Enterprise management method and system based on transaction perspective

Abstract

The application discloses an enterprise management method and system based on a transaction perspective, which comprises the following steps: structuring and registering transaction elements and anchoring context as a starting point, then deducing trends, identifying a candidate set of inflection points and calculating inflection point potential indicators based on a normalized state evolution set, so that the system can immediately depict a fragile stage in a future execution window at the moment when a transaction is initiated; combining an environmental background vector and the inflection point potential indicators to complete path preliminary screening and risk scoring, so as to output several preferred paths which are structurally identifiable, resource-known and risk-traceable before the transaction is started; finally, through man-machine collaborative confirmation, deviation capture and recalibration, influence domain verification and closed-loop archiving, a complete closed loop from optional to controllable to verifiable is formed, and the multi-path generation and robustness quantification and differentiation are realized at the transaction initiation stage.

Description

Technical Field

[0001] This invention relates to the field of business management technology, specifically to a business management method and system based on a transactional perspective. Background Technology

[0002] In daily business operations, tasks such as order placement, production scheduling, credit approval, logistics dispatch, and contract fulfillment generally employ static response methods based on preset rules. A typical existing solution involves building a manually written condition-action rule library. For example, if the contract amount exceeds 5 million yuan, a legal review is triggered; if the equipment maintenance status is pending repair, production scheduling is prohibited. This rule library is configured and permanently implemented. When a transaction is initiated, the system matches each rule against the current input fields, outputting a unique execution command or prompting a blocking action. This type of solution is feasible in scenarios with stable business environments, few variable dimensions, and high historical pattern repetition.

[0003] However, this solution has the following problems: it cannot simultaneously generate multiple feasible execution paths that meet real-world constraints during the transaction initiation phase, nor can it quantitatively differentiate the robustness of each path under the current environment. Because the rule base itself is unaware of time-series evolution patterns and does not integrate real-time resource status or external disturbance signals, its output only indicates whether execution is permitted, rather than specifying the pace, stages, and resource combinations required to reliably achieve the goal. When market volatility intensifies, supply chain nodes temporarily fail, personnel allocation is dynamically adjusted, or external factors such as weather change abruptly, the existing rules cannot predict periods of concentrated potential risks, nor can they assess the absorption capacity of different paths for anomalies. This leads to frequent transaction interruptions, repeated rework, unclear attribution of responsibility, and operational responses lagging behind the actual pace of change. Summary of the Invention

[0004] The present invention aims to provide a business management method and system based on a transaction perspective, which can alleviate the problem of transaction execution breakage caused by the lack of spatiotemporal adaptability of static rules.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a business management method based on a transactional perspective, comprising: The process involves: structuring and anchoring transaction elements to generate attribute graphs, normalized state evolution sets, and environmental background vectors; performing multi-scale temporal evolution trend deduction based on the normalized state evolution sets, identifying inflection point candidate sets, and calculating inflection point potential energy indicators; conducting preliminary screening of feasible path spaces based on the inflection point candidate sets and inflection point potential energy indicators to generate a path set; performing risk scoring on the path set based on the environmental background vectors and inflection point potential energy indicators to form a preferred path set; conducting human-machine collaborative confirmation of the preferred path set to solidify the execution intent, monitoring the execution process, capturing real-time deviations, and performing path adaptive recalibration based on the preliminary screening path set; performing cross-transaction influence domain convergence verification on the recalibrated paths, generating a resource occupancy table, completing the closed-loop archiving of transaction execution, and injecting the full transaction trajectory into the historical trajectory database.

[0006] Preferably, the generation of the attribute graph, normalized state evolution set, and environmental background vector includes: mapping transaction forms into labeled atomic nodes to form an attribute graph; extracting similar historical trajectories and aligning them in a relative time coordinate system to form a normalized state evolution set; converting the real-time snapshot values ​​of the asset ledger and organizational charter into an environmental background vector; and outputting a triple consisting of the attribute graph, the normalized state evolution set, and the environmental background vector.

[0007] Preferably, the step of identifying the candidate set of inflection points and calculating the inflection point potential energy index includes: dividing the execution window into continuous non-overlapping time periods to form a time period change frequency sequence and a high-risk transfer rate sequence; performing sliding window extreme value detection on the time period change frequency sequence and the high-risk transfer rate sequence to identify the candidate set of inflection points; determining weighting coefficients based on the job description associated with the attribute graph, and calculating the inflection point potential energy index by combining the change frequency, high-risk transfer rate and time proximity of the candidate set of inflection points; and outputting a quadruple consisting of the candidate set of inflection points, the inflection point potential energy index, the time period change frequency sequence and the high-risk transfer rate sequence.

[0008] Preferably, the generated path set includes: dividing the execution cycle into rigid segments and elastic segments using the inflection point candidate set as the skeleton; inserting corresponding protective actions by consulting the preset protective action mapping table based on the inflection point potential energy index; implementing a hierarchical pruning strategy to remove paths that do not contain rigid segment protective actions, violate environmental background vector constraints, or exceed the historical median length plus twice the standard deviation; and classifying the remaining paths according to structural fingerprints, retaining at most three paths for each fingerprint category to form a path set.

