Bill collaborative management method and system based on multi-source heterogeneous data fusion

Abstract

The embodiment of the invention provides a bill collaborative management method and system based on multi-source heterogeneous data fusion, and belongs to the technical field of data processing. The method comprises the following steps: performing hash calculation according to standardized bill data to obtain a unique identifier; and obtaining a map node corresponding to the standardized bill data to obtain a target map node. The path from the map root node to the target map node is a target structure path. And obtaining a complete behavior sequence of the standardized bill data according to the unique identifier and the behavior log. And constructing a behavior structure joint sequence according to the target structure path and the complete behavior sequence. And inputting the behavior structure joint sequence into the structure perception embedding model to obtain a sequence embedding vector. And obtaining a structure behavior inconsistency score and an abnormal type label according to the structure behavior consistency evaluation function, the behavior structure joint sequence and the sequence embedding vector. And generating a suggestion text according to the structure behavior inconsistency score and the abnormal type label, and displaying the suggestion text. Accurate identification of abnormal bills is realized.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for collaborative bill management based on multi-source heterogeneous data fusion. Background Art

[0002] In rail transit projects, district development and related supporting industries, bills serve as the key medium between project execution and financial disbursement. They run through multiple links such as project planning, contract performance, progress verification, fund payment and tax filing, and carry data interaction and process drive between multiple systems and multiple roles. The same bill may exist in multiple systems with different codes, formats, times, and amounts, resulting in serious fragmentation of bill data at both the semantic and process levels. Not only is the reconciliation cost high and the process penetration poor, but it is also difficult to support attribution judgments and behavioral audits centered on the project structure. Currently, common methods in the industry rely on field comparison or fixed rule matching, which cannot identify the legal behavior path of bills across the system life cycle, and it is difficult to discover hidden violation risks caused by process detours, skipped execution, and authority mismatches. Therefore, how to accurately identify abnormal bills has become a technical problem that needs to be solved urgently. Summary of the Invention

[0003] The main purpose of the embodiments of the present application is to propose a bill collaborative management method and system based on multi-source heterogeneous data fusion, aiming to accurately identify abnormal bills.

[0004] To achieve the above objectives, a first aspect of an embodiment of the present application proposes a method for collaborative bill management based on multi-source heterogeneous data fusion, the method comprising:

[0005] Obtain standardized bill data;

[0006] Performing a hash calculation based on each of the standardized bill data to obtain a unique identifier;

[0007] Obtaining a behavior log and a preset structure graph; wherein the structure graph includes a graph root node and multiple graph nodes;

[0008] Obtain the graph node corresponding to the standardized bill data to obtain a target graph node; wherein the path from the graph root node to the target graph node is a target structure path;

[0009] Acquire a complete behavior sequence of each of the standardized ticket data according to the unique identifier and the behavior log;

[0010] Constructing a behavior structure joint sequence according to the target structure path and the complete behavior sequence;

[0011] Inputting the behavior-structure joint sequence into a preset structure-aware embedding model to obtain a sequence embedding vector;

[0012] Calculate based on a preset structure-behavior consistency evaluation function, the behavior-structure joint sequence, and the sequence embedding vector to obtain a structure-behavior inconsistency score and anomaly type label;

[0013] Suggested text is generated and displayed according to the structure-behavior inconsistency score and the anomaly type label.

[0014] In some embodiments, generating and displaying suggested text based on the structure-behavior inconsistency score and the anomaly type label includes:

[0015] Scoring is performed according to a preset collaborative suggestion scoring function, the sequence embedding vector, the structure-behavior inconsistency score, the anomaly type label, and a preset role responsibility vector to obtain a collaborative suggestion score;

[0016] If the collaborative suggestion score is greater than a preset suggestion threshold, generating the suggestion text according to the structure-behavior inconsistency score and the anomaly type label;

[0017] The suggested text is displayed in the bill view of the corresponding role.

[0018] In some embodiments, scoring according to a preset collaborative suggestion scoring function, the sequence embedding vector, the structure-behavior inconsistency score, the anomaly type label, and a preset role responsibility vector to obtain a collaborative suggestion score includes:

[0019] The collaborative suggestion scoring function is:

[0020] ;

[0021] in, Indicates standardized bill data , the system determines whether it should be Collaborative recommendation scores for focused attention and interventional treatment, represents the sequence embedding vector, Representing a role The attention preference vector, express The transpose of Indicates the structural behavior inconsistency score, represents the indicator function, Indicates the exception type label , Representing a role The set of exceptions to watch. 、 Represents the weight parameter.

[0022] In some embodiments, obtaining standardized bill data includes:

[0023] Obtain original bill data from different systems;

[0024] Performing unified field processing on the original bill data to obtain initial bill data;

[0025] The initial bill data is searched and the initial bill data that meets the preset conditions is merged to obtain the standardized bill data.

