the value stream configuration table.

3. The enterprise multi-service interconnection system for data processing according to claim 2, characterized in that, The process of decomposing the business value stream is as follows: Receive the business value stream input structure VF; perform standardization processing on each original business node to generate a process node model and form a process node model set. Construct a process sequence matrix, a business state set, a state trigger relationship matrix, and a data dependency relationship matrix based on the process node model set; The cross-node collaboration relationship matrix is ​​calculated and the process analysis results are output.

4. The enterprise multi-service interconnection system for data processing according to claim 3, characterized in that, The process of obtaining the minimum necessary data interconnect range is as follows: Based on the optimization target parameter set, a set of process nodes is selected from the set of process node models; the business identifiers of the nodes in the selected set of process nodes are extracted to construct a set of candidate businesses; Based on the collaboration relationship matrix, the candidate business set is expanded to obtain an extended set; the data field set of all nodes in the extended set is extracted to construct a candidate data object set; Based on the optimization objective constraints, the minimum subset of data objects is solved from the candidate data object set; a responsibility mapping model is established to detect responsibility conflicts and generate responsibility reconstruction identification data; finally, the minimum necessary data interconnection range is generated.

5. The enterprise multi-service interconnection system for data processing according to claim 4, characterized in that, The specific process for data interaction between multiple heterogeneous services is as follows: Input the minimum interconnection constraint range; obtain the data interaction request Req; perform business system validity determination and data object validity determination; read data V from the source business program. src ; Perform data structure adaptation V std If the interaction involves restructuring of authority and responsibility, add constraint markers; adapt the data structure to V. std Write to the target business program and generate an interaction log.

6. The enterprise multi-service interconnection system for data processing according to claim 5, characterized in that, The process of constructing a business interaction view corresponding to the business value stream is as follows: Using the business value stream structure VF as input, construct a directed graph G. VF The node topology order Seq is calculated; the associated business sets of each node are identified; node operation data is collected and time standardization and structure alignment are performed to obtain the node aligned operation dataset; business object-level association is performed on the data of adjacent nodes based on the node relationship set to establish the association relationship; Build a business interaction view and calculate node running status and value stream performance metrics.

7. The enterprise multi-service interconnection system for data processing according to claim 6, characterized in that, The optimization effect of the business value stream is analyzed based on operational performance indicators, specifically as follows: The difference between the current performance indicator and the baseline indicator is calculated to obtain the change in performance; the change in performance is normalized and standardized to obtain a standardized difference vector; the change in performance is weighted and summed based on a preset weight vector to obtain the optimization effect value.

8. The enterprise multi-service interconnection system for data processing according to claim 7, characterized in that, The result of the judgment will be displayed. The process is as follows: When the optimization effect value is greater than 0, it indicates that the business value stream operation effect has improved; when the optimization effect value is equal to 0, it indicates that the business value stream operation effect has not changed; when the optimization effect value is less than 0, it indicates that the business value stream operation effect has decreased.

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