[0009] Preferably, forming the preferred path set includes: extracting the rigid segment protection action type of each path in the path set, querying the environmental background vector to determine the fit score; combining the inflection point potential energy index with geometric mean and exponential decay to calculate the path robustness score; sorting the top five paths in descending order of score to form the preferred path set; and adding structural fingerprints, rigid segment lookup tables, and activated resource node lists to the preferred path set.

[0010] Preferably, the human-machine collaborative confirmation of the preferred path set includes: pushing a visual timeline interface of the preferred path set to the applicant and the person in charge; receiving acceptance, adjustment or rejection operations, wherein the adjustment operation is limited to the replacement of the dwell time of adjacent states within the elastic segment; generating an execution commitment statement with a condition set and a maximum response interval for each state transition pair in the accepted path; generating a unique text hash value after the execution commitment statement is electronically signed by two people, forming a quintuple containing the selected path, hash value, condition set, maximum response interval and signing time.

[0011] Preferably, the step of capturing real-time deviations and performing path adaptive recalibration based on the initial screening path set includes: collecting operation event streams associated with the status codes of selected paths through the business system interface; detecting deviations where the actual start time of the status exceeds the promised start window; locating the segment where the deviation is located, triggering recalibration for rigid segment deviations or elastic segment deviations that lead to non-performance; screening candidate segments starting from the deviation point based on the initial screening path set, calculating the fit with the current environmental background vector, and selecting the segment with the highest fit to reconstruct the path.

[0012] Preferably, the generation of the resource occupancy table includes: identifying other transactions constituting the influence domain based on attribute graph topology connections for shared resource nodes or job nodes; extracting current load snapshots of all transactions within the influence domain, superimposing incremental load predictions of the recalibrated path to form a comprehensive load; comparing the comprehensive load with the safety threshold stipulated by regulations, selecting similar transactions with lagging progress for nodes exceeding the threshold, performing elastic segment execution window shifting, and generating the final path and corresponding resource occupancy table verified by the influence domain.

[0013] Preferably, injecting the full transaction trajectory into the historical trajectory library includes: after the archiving completion event is verified, packaging the final path full trajectory into a structured archive package; writing the structured archive package, along with the original form, signature hash, recalibration hash, and impact domain operation summary, into a long-term archive; parsing the current transaction into a trajectory instance with an attached environmental background vector and the ratio of actual time consumption to the statutory maximum period, and appending it to the end of the historical trajectory library in the form of an instance-context-result triplet; completing the transaction lifecycle closure and providing a reference sample for subsequent similar transactions.

[0014] On the other hand, this invention proposes a business management system based on a transactional perspective, comprising: The transaction registration unit is used to perform structured registration and context anchoring of transaction elements, and to generate attribute graphs, normalized state evolution sets and environmental background vectors. The trend extrapolation unit is used to extrapolate the trend of multi-scale temporal evolution based on the normalized state evolution set, identify the candidate set of inflection points and calculate the inflection point potential energy index. The path initial wave unit is used to perform preliminary screening of the feasible path space based on the inflection point candidate set and inflection point potential energy index, and generate a path set. The risk assessment unit is used to score the risk of a set of paths based on environmental background vectors and inflection point potential energy indicators, and to form a set of preferred paths. The intent confirmation unit is used to perform human-machine collaborative confirmation of the preferred path set, thereby making the execution intent explicit and solidified. The deviation calibration unit is used to monitor the execution process, capture real-time deviations, and perform path adaptive recalibration based on the initial screening path set. The impact verification unit is used to perform cross-transaction impact domain convergence verification on the recalibrated path and generate a resource usage table. The archiving and accumulating unit is used to complete the closed-loop archiving of transaction execution, injecting the full transaction trajectory into the historical trajectory library.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention starts with the structured registration and context anchoring of transaction elements, then uses a normalized state evolution set to deduce trends, identify candidate inflection points, and calculate inflection point potential indicators. This allows the system to depict vulnerable phases within the future execution window at the moment a transaction is initiated. Combining environmental background vectors and inflection point potential indicators, it performs initial path screening and risk scoring, thus outputting several structurally identifiable, resource-aware, and risk-traceable optimal paths before the transaction even begins. Finally, through human-machine collaborative confirmation, deviation capture and recalibration, impact domain verification, and closed-loop archiving, a complete closed loop is formed, from optional to controllable to verifiable. This achieves multi-path generation and robustness quantification at the transaction initiation stage, enabling business decision-makers to make choices that balance feasibility and resilience based on current real conditions at the source of the solution, mitigating the transaction execution breakage problem caused by the lack of spatiotemporal adaptability of static rules. Attached Figure Description

[0016] Figure 1 This is a flowchart of the enterprise management method based on a transaction perspective according to the present invention; Figure 2 This is a block diagram of the enterprise management system based on a transaction perspective according to the present invention. Detailed Implementation