[0026] In some embodiments, obtaining the graph node corresponding to the standardized bill data to obtain a target graph node includes:

[0027] Calculating the score of mapping the standardized bill data to each of the graph nodes according to a preset anchor scoring function to obtain an anchor score set;

[0028] Obtaining the highest anchor score in the anchor score set to obtain a target anchor score;

[0029] Obtain the graph node corresponding to the target anchor score to obtain the target graph node.

[0030] In some embodiments, calculating the score of mapping the standardized bill data to each of the graph nodes according to a preset anchor scoring function to obtain an anchor score set includes:

[0031] The anchor scoring function is:

[0032] ;

[0033] ;

[0034] ;

[0035] in, Represents standardized bill data Mapping to graph nodes The anchor score, Represents standardized bill data Amounts in the middle and graph nodes Proportional similarity of historical amounts, Represents standardized bill data The invoicing time is in the graph node The probability value under the time density function is generated by kernel density estimation, represents the structural level penalty regularization term, Indicates the missing length of the longest common subpath between the bill contract path and the node path, represents the structural penalty coefficient, Represents standardized bill data The amount field, Represents a graph node The average amount of historical vested notes, Represents standardized bill data The contract number, Represents a graph node The project level path, 、 、 They respectively represent the weight coefficients of amount similarity, time similarity and structural level penalty items in the overall anchor score.

[0036] In some embodiments, the calculation based on a preset structure-behavior consistency evaluation function, the behavior-structure joint sequence, and the sequence embedding vector to obtain a structure-behavior inconsistency score and anomaly type label includes:

[0037] The structural behavior consistency evaluation function is:

[0038] ;

[0039] ;

[0040] in, represents the structural behavior consistency score, represents the sigmoid activation function, represents the sequence embedding vector, Represents a structure node The normal behavior embedding mean, represents the position offset penalty term, Indicates the structural behavior inconsistency score, Represents the weight coefficient of the position offset penalty term.

[0041] To achieve the above objectives, a second aspect of the embodiments of the present application proposes a collaborative bill management system based on multi-source heterogeneous data fusion, the system comprising:

[0042] A first acquisition module is used to acquire standardized bill data;

[0043] A first calculation module, configured to perform a hash calculation based on each of the standardized bill data to obtain a unique identifier;

[0044] A second acquisition module is used to acquire the behavior log and a preset structure graph; wherein the structure graph includes a graph root node and multiple graph nodes;

[0045] A third acquisition module is used to acquire the graph node corresponding to the standardized bill data to obtain a target graph node; wherein the path from the graph root node to the target graph node is a target structure path;

[0046] a fourth acquisition module, configured to acquire a complete behavior sequence of each of the standardized bill data according to the unique identifier and the behavior log;

[0047] A construction module, configured to construct a behavior-structure joint sequence according to the target structure path and the complete behavior sequence;

[0048] An input module, configured to input the behavior-structure joint sequence into a preset structure-aware embedding model to obtain a sequence embedding vector;

[0049] A second calculation module is used to calculate according to a preset structure-behavior consistency evaluation function, the behavior-structure joint sequence and the sequence embedding vector to obtain a structure-behavior inconsistency score and an anomaly type label;

[0050] A display module is used to generate and display suggested text based on the structural behavior inconsistency score and the abnormality type label.

[0051] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0052] To achieve the above-mentioned purpose, the fourth aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method described in the first aspect.

[0053] This application proposes a collaborative bill management method and system based on multi-source heterogeneous data fusion. The method obtains standardized bill data and performs a hash calculation on each piece of standardized bill data to obtain a unique identifier. The method then obtains a behavior log and a pre-set structure graph, where the structure graph includes a graph root node and multiple graph nodes. The method then obtains the graph node corresponding to the standardized bill data and obtains the target graph node. The path from the graph root node to the target graph node is the target structure path. Based on the unique identifier and the behavior log, the complete behavior sequence for each piece of standardized bill data is obtained. A behavior-structure joint sequence is constructed based on the target structure path and the complete behavior sequence. The behavior-structure joint sequence is input into a pre-set structure-aware embedding model to obtain a sequence embedding vector. Calculations are performed based on a pre-set structure-behavior consistency evaluation function, the behavior-structure joint sequence, and the sequence embedding vector to obtain a structure-behavior inconsistency score and anomaly type label. Recommendation text is generated and displayed based on the structure-behavior inconsistency score and anomaly type label. Sensitivity scores are assigned to bill anomalies and recommendations are generated, enabling different functional departments to accurately identify key risks and obtain personalized handling recommendations within their respective views, thereby accurately identifying abnormal bills. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of a method for collaborative bill management based on multi-source heterogeneous data fusion provided by an embodiment of the present application;

[0055] Figure 2 yes Figure 1 Flowchart of step S101 in FIG.