[0017] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0018] like Figure 1As shown, this invention proposes a business management method based on a transaction perspective. Its core lies in treating various daily business transactions (such as purchase orders, capacity scheduling, credit approval, logistics dispatch, and contract fulfillment) as dynamic entities with initial trigger points, process constraint sets, target achievement states, and external disturbance response capabilities. At the transaction initiation stage, a dual evolutionary process of path generation and risk characterization is initiated. This process does not rely on static matching from a preset rule base. Instead, within a millisecond-level window after the transaction definition is completed, based on currently available multi-source time-series observations, it deduces several feasible execution paths that satisfy all hard boundary and soft adaptation requirements. Specifically, it includes the following steps: The process involves structured registration and context anchoring of transaction elements, generating attribute graphs, normalized state evolution sets, and environmental background vectors. Specifically, this includes: mapping transaction forms to labeled atomic nodes to form attribute graphs; extracting similar historical trajectories and aligning them in a relative time coordinate system to form normalized state evolution sets; converting real-time snapshot values ​​from asset ledgers and organizational charters into environmental background vectors; and outputting triples composed of attribute graphs, normalized state evolution sets, and environmental background vectors. This achieves computable anchoring of transaction semantics, ensuring that all subsequent deductions are based on a unified, verifiable, and real-time contextualized factual foundation.

[0019] Multi-scale temporal evolution trend deduction is performed based on the normalized state evolution set to identify inflection point candidate sets and calculate inflection point potential energy indicators. Specifically, this includes: dividing the execution window into continuous non-overlapping time periods to form a time period change frequency sequence and a high-risk transfer rate sequence; performing sliding window extreme value detection on the time period change frequency sequence and the high-risk transfer rate sequence to identify inflection point candidate sets; determining weighting coefficients based on the job descriptions associated with the attribute graph, and calculating inflection point potential energy indicators by combining the change frequency, high-risk transfer rate, and time proximity of the inflection point candidate sets; and outputting a quadruple consisting of the inflection point candidate set, the inflection point potential energy indicator, the time period change frequency sequence, and the high-risk transfer rate sequence.

[0020] It can identify key risk inflection points in organizational operations in advance and quantify their urgency and impact, providing decision support for timely strategy adjustments and resource optimization. Through dynamic monitoring and analysis, it enhances sensitivity to and response efficiency to potential changes in time-series data.

[0021] The feasible path space is initially screened based on the inflection point candidate set and inflection point potential energy index to generate a path set. Specifically, this includes: dividing the execution cycle into rigid and elastic segments using the inflection point candidate set as the skeleton; inserting corresponding protective actions by consulting the pre-set protection action mapping table according to the inflection point potential energy index; implementing a hierarchical pruning strategy to remove paths that do not contain rigid segment protection actions, violate environmental background vector constraints, or exceed the historical median length plus twice the standard deviation; and classifying the remaining paths according to structural fingerprints, retaining at most three paths for each fingerprint category to form a path set.

[0022] While ensuring compliance and practical feasibility, multiple execution paths with diverse structures, controllable pace, and resource adaptability are generated to avoid systemic execution risks caused by dependence on a single path.

[0023] Risk scoring is performed on the path set based on environmental background vectors and inflection point potential energy indicators to form a preferred path set. Specifically, this includes: extracting the rigid segment protection action type of each path in the path set, querying the environmental background vector to determine the suitability score; calculating the path robustness score using geometric mean and exponential decay in combination with the inflection point potential energy indicator; selecting the top five paths in descending order of score to form the preferred path set; and adding structural fingerprints, rigid segment lookup tables, and activated resource node lists to the preferred path set.

[0024] Achieving a precise match between path robustness and real-world conditions enables the risk levels of different paths to be quantifiable, comparable, and traceable, supporting decision-makers in making rational choices that balance certainty and adaptability at the initiation stage of an event.

[0025] The preferred path set is confirmed through human-machine collaboration to make the execution intention explicit and solidified. Specifically, this includes: pushing a visual timeline interface of the preferred path set to the applicant and the person in charge; receiving acceptance, adjustment, or rejection operations, with adjustment operations limited to replacing the dwell time of adjacent states within the flexible segment; generating an execution commitment statement with a condition set and maximum response interval for each state transition pair in the accepted path; generating a unique text hash value after the execution commitment statement is electronically signed by two people, forming a quintuple containing the selected path, hash value, condition set, maximum response interval, and signing time.

[0026] Transform implicit consensus into verifiable, non-repudiable, and enforceable structured commitments to ensure that responsibilities are clearly defined, the pace is controllable, and the basis for action is traceable.

[0027] The system monitors the execution process, captures real-time deviations, and performs adaptive recalibration based on the initially screened path set. Specifically, this includes: collecting operation event streams associated with the status codes of selected paths through business system interfaces; detecting deviations where the actual start time exceeds the promised start window; locating the segment where the deviation occurs, triggering recalibration for rigid segment deviations or elastic segment deviations that could lead to non-performance; filtering candidate segments starting from the deviation point based on the initially screened path set, calculating the fit with the current environment's background vector, and selecting the segment with the highest fit to reconstruct the path. During execution, deviations are automatically identified and local path regeneration is initiated to avoid global interruptions and ensure that transactions continuously converge towards the target in dynamically changing environments.

[0028] The recalibrated path undergoes cross-transaction impact domain convergence verification, generating a resource occupancy table. Specifically, this involves: identifying other transactions constituting the impact domain based on attribute graph topology connections for shared resource nodes or job nodes; extracting current load snapshots of all transactions within the impact domain and overlaying incremental load predictions of the recalibrated path to form a comprehensive load; comparing the comprehensive load with the stipulated safety threshold, and for nodes exceeding the threshold, selecting similar transactions that are lagging behind in progress for elastic segment execution window shifting, generating the final path and corresponding resource occupancy table verified by the impact domain. This ensures that adjustments to a single transaction do not trigger a chain reaction of overload in related transactions, achieving coordinated balance and overall stability in resource occupancy across multiple transactions.

[0029] Complete the transaction execution closed-loop archiving and inject the full transaction trajectory into the historical trajectory library. Specifically, this includes: after the archiving completion event is verified, packaging the final path full trajectory into a structured archive package; writing the structured archive package, along with the original form, signature hash, recalibration hash, and impact domain operation summary, into the long-term archive library; parsing this transaction into a trajectory instance with an attached environmental background vector and the ratio of actual time consumption to the statutory maximum period, and appending it to the end of the historical trajectory library in the form of instance-context-result triples; completing the transaction lifecycle closed loop and providing a reference sample for subsequent similar transactions.

[0030] This transforms every transaction into a reusable, comparable, and iterative business knowledge asset, continuously enhancing the organization's responsiveness and adaptability to similar transactions.

[0031] On the other hand, this invention proposes a business management system based on a transactional perspective, such as... Figure 2 As shown, it includes: The transaction registration unit is used to perform structured registration and context anchoring of transaction elements, and to generate attribute graphs, normalized state evolution sets and environmental background vectors. The trend extrapolation unit is used to extrapolate the trend of multi-scale temporal evolution based on the normalized state evolution set, identify the candidate set of inflection points and calculate the inflection point potential energy index. The path initial wave unit is used to perform preliminary screening of the feasible path space based on the inflection point candidate set and inflection point potential energy index, and generate a path set. The risk assessment unit is used to score the risk of a set of paths based on environmental background vectors and inflection point potential energy indicators, and to form a set of preferred paths. The intent confirmation unit is used to perform human-machine collaborative confirmation of the preferred path set, thereby making the execution intent explicit and solidified. The deviation calibration unit is used to monitor the execution process, capture real-time deviations, and perform path adaptive recalibration based on the initial screening path set. The impact verification unit is used to perform cross-transaction impact domain convergence verification on the recalibrated path and generate a resource usage table. The archiving and accumulating unit is used to complete the closed-loop archiving of transaction execution, injecting the full transaction trajectory into the historical trajectory library.

[0032] In addition, each unit in the above system is also used to implement other steps of the above-mentioned transaction-based business management method, as follows: Step 1: Structured Registration and Context Anchoring of Transaction Elements When any business unit within an enterprise submits a new transaction request, the system first receives a structured form filled out by the applicant. This form contains six mandatory fields: transaction type identifier, expected start time, target deliverable, responsible position, associated resource number, and historical similar transaction number, as well as up to three free descriptive fields. This form content constitutes the original semantic basis of the transaction and is the sole starting point for all subsequent calculations. The system maps each field in the form to labeled atomic nodes, forming a directed attribute graph. The edge relationships in the graph are jointly defined by four authoritative documents: the enterprise's bylaws, job descriptions, asset ledgers, and contract templates, ensuring that the semantic boundaries of each node are clear and unambiguous. For example, the responsible position node is automatically associated with three derived attributes—the equipment list under that position, the available working hours pool, and the approval chain length—through the responsibilities and rights clauses in the bylaws document; the associated resource number node is parsed from the asset ledger to obtain three real-time snapshot values: its current maintenance status, geographical coordinates, and remaining available cycle. This graph structure is not permanently stored but only maintained in memory until the end of step four; its purpose is solely to provide a semantic coordinate system for subsequent time-series data access.

[0033] Based on this, the system retrieves the full trajectory records of all completed instances of this transaction type within the past eighteen months. Each trajectory consists of a timestamp sequence and a corresponding status code sequence. The status codes are assigned values ​​in the set {01, 02, ..., 17} according to a unified coding standard, representing application submission, initial review approval, resource locking, third-party confirmation, initial execution, mid-term review, deviation warning, plan adjustment, secondary confirmation, final delivery, customer acceptance, warranty initiation, abnormal termination, restart of negotiation, alternative execution, inter-period settlement, and archiving completion, respectively. These historical trajectories are not directly reused but serve as a time-series model reference system to mark the relative position of the current transaction's lifecycle stage on the global timeline.

[0034] Specifically, the system extracts the expected start time of the current transaction. And filter out all starting times falling within the interval from the historical trajectory database. The total number of similar transactions within the scope Article, denoted as For each Align its status code sequence along the time axis to the desired value. Using a relative coordinate system with the origin as the origin, we obtain the normalized state evolution curve. ,in Indicates distance The number of days, with a range of values. Status codes outside this range are set to null. This constructs a system... A set of discrete functions This set becomes the direct input source for time series modeling in the next step.

[0035] To make historical patterns more relevant to current real-world applications, the system further introduces context anchoring: all nodes in the attribute graph with real-time snapshot values ​​(such as equipment maintenance status, geographic coordinates, and remaining availability period) are converted into numerical feature vectors. ,in Indicates the first The current value of each snapshot attribute. This represents the total number of snapshot attributes. This vector... It does not participate in time series modeling, but is embedded as an environmental background parameter into the constraints of all subsequent path generation stages.