[0056] Figure 3 yes Figure 1 Flowchart of step S104 in FIG.

[0057] Figure 4 yes Figure 1 Flowchart of step S109 in FIG.

[0058] Figure 5 This is a structural diagram of a bill collaborative management system based on multi-source heterogeneous data fusion provided by an embodiment of the present application;

[0059] Figure 6 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0061] It should be noted that although the system diagrams illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the system or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0063] The same bill may exist in multiple systems with different codes, formats, times, and amounts, resulting in severe fragmentation of bill data at both the semantic and process levels. This not only increases reconciliation costs and reduces process penetration, but also makes it difficult to support attribution judgments and behavioral audits centered on engineering structures. Current industry-wide approaches rely on field comparisons or fixed rule matching, failing to identify the legitimate behavior paths of bills across their lifecycles. This makes it difficult to detect hidden violations caused by process bypasses, skipped execution steps, and permission mismatches.

[0064] Based on this, embodiments of the present application provide a collaborative bill management method and system based on multi-source heterogeneous data fusion. This system aims to achieve full-link modeling capabilities, from data fusion, path reconstruction, anomaly identification, to multi-role response, by integrating standardized bill objects with graph-based engineering structures. First, a unique cross-system bill primary key is constructed based on key attributes such as contract number, amount, and time. This key is then assigned to a precise engineering structure node through a graph anchoring mechanism, achieving a transition from traditional field matching to structural semantic alignment. Second, behavioral sequence optimization is guided by the structural path, effectively suppressing system drift and process misalignment. Furthermore, by fusing behavioral paths with structural anchoring information, a structure-behavior consistency assessment mechanism is constructed. This incorporates a structure-aware embedding model and a process deviation regularization term to identify multiple types of anomaly patterns, including behavioral misalignment, structural overreach, and path anomalies. Furthermore, a multi-role collaborative perception and recommendation output mechanism is designed. By combining the response preferences of each business role in historical processes, sensitivity scoring and recommendation generation are performed for bill anomalies. This enables different functional departments to accurately identify risk priorities and obtain personalized handling recommendations within their respective views, thus accurately identifying anomaly bills.

[0065] The collaborative bill management method and system based on multi-source heterogeneous data fusion provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the collaborative bill management method based on multi-source heterogeneous data fusion in the embodiments of the present application is described.

[0066] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0067] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0068] The method for collaborative management of bills based on multi-source heterogeneous data fusion provided in the embodiments of the present application can be applied to a terminal, can be applied to a server side, and can also be software running on a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the method for collaborative management of bills based on multi-source heterogeneous data fusion, etc., but is not limited to the above forms.

[0069] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0070] Please refer to Figure 1 , Figure 1This is a flowchart of a method for collaborative bill management based on multi-source heterogeneous data fusion provided by an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S109.

[0071] Step S101, obtaining standardized bill data;

[0072] Step S102, performing hash calculation based on each standardized bill data to obtain a unique identifier;

[0073] Step S103: Obtaining the behavior log and a preset structure graph; wherein the structure graph includes a graph root node and multiple graph nodes;

[0074] Step S104: Obtain the graph node corresponding to the standardized bill data to obtain the target graph node; wherein the path from the graph root node to the target graph node is the target structure path;

[0075] Step S105 , obtaining a complete behavior sequence of each standardized bill data according to the unique identifier and the behavior log;

[0076] Step S106, constructing a behavior structure joint sequence according to the target structure path and the complete behavior sequence;

[0077] Step S107: inputting the behavior-structure joint sequence into a preset structure-aware embedding model to obtain a sequence embedding vector;

[0078] Step S108, performing calculations based on a preset structure-behavior consistency evaluation function, a behavior-structure joint sequence, and a sequence embedding vector to obtain a structure-behavior inconsistency score and an anomaly type label;

[0079] Step S109 : generating and displaying suggested text based on the structure-behavior inconsistency score and the abnormality type label.

[0080] See also Figure 2 In some embodiments, step S101 further includes but is not limited to steps S201 to S203:

[0081] Step S201, obtaining original bill data from different systems;

[0082] Step S202: Perform unified field processing based on the original bill data to obtain initial bill data;

[0083] Step S203: searching based on the initial bill data, merging the initial bill data that meets the preset conditions to obtain standardized bill data.

[0084] In step S201 of some embodiments, original bill data is obtained from three core systems, namely, invoice information from the invoice verification platform ,include 、 、 , payment information from the company's financial system , including ticket number, amount, payment time and data from the contract management system , using only the Contract Number field .