[0036] For example, if a piece of equipment is currently under maintenance and awaiting repair, it cannot be included in the resource allocation list of any execution path for the next 30 days; if the geographical coordinates show that the resource is located in a high-risk area during the flood season, all paths involving that resource must avoid the period marked as a red alert in the weather forecast for the next 14 days. At this point, the transaction has completed its first transformation from a natural language description to a computable structure, and the output is a triple. ,in For attribute graphs, For the normalized set of state evolutions, This is the environmental context vector. This triple will serve as the sole input for step two.

[0037] Step 2: Multi-scale temporal evolution trend deduction and key inflection point identification The triplet output in step one Upon entering this step, the primary task is to analyze the set. All The purpose of co-analyzing normalized state evolution curves is to identify the most likely state transition events and their probability distributions within the future execution window. Instead of single-point prediction, an interval coverage approach is used: the entire prediction horizon is divided into... A series of consecutive non-overlapping time periods, denoted as ,in This is the legally mandated maximum execution period for this transaction type (unit: day). The length of each time period is adaptively set according to the transaction complexity; simple transactions take [a certain value]. Each segment lasts 12 days; complex matters are handled accordingly. Each period lasts 7 days. For each time period... ,statistics The frequency of status code changes for all curves within this time period is denoted as . After further statistical changes, the status code enters a high-risk state (defined as the coded value belonging to...). The percentage of a subset of (the subsets of) is denoted as This yields two basic sequences: the time-period change frequency sequence. High-risk transfer rate sequence .

[0038] Based on this, the system... Sequence-based sliding window extreme value detection: setting the window width For each position Calculate the mean within the window Then determine the time period This is a high-frequency change zone. Similarly, for... Perform the same detection on the sequence to obtain high-risk concentration areas. The intersection of these two areas constitutes the candidate set of key inflection points. This set Each time period represents the stage in the transaction's lifecycle where stability is most vulnerable and the intervention window is most urgent. Explicit protective actions must be set in subsequent path design.

[0039] To quantify the impact of inflection points, the system introduces an inflection point potential energy index. Defined as:

[0040] in, The non-negative weighting coefficients satisfy the following conditions: Its value is determined by the attribute graph in step one. The associated job description specifies that: if the primary responsibility position is explicitly defined in the charter as a risk pre-control position, then... If defined as a position primarily responsible for execution efficiency, then ; Indicates time period Start time With the current moment The absolute time difference (unit: day). The longest legally mandated execution period for a transaction This term characterizes the impact of temporal proximity on decision weights. The formula outputs the value... The larger the value, the more redundant resources need to be configured or manual review nodes need to be set up during that period.

[0041] Finally, this step outputs a quadruple. ,in This is a set of candidate time periods for key inflection points. Its corresponding potential energy sequence This is the original statistical sequence. This quadruple will be passed in its entirety to step three, serving as the source of spatiotemporal constraint anchor points during path generation.

[0042] Step 3: Initial screening of feasible path space and structural decoupling for inflection point potential energy The quadruple output in step two Upon entering this step, the system begins constructing a set of feasible execution paths for the transaction. A path is defined as an ordered sequence of state transition pairs, in the form of... ,in For status codes, For path length, all It must belong to the set of legal values ​​in the unified coding standard described in step one, and the first and last states must be application submission (01) and archiving completion (17), respectively. The path generation is not constructed from scratch, but based on the key inflection point period identified in step two. Using the skeleton as a fulcrum, the entire execution cycle is divided into several semantic segments: each Consider it as a rigid segment where a protective action must be inserted; the remaining time periods are merged into elastic segments. For each rigid segment... The system is based on its inflection point potential energy. Consult the pre-set protective action mapping table. This table, jointly signed and effective by the company's quality management department, legal department, finance department, and operations department, specifies the standard response actions corresponding to different potential energy ranges, for example... The process involves dual review, written documentation, and filing with higher authorities. The system automatically verifies, sends SMS reminders, and ensures a closed-loop process on the same day. Corresponding log records and quarterly summary analysis. Each type of action is bound to a unique status code extension set. For example, double review corresponds to the newly added status code 21, automatic system verification corresponds to 22, and so on, ensuring that all status codes in the path can be traced back to the management basis.

[0043] The initial path screening process employs a hierarchical pruning strategy. The first layer removes all paths not included in the initial path selection. The state transition sequence corresponding to any rigid segment in the first layer; the second layer, removes all environmental background vectors from step one that appear in the elastic segment. Forbidden combinations of states, such as If the equipment is in the maintenance pending state, any transfer pair that includes resource activation (status code 05) immediately following maintenance completion (status code 06) is excluded; Thirdly, all path lengths are discarded. Exceeding the historical median length of this transaction type Add twice the standard deviation The sequence, that is, only retaining The path. The median. with standard deviation The statistical results are directly taken from the historical trajectory database in step one, and there is no need to recalculate.