[0085] In some embodiments, in steps S202 to S203, when the original bill data is imported into the system through the interface, problems such as inconsistent field naming, different formats, and confusing time standards may occur. Therefore, it is necessary to unify the fields of these data: convert all time fields to UTC timestamp format; convert the amount field to a unified unit of "yuan" with two decimal places; and unify the invoice number field to ; The contract number field is unified as .

[0086] Subsequently, the system is based on 、 、 The three fields are used as primary matching keys for retrieval. Records from the invoice verification platform and the financial system are merged and considered to be the same invoice if the following preset conditions are met:

[0087] completely consistent;

[0088] ,in, Indicates that it comes from the invoice verification platform ;

[0089] Time Difference sky.

[0090] The records that meet the above conditions are merged into standardized bill data and recorded as , indicating the Standardized bill data with unified fields and consistent structure. .

[0091] Through the above steps S201 to S203, without introducing any external manual intervention, the bill data from the invoice verification platform, financial system and contract system are integrated through field unification and approximate merging strategies to generate standardized bill data, thereby realizing collaborative management of bills under multi-source heterogeneous data.

[0092] In step S102 of some embodiments, in order to make the bill have a global unique identifier in the entire system, a hash unique identifier is constructed. , which is generated as shown in the following formula (1):

[0093] , (1)

[0094] in, Represents a unique identifier, Indicates the invoice number, Indicates the amount field, Indicates the contract number, Indicates the unified timestamp format. Indicates the use of a deterministic hash function based on algorithm SM3. Indicates a field concatenation operation, which is encoded in UTF-8 and then input into the hash function.

[0095] In one example, an invoice , Yuan, ", , after splicing, the input is , whose hash value is the bill .

[0096] To prevent data confusion caused by hash collisions, the system sets up an automatic verification mechanism to detect whether but If there is a conflict, mark it and submit it for manual review.

[0097] In step S103 of some embodiments, the behavior log comes from the contract system, invoice verification platform, approval platform, etc. , each is a triple , the record unique identifier is The bill at the time The behavior that occurred . Structural map Including the graph root node and multiple graph nodes, each node Represents a structured engineering unit, containing the following fields:

[0098] : The contract number associated with the node;

[0099] : The average amount of historically attributed bills, used to assist in judgment;

[0100] : The engineering level path where the node is located, such as "Line > Section > Section > Unit";

[0101] : plot number or stake number, used for structure adjacency calculation;

[0102] : Invoice time distribution density function of historical bills.

[0103] See also Figure 3 In some embodiments, step S104 further includes but is not limited to steps S301 to S303:

[0104] Step S301: Calculate the score of the standardized bill data mapped to each graph node according to a preset anchor scoring function to obtain an anchor score set;

[0105] Step S302: Obtain the highest anchor score in the anchor score set to obtain the target anchor score;

[0106] Step S303: Obtain the graph node corresponding to the target anchor score to obtain the target graph node.

[0107] In step S301 of some embodiments, in order to resolve the ambiguity of the attribution of the structure graph, the following anchor scoring functions shown in formulas (2) to (4) are proposed:

[0108] , (2)

[0109] , (3)

[0110] , (4)

[0111] in, Represents standardized bill data Mapping to graph nodes The anchor score, Represents standardized bill data Amounts in the middle and graph nodes Proportional similarity of historical amounts. Represents standardized bill data The invoicing time is in the graph node The probability value under the time density function is generated by kernel density estimation. Represents a structural level penalty regularization term, which is used to penalize the anchoring of standardized bill data to graph nodes with large differences in logical paths. Indicates the missing length of the longest common sub-path between the bill contract path and the node path. For example, if "line-section" matches "line-section-section", one layer is missing. . represents the structural penalty coefficient, Represents standardized bill data The amount field, Represents a graph node The average amount of historical vested notes, Represents standardized bill data The contract number, Represents a graph node The project level path. 、 、 They respectively represent the weight coefficients of amount similarity, time similarity, and structural level penalty in the overall anchor score, and are used to regulate the influence of each indicator on the final anchor score.

[0112] In some embodiments, in step S302 to step S303, the highest anchor score in the anchor score set is directly obtained to obtain the target anchor score, and the graph node corresponding to the target anchor score is the target graph node. Among all the related candidate graph nodes, the candidate graph node with the highest score is selected as the target graph node, as shown in the following formula (5):

[0113] , (5)

[0114] in, represents the target graph node, represents a candidate graph node, Represents standardized bill data Mapping to graph nodes Anchor score.

[0115] Furthermore, from the root node of the graph to the target graph node The path is the target structure path, the target graph node set , each Identifies the structural entity. Represents a set of mapping relationships between unique identifiers and target graph nodes, which is used for subsequent behavioral link modeling.

[0116] Through the above steps S301 to S303, each standardized bill data is accurately matched to its corresponding target graph node , thereby establishing a semantic connection between standardized invoice data and engineering structures. This structural attribution is not only a key prerequisite for visualizing the entire invoice lifecycle, but also provides the basic coordinates of physical locations and business units for subsequent process behavior modeling and anomaly detection.

[0117] It should be noted that in rail transit projects, the contracts on which the bills are based often correspond to multiple bidding sections, and there are multiple contracts with the same name or partially duplicate numbers. For example, the "M101 Line Civil Engineering Bid 02" and the "M101 Line Civil Engineering Bid 03" differ by only one digit in the contract number, and may use different abbreviations in different systems. At the same time, there are obvious structural differences between different lines, sections and facility types, so simple contract number matching can easily lead to attribution errors. Traditional primary key connection or fuzzy comparison methods cannot fully characterize these complex structures. In this step, the anchor scoring function is not only based on contract number matching, but also integrates multiple features such as amount, time distribution, and structural hierarchy, and designs a structural penalty regularization term for "engineering structure heterogeneity" to improve the stability and accuracy of anchoring.

[0118] In some embodiments, in steps S105 to S106, the behavior log is sorted by unique identifier Log Aggregation is performed to obtain the complete behavior sequence of each standardized bill data, as shown in the following formula (5):

[0119] , (5)

[0120] in, Represents standardized bill data The complete sequence of actions performed in all business systems, Indicates sorting in ascending order of time. Indicates behavior, Represents a unique identifier, A unique identifier for the ticket corresponding to a behavior event in the behavior log record.

[0121] Next, a behavior-structure joint sequence is constructed based on the target structure path and the complete behavior sequence, as shown in the following formula (6):

[0122] , (6)

[0123] in, represents a joint sequence of behavioral structures, Indicates the target structure path (such as [line number, section number, lot number]). Represents a complete behavior sequence. The symbol ‖ represents the concatenation operation of structural information and behavioral information.

[0124] In step S107 of some embodiments, in order to further support subsequent anomaly detection, consistency assessment and other tasks, the behavior structure joint sequence is constructed. Then, the behavior structure is combined with the sequence Input into a structure-aware embedding model to generate a low-dimensional sequence embedding vector .

[0125] The structure-aware embedding model is based on a bidirectional gated recurrent network (Bi-GRU) and a structural position encoding module, which can simultaneously capture the temporal contextual dependencies of the behavior sequence and the hierarchical constraints of the structural path. Specifically, the behavior-structure joint sequence Each structural element and behavioral node in will be encoded as a fixed-dimensional vector. The structural part adopts the path hierarchical embedding method (for example, [line, section, section] is mapped to three-level position information), and the behavioral part is converted into a time-sensitive input vector based on the dictionary mapping and position encoding method. The final sequence embedding vector is generated by the following formula (9):

[0126] , (9)

[0127] in, represents the sequence embedding vector, Represents a bidirectional GRU network based on joint input of behavior and structure, used to extract contextual sequence representation. Represents a structural path The output of the path-level encoder encodes the structural depth information, It represents the weight of the control structure embedded in the overall representation. The empirical value is set between 0.2 and 0.5 and adjusted according to the complexity of the project.

[0128] Sequence Embedding Vector It integrates structural information and behavioral evolution patterns, and can provide stable, low-dimensional, and semantically sufficient input representation for subsequent structural consistency assessment modules. It is also compatible with the distance calculation and classifier operations of subsequent modules, ensuring the end-to-end reproducibility and business adaptability of the overall solution.

[0129] In step S108 of some embodiments, in actual engineering scenarios, “behavior structure inconsistency” is usually manifested in the following situations:

[0130] Section It belongs to "civil construction", but the bill behavior shows "material storage" or "equipment maintenance" operations;

[0131] The ordering of nodes in the behavior sequence violates the normal flow of the structure, such as "payment" comes before "audit";

[0132] Certain behaviors never appear under this structural path (for example, “application for payment on behalf of” appears in the supervisory unit’s bill).