[0044] To ensure path diversity, the system decouples and classifies the remaining paths based on structural features. The path structure fingerprint is defined as a triple. ,in The number of rigid segments (i.e.) (Number of time periods actually used in the middle) This represents the total number of protection action types (i.e., the number of different extended status codes in the path). The average state dwell time within the elastic segment (unit: days) is calculated as follows: for all elastic segments in the path Count the number of status codes it crosses Compared with the actual number of days used ,but ,in This is a set of flexible segments. The system sorts the structural fingerprints of all remaining paths in lexicographical order, retaining at most three paths for each fingerprint type, ensuring that the final output set of initially screened paths uniformly covers the structural dimension.

[0045] The output of this step is a set of paths. ,in Each Each is a complete state transition sequence, accompanied by its structural fingerprint. This set will serve as the direct operational object for risk scoring and depth optimization in step four.

[0046] Step 4: Risk scoring modeling and path optimization integrating environmental background and inflection point potential. The set of paths output in step three In this step, the system no longer focuses on the feasibility of the path, but instead assesses its robustness in a real-world operating environment. The scoring is based on two sources: first, the environmental context vector provided in step one. The second is the inflection point potential energy sequence output in step two. The scoring process does not use weighted summation, but instead employs path-inflection point coupling strength modeling: for each path... First, extract the actual occurrence time periods corresponding to all its rigid segments. (That is, those time periods in the path during which protective actions are inserted), forming a subset. Then extract the path in each The types of protective actions deployed internally are denoted as follows: Finally, look up the table to obtain the action type in the current environment. Fit score This score is updated quarterly by the Enterprise Operations Center and is based on... The closed interval reflects the probability that the action will actually take effect under the current equipment status, geographical location, personnel configuration, and other conditions. For example, if a protective action requires an on-site engineer to verify it, and... If the system shows that there are currently no engineers available in this area, then... If all conditions are met, then .

[0047] The overall path risk score is the geometric mean of the fit scores of all its rigid segments, with an inflection point potential energy decay correction applied, expressed as:

[0048] in, The attenuation coefficient is determined by the transaction type identifier in step one: for financial transactions (identifier includes FIN). For delivery-type transactions (identified by DEL). For compliance-related transactions (identifiers containing COM) This formula ensures that even if two paths have the same fit, a higher sum of inflection point potential energy results in a lower overall score, reflecting the business intuition that higher risk density is less desirable. Scoring Results The closer the value is to 1, the more robust the path is in the current environment.

[0049] After the scoring is completed, the system will... All paths in the middle Sort in descending order, truncate the first few truncations The set of preferred paths consists of 100 paths. ,in Each It also includes three explanatory pieces of information: the first being the corresponding structural fingerprint. The first aspect reveals the compactness of the path, the richness of protection, and the pace of the movement; the second aspect is the rigid sections. The third is a comparison table to help decision-makers identify weaknesses; the fourth is the attribute diagram of the path in step one. The list of all activated resource nodes and job nodes indicates their current occupancy status.

[0050] Thus, starting from the original request, the process undergoes four irreversible stages: structured registration, trend deduction, path generation, and risk characterization. Ultimately, it outputs a set of feasible solutions with controllable quantity, identifiable structure, traceable risks, and knowable resources, providing business decision-makers with truly decision-ready input. This optimal path set... This will serve as the input for step five, initiating the human-machine collaborative confirmation process.

[0051] Step 5: Confirming the Human-Machine Collaboration Path and Explicitly Consolidating the Execution Intent The optimal path set output in step four Upon reaching this step, the system ceases automatic calculations and instead delegates decision-making authority to the smallest collaborative unit comprised of the initiating and responsible personnel. This unit consists of two individuals: the applicant (usually a frontline business staff member) and the responsible person (usually a department head or project director), both of whom must jointly confirm their decision within the same workday. The system simultaneously pushes notifications to both individuals. A visual representation of all paths in the interface, showing each path. Expanded along a horizontal timeline, the axis is marked with status codes, corresponding time periods, names of protective actions, resource occupancy nodes, and each rigid segment. Numerical pairs; the bottom of the interface displays the original transaction target deliverables and expected start times registered in step one. This ensures that all judgments are always anchored to the initial commitment.

[0052] The confirmation process consists of three categories: Accept, Adjust, and Reject. If Accept is selected, the system records the path. Execute the blueprint for the current transaction as a baseline and initiate the intent solidification process; if adjustments are selected, they are only allowed without changing the number of rigid segments. Without adding new status code types, the dwell time of two adjacent statuses within the elastic segment is replaced. For example, the combination of initial execution (08) - mid-term review (09) which took 5 days and 3 days respectively is changed to take 4 days and 4 days. However, no new statuses can be inserted or existing statuses can be deleted. If the option to reject is selected, the system will automatically revert to the complete initial screening set output in step three. Remove all rejected paths, re-execute step four of scoring and truncation, and generate a new round. The message will be pushed again, and this process will repeat a maximum of two times. All operations require electronic signatures from both parties; unilateral operations are invalid.

[0053] To prevent intent drift, the system accepts the path. The execution intent is made explicit and solidified. Specifically, this is done by making each state transition pair in the path explicit. This is mapped to a constrained execution commitment statement, in the form: when Complete and satisfy the condition set At that time, it must be Start within the day .in, From the attribute diagram of step one It consists of all real-time snapshot attributes related to this state, such as When a third party confirms (04), The three Boolean conditions include: contract signed, prepayment received, and tracking number generated. Then it is taken from the historical trajectory set in step two of this state. The median response time is calculated and rounded up to the nearest integer day. All commitment statements are double-checked and then a unique text hash value is generated. The hash value is related to the original form number, the time of double signature, and... Together they are written into the enterprise's blockchain evidence storage node, forming an immutable basis for execution.