[0133] Therefore, it is necessary to identify abnormal bills whose behavior process is inconsistent with the attribution structure through the structure-behavior consistency evaluation function. The core of the structure-behavior consistency evaluation function is to evaluate At the current structure node To this end, pre-calculate the matching probability of each node in the structure graph. The corresponding historical normal embedding mean , and the deviation is modeled based on the Euclidean distance. The structural behavior consistency evaluation function is shown in the following formulas (10) and (11):

[0134] , (10)

[0135] , (11)

[0136] in, represents the structural behavior consistency score, represents the sigmoid activation function, represents the sequence embedding vector, Represents a structure node The normal behavior embedding mean is constructed through a large number of historical "normal tickets" reviewed by humans. Represents the position offset penalty term, indicating whether the key nodes in the behavior sequence have abnormal positions (for example, "payment" comes before "signature"). There is a violation of the order of behavior in The process template of the structure node, then , otherwise it is 0. The process template comes from the business sub-process definition in the structure map. For example, the process under "Section B3" should be: [Contract signing → Acceptance → Invoicing → Audit → Payment]. Indicates the structural behavior inconsistency score, Lower values indicate more consistent behavior with structure, and higher values indicate a higher risk of inconsistency. When the score is greater than the threshold of 0.6, it is marked as abnormal. Represents the weight coefficient of the position offset penalty term.

[0137] Furthermore, the exception type label It is divided into three categories: Type 1 is "behavior misalignment", Type 2 is "structural overreach", and Type 3 is "path abnormality". It is generated by the following logic:

[0138] like , then it is “behavior dislocation”;

[0139] like Closer to other structural nodes , that is, misattribution of behavior, then it is “structural overreach”;

[0140] If the behavior shows abnormal repetition, jumps, etc., it is classified as "path abnormality".

[0141] See also Figure 4In some embodiments, step S109 further includes but is not limited to steps S401 to S403:

[0142] Step S401: Scoring is performed based on a preset collaborative suggestion scoring function, a sequence embedding vector, a structure-behavior inconsistency score, an anomaly type label, and a preset role responsibility vector to obtain a collaborative suggestion score;

[0143] Step S402: If the collaborative suggestion score is greater than a preset suggestion threshold, a suggestion text is generated based on the structure-behavior inconsistency score and the anomaly type label;

[0144] Step S403: Display the suggested text in the bill view of the corresponding role.

[0145] In step S401 of some embodiments, in order to allow different business positions (such as financial audit, project management, contract approval, etc.) to form their own perception of bill anomalies, a role-anomaly matching perception mechanism is introduced. Has its attention preference vector , which can be learned through historical processing records, represents the role's focus on sensitive features of different types of anomalies. The collaborative suggestion scoring function is shown in the following formula (11):

[0146] , (11)

[0147] in, Indicates standardized bill data , the system determines whether it should be Collaborative recommendation scores for focused attention and interventional treatment, represents the sequence embedding vector, Representing a role The attention preference vector, dimension and same. express The transpose of Indicates the structural behavior inconsistency score. Indicates the indicator function, indicating the current exception type label Is it a role? Collection of exceptions of interest . 、 Represents the weight parameter, which is used to control the weight influence of abnormality intensity and role responsibility domain in the score.

[0148] In some embodiments, in step S402 to step S403, the threshold value may be set to 0.6. When it is considered that the standardized bill data should cause the role Therefore, a suggestion text is generated based on the structural behavior inconsistency score and the abnormality type label, and the suggestion text is displayed in the bill view of the corresponding role. For example, if the standardized bill data is judged to be an abnormality of "structural overreach", ; , whose attention preference vector It is highly sensitive to bills with “structural overreach”;

[0149] calculate , exceeds the recommended threshold, the system will include the standardized bill data in the "Project Manager View" and generate the following recommended text:

[0150] "The current bill contains cross-structure operations, and the process trigger system is not within the scope of the attribution structure. It is recommended to verify the contract attribution and personnel authority configuration."

[0151] Furthermore, a role view dictionary is constructed as shown in the following formula (12):

[0152] , (2)

[0153] in, Representing a role The collection of bill collaborative views is structured and contains unique identifiers, exception type labels, structural behavior inconsistency scores, and suggested text. Represents a unique identifier, Indicates the exception type label, Indicates the structural behavior inconsistency score, Indicates standardized bill data , the system determines whether it should be Collaborative recommendation scoring for focused attention and interventional treatment. Represents the suggested text, which is obtained based on the template filling and scoring generation mechanism. Indicates the recommended threshold.

[0154] Based on this role view dictionary, the system automatically generates front-end interface content, displaying multi-dimensional information such as the abnormal bill ID, risk type, score, and recommended text. Recommended content is generated through a templated combination. Templates are preset by the business team and populated with abnormality type and structural path context, making them highly controllable and deployable.

[0155] Through the above steps S401 to S403, the bills that have completed the structural behavior consistency scoring and anomaly detection are output and packaged to generate a collaborative view that can be perceived and processed by different business roles, and provide a structured and implementable preliminary suggestion text.

[0156] In steps S101 to S109 shown in the embodiment of the present application, standardized bill data is obtained, and hash calculation is performed on each standardized bill data to obtain a unique identifier. A behavior log and a preset structure map are obtained, wherein the structure map includes a map root node and multiple map nodes. The map node corresponding to the standardized bill data is obtained to obtain a target map node. The path from the map root node to the target map node is the target structure path. The complete behavior sequence of each standardized bill data is obtained according to the unique identifier and the behavior log. A behavior-structure joint sequence is constructed according to the target structure path and the complete behavior sequence. The behavior-structure joint sequence is input into the preset structure-aware embedding model to obtain a sequence embedding vector. Calculation is performed based on the preset structure-behavior consistency evaluation function, the behavior-structure joint sequence and the sequence embedding vector to obtain a structure-behavior inconsistency score and anomaly type label. Suggestion text is generated and displayed based on the structure-behavior inconsistency score and anomaly type label. Sensitivity scoring and suggestion generation are performed for bill anomalies, so that different functional departments can accurately identify risk priorities and obtain personalized processing suggestions in their respective views, thereby accurately identifying abnormal bills.

[0157] See also Figure 5 The embodiment of the present application further provides a bill collaborative management system based on multi-source heterogeneous data fusion, which can implement the above-mentioned bill collaborative management method based on multi-source heterogeneous data fusion. The system includes:

[0158] The first acquisition module 501 is used to acquire standardized bill data;

[0159] A first calculation module 502 is configured to perform a hash calculation based on each standardized bill data to obtain a unique identifier;

[0160] The second acquisition module 503 is used to obtain the behavior log and a preset structure map; wherein the structure map includes a map root node and multiple map nodes;

[0161] The third acquisition module 504 is used to obtain the graph node corresponding to the standardized bill data to obtain the target graph node; wherein the path from the graph root node to the target graph node is the target structure path;

[0162] A fourth acquisition module 505 is configured to acquire a complete behavior sequence of each standardized bill data according to the unique identifier and the behavior log;

[0163] A construction module 506 is used to construct a behavior structure joint sequence according to the target structure path and the complete behavior sequence;

[0164] An input module 507 is used to input the behavior-structure joint sequence into a preset structure-aware embedding model to obtain a sequence embedding vector;

[0165] The second calculation module 508 is used to calculate according to the preset structure-behavior consistency evaluation function, the behavior-structure joint sequence and the sequence embedding vector to obtain the structure-behavior inconsistency score and the abnormality type label;

[0166] The display module 509 is used to generate and display suggestion text based on the structure-behavior inconsistency score and the abnormality type label.

[0167] The specific implementation of the bill collaborative management system based on multi-source heterogeneous data fusion is basically the same as the specific embodiment of the above-mentioned bill collaborative management method based on multi-source heterogeneous data fusion, and will not be repeated here.

[0168] The present application also provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method for collaborative bill management based on multi-source heterogeneous data fusion. The electronic device can be any intelligent terminal, including a tablet computer and an in-vehicle computer.

[0169] See also Figure 6 , Figure 6 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0170] The processor 601 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0171] The memory 602 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 602 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 602 and is called by the processor 601 to execute the bill collaborative management method based on multi-source heterogeneous data fusion according to the embodiments of this application.

[0172] Input / output interface 603, used to implement information input and output;

[0173] Communication interface 604, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0174] Bus 605 , which transmits information between various components of the device (e.g., processor 601 , memory 602 , input / output interface 603 , and communication interface 604 );

[0175] The processor 601 , the memory 602 , the input / output interface 603 and the communication interface 604 are connected to each other in communication within the device via a bus 605 .

[0176] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned bill collaborative management method based on multi-source heterogeneous data fusion.

[0177] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0178] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0179] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0180] The system embodiment described above is merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0181] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0182] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0183] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0184] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the above-mentioned units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0185] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0186] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0187] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0188] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A bill collaborative management method based on multi-source heterogeneous data fusion, characterized by: The method comprises: Obtain standardized bill data; Performing a hash calculation based on each of the standardized bill data to obtain a unique identifier; Obtaining a behavior log and a preset structure graph; wherein the structure graph includes a graph root node and multiple graph nodes; Obtain the graph node corresponding to the standardized bill data to obtain a target graph node; wherein the path from the graph root node to the target graph node is a target structure path; Acquire a complete behavior sequence of each of the standardized ticket data according to the unique identifier and the behavior log; Constructing a behavior structure joint sequence according to the target structure path and the complete behavior sequence; Inputting the behavior-structure joint sequence into a preset structure-aware embedding model to obtain a sequence embedding vector; Calculate based on a preset structure-behavior consistency evaluation function, the behavior-structure joint sequence, and the sequence embedding vector to obtain a structure-behavior inconsistency score and anomaly type label; Suggested text is generated and displayed according to the structure-behavior inconsistency score and the anomaly type label.