[0054] The final output of this step is a quintuple. ,in It is the only selected path after confirmation by human-machine collaboration. The set of conditions upon which all its state transitions depend. The set of maximum allowable response intervals for all its state transitions. This specifies the precise moment when both individuals complete their signatures. This quintuple will serve as the input for step six, becoming the sole triggering reference for all subsequent dynamic response actions.

[0055] Step Six: Real-time Deviation Capture and Path Adaptive Recalibration During Execution The quintuple output in step five After entering this step, the system transitions to transaction execution monitoring mode. Monitoring does not rely on manual data entry; instead, it collects data in real time through interfaces with existing enterprise business systems. The system directly links operation event flows to each status code. For example, when status code 08 (initial execution) appears in the path, the system automatically subscribes to three types of signals in the ERP system: the start-up instruction issuance event, the first-piece inspection pass event, and the material outbound completion event. When status code 11 (customer receipt) appears, it automatically connects to the logistics platform's end-of-line receipt scan event and the customer's CRM system's service evaluation submission event. All events are accompanied by a timestamp of occurrence and a signature from the source system to ensure verifiability.

[0056] Deviation is defined as any state actual start time Exceeding its promised launch window ,in The previous state The actual completion time (determined by the corresponding event stream). Once a deviation is detected, the system immediately initiates path adaptive recalibration: first, it locates the elastic or rigid segment where the deviation occurs; if it is an elastic segment, and the deviation does not prevent subsequent status codes from being executed... If the previous state is completed, only the remaining state within that segment will be updated. The value is compressed proportionally to maintain the overall cycle; if it is a rigid segment, or the deviation has made it impossible for any subsequent state to satisfy its value. If any of the following conditions are met (such as the prepayment not being received but the latest performance date stipulated in the contract has arrived), the recalibration agreement will be triggered.

[0057] The core of the recalibration protocol is local path reconstruction. The system uses the point of deviation occurrence as a starting point. Starting from step three, the initial screening... Filter all by The initial state and structural fingerprint Compared to the original path exist Path segments with a difference of no more than 1 constitute the candidate segment set. For each segment Calculate its relationship with the current environmental background. Real-time adaptability: ; in, For fragments Number of state transition pairs included; Let be an indicator function, taking the value 1 if and only if All conditions must be true at the current moment; otherwise, the value is 0. The system uses the average response time of similar trajectories from step two to determine the state. The estimated completion time is given; The formula essentially measures whether, under current conditions, the segment can be realistically implemented, and whether the implementation pace remains controllable. The higher the value, the more seamlessly the segment can be embedded.

[0058] The output of this step is the reconstructed new path. and its corresponding updated version of the quintuple To ensure the recalibration is complete, all All data has been corrected based on the latest facts. This output will serve as the input for step seven, entering the closed-loop verification process.

[0059] Step 7: Cross-transaction impact domain convergence verification and resource rebalancing The updated quintuple output in step six Upon entering this step, the system initiates an impact domain review. The review scope is not limited to this transaction itself, but extends to all other ongoing transactions sharing the same resource node or role node; these transactions collectively constitute the impact domain of the current transaction. The influence domain is identified based on the attribute graph from step one. Topological connection: if another transaction The set of resource IDs and There is a non-empty intersection, or its main responsibility position is... If the responsible positions are in the same approval chain, then .

[0060] The verification goal is to confirm The execution will not cause any resource node within the domain to overload or any job node to exceed the system's continuous working time limit. (System extraction) A current load snapshot is generated by analyzing three metrics: resource hours, equipment runtime, and number of pending tasks for all transactions in the current state. ; and then according to In each state Predicting the future This path will generate additional similar loads during the day, forming an incremental load forecast. The combined load of the two is If any component exceeds the security threshold specified by the node system, If so, it is determined that there is a cross-transaction conflict.

[0061] Conflict resolution employs resource balancing: the system from Select one from the list. Transactions of the same type (i.e., those with the same transaction type identifier) ​​that are currently behind the planned median. The execution window of a non-critical state within its elastic segment is shifted backward. On the day, among them In order to make All portions fell back to The smallest positive integer below. This translation operation is updated synchronously. A new hash value is generated, and all changes are recorded on the blockchain and notified. The person in charge. This process is executed only once and is not triggered in a chain.

[0062] The output of this step is the final path after influence domain verification. and its supporting resource occupancy table The table lists the net occupancy of each resource in each time period. This path and occupancy table will serve as input for step eight, in the final delivery preparation.

[0063] Step 8: Transaction execution closed-loop archiving and knowledge accumulation injection The final path output in step seven and its resource usage table Upon entering this step, the system initiates transaction lifecycle end management. This occurs when the last status code in the path... (Archiving complete) After the corresponding event is successfully collected and verified, the system automatically performs three actions: First, ... First, package the complete trajectory (including all actual timestamps, condition fulfillment status, deviation occurrence points, recalibration records, and impact domain adjustment logs) into a structured archive package; second, package this along with the original form from step one and the signature hash from step five. Step 6: Hash Recalibration Step 7: Write the impact domain operation summary into the enterprise's long-term archive and retain it for no less than 15 years; Step 3: Initiate the knowledge accumulation and injection process.