2. The method according to claim 1, characterized in that Generating and displaying suggested text according to the structure-behavior inconsistency score and the abnormality type label includes: Scoring is performed according to a preset collaborative suggestion scoring function, the sequence embedding vector, the structure-behavior inconsistency score, the anomaly type label, and a preset role responsibility vector to obtain a collaborative suggestion score; If the collaborative suggestion score is greater than a preset suggestion threshold, generating the suggestion text according to the structure-behavior inconsistency score and the anomaly type label; The suggested text is displayed in the bill view of the corresponding role.

3. The method according to claim 1, characterized in that Scoring according to a preset collaborative suggestion scoring function, the sequence embedding vector, the structure-behavior inconsistency score, the anomaly type label, and the preset role responsibility vector to obtain a collaborative suggestion score includes: The collaborative suggestion scoring function is: ； in, Indicates standardized bill data , the system determines whether it should be Collaborative recommendation scores for focused attention and interventional treatment, represents the sequence embedding vector, Representing a role The attention preference vector, express The transpose of Indicates the structural behavior inconsistency score, represents the indicator function, Indicates the exception type label , Representing a role The set of exceptions of interest, 、 Represents the weight parameter.

4. The method according to claim 1, wherein The obtaining of standardized bill data includes: Obtain original bill data from different systems; Performing unified field processing on the original bill data to obtain initial bill data; The initial bill data is searched and the initial bill data that meets the preset conditions is merged to obtain the standardized bill data.

5. The method according to claim 1, wherein The step of obtaining the graph node corresponding to the standardized bill data to obtain a target graph node includes: Calculating the score of mapping the standardized bill data to each of the graph nodes according to a preset anchor scoring function to obtain an anchor score set; Obtaining the highest anchor score in the anchor score set to obtain a target anchor score; Obtain the graph node corresponding to the target anchor score to obtain the target graph node.

6. The method according to claim 5, characterized in that The step of calculating the score of mapping the standardized bill data to each of the graph nodes according to a preset anchor scoring function to obtain an anchor score set includes: The anchor scoring function is: ； ； ； in, Represents standardized bill data Mapping to graph nodes The anchor score, Represents standardized bill data Amounts in the middle and graph nodes Proportional similarity of historical amounts, Represents standardized bill data The invoicing time is in the graph node The probability value under the time density function is generated by kernel density estimation, represents the structural level penalty regularization term, Indicates the longest missing length of the common subpath between the bill contract path and the node path, represents the structural penalty coefficient, Represents standardized bill data The amount field, Represents a graph node The average amount of historical vested notes, Represents standardized bill data The contract number, Represents a graph node The project level path, 、 、 They respectively represent the weight coefficients of amount similarity, time similarity and structural level penalty items in the overall anchor score.

7. The method according to claim 1, characterized in that The calculation is performed based on a preset structure-behavior consistency evaluation function, the behavior-structure joint sequence, and the sequence embedding vector to obtain a structure-behavior inconsistency score and an anomaly type label, including: The structural behavior consistency evaluation function is: ； ； in, represents the structural behavior consistency score, represents the sigmoid activation function, represents the sequence embedding vector, Represents a structure node The normal behavior embedding mean, represents the position offset penalty term, Indicates the structural behavior inconsistency score, Represents the weight coefficient of the position offset penalty term.

8. A bill collaborative management system based on multi-source heterogeneous data fusion, characterized by: The system comprises: A first acquisition module is used to acquire standardized bill data; A first calculation module, configured to perform a hash calculation based on each of the standardized bill data to obtain a unique identifier; A second acquisition module is used to acquire the behavior log and a preset structure graph; wherein the structure graph includes a graph root node and multiple graph nodes; A third acquisition module is used to acquire the graph node corresponding to the standardized bill data to obtain a target graph node; wherein the path from the graph root node to the target graph node is a target structure path; a fourth acquisition module, configured to acquire a complete behavior sequence of each of the standardized bill data according to the unique identifier and the behavior log; A construction module, configured to construct a behavior-structure joint sequence according to the target structure path and the complete behavior sequence; An input module, configured to input the behavior-structure joint sequence into a preset structure-aware embedding model to obtain a sequence embedding vector; A second calculation module is used to calculate according to a preset structure-behavior consistency evaluation function, the behavior-structure joint sequence and the sequence embedding vector to obtain a structure-behavior inconsistency score and an anomaly type label; A display module is used to generate and display suggested text based on the structural behavior inconsistency score and the abnormality type label.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the bill collaborative management method based on multi-source heterogeneous data fusion as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for collaborative bill management based on multi-source heterogeneous data fusion according to any one of claims 1 to 7 is implemented.

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