[0064] Knowledge accumulation does not extract abstract rules, but rather injects them into the historical trajectory database in the form of instance, context, and result triples. Specifically, the system parses the complete archive of this transaction into a new trajectory instance. Its status code sequence and timestamp sequence form the basic skeleton; then its step one environmental background vector Attach it as a context label; finally, record the actual total time taken for the transaction. With the legally maximum period ratio As the result label. The triplet The data is directly appended to the end of the 18-month historical trajectory database used in step one, becoming the new reference sample for all subsequent similar transactions in step two. The injection process does not modify the original trajectory; it only performs a linear append, ensuring the fidelity of historical data.

[0065] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A business management method based on a transactional perspective, characterized in that, include: Perform structured registration and context anchoring of transaction elements, and generate attribute graphs, normalized state evolution sets, and environmental background vectors; Multi-scale temporal evolution trend inference is performed based on the normalized state evolution set, inflection point candidate set is identified and inflection point potential energy index is calculated; Based on the candidate inflection point set and inflection point potential energy index, the feasible path space is initially screened to generate a path set. Risk scoring is performed on the path set based on environmental background vectors and inflection point potential energy indicators to form a preferred path set; Human-machine collaboration is used to confirm the preferred path set, the execution intention is made explicit and solidified, the execution process is monitored, real-time deviations are captured, and path adaptive recalibration is performed based on the initial screening path set. Perform cross-transaction impact domain convergence verification on the recalibrated path, generate a resource usage table, complete the transaction execution closed-loop archiving, and inject the full transaction trajectory into the historical trajectory library.

2. The enterprise management method based on a transaction perspective according to claim 1, characterized in that, The generated attribute graph, normalized state evolution set, and environmental background vector include: Map transaction forms to labeled atomic nodes to form an attribute graph; Extract similar historical trajectories and align them in a relative time coordinate system to form a normalized set of state evolutions; Transform the real-time snapshot values ​​of the asset ledger and organizational charter into an environmental context vector; Output a triple consisting of the attribute graph, the normalized state evolution set, and the environmental background vector.

3. The enterprise management method based on a transaction perspective according to claim 2, characterized in that, The process of identifying the candidate set of inflection points and calculating the inflection point potential energy index includes: The execution window is divided into consecutive non-overlapping time periods to form a time period change frequency sequence and a high-risk transfer rate sequence; Sliding window extreme value detection is applied to time period change frequency sequences and high-risk transfer rate sequences to identify inflection point candidate sets; The weighting coefficients are determined based on the job descriptions associated with the attribute graph, and the inflection point potential energy index is calculated by combining the change frequency, high-risk transfer rate and time proximity of the inflection point candidate set. Output a quadruple consisting of a candidate set of inflection points, an inflection point potential energy index, a time period change frequency sequence, and a high-risk transfer rate sequence.

4. The business management method based on a transactional perspective according to claim 3, characterized in that, The generated path set includes: The execution cycle is divided into rigid and flexible segments using the inflection point candidate set as the skeleton. Based on the inflection point potential energy index, consult the preset protection action mapping table and insert the corresponding protection action; Implement a layered pruning strategy to remove paths that do not contain rigid segment protection actions, violate environmental background vector constraints, or exceed the historical median length plus twice the standard deviation; The remaining paths are classified according to structural fingerprints, and each fingerprint class retains at most three paths to form a path set.

5. A business management method based on a transactional perspective according to claim 4, characterized in that, The formation of the preferred path set includes: Extract the rigid segment protection action type for each path in the path set, and query the environmental background vector to determine the fit score; The robustness score of the path is calculated by combining the inflection point potential energy index with geometric mean and exponential decay. The top five paths are selected in descending order of their scores to form a preferred path set. To optimize the path set, add structural fingerprints, rigid segment lookup tables, and lists of activated resource nodes.

6. A business management method based on a transactional perspective according to claim 5, characterized in that, The human-machine collaborative confirmation of the preferred path set includes: A visual timeline interface for pushing the preferred path set to the applicant and the person in charge; The system accepts, adjusts, or rejects operations. Adjustment operations are limited to replacing the dwell time of adjacent states within the elastic segment. For each state transition pair in the acceptance path, generate an execution commitment statement with a condition set and a maximum response interval; The execution commitment statement is electronically signed by two people to generate a unique text hash value, forming a quintuple containing the selected path, hash value, condition set, maximum response interval, and signing time.

7. A business management method based on a transactional perspective according to claim 6, characterized in that, The process of capturing real-time deviations and performing adaptive path recalibration based on the initial screening path set includes: Collect operation event streams associated with the status codes of the selected path through the business system interface; The actual startup time detected deviates from the promised startup window. The segment in which the positioning deviation occurs, whether it is a rigid segment deviation or an elastic segment deviation that could lead to non-compliance, triggers recalibration. Candidate segments starting from deviation points are selected based on the initial screening path set. The fit with the current environment background vector is calculated, and the segment with the highest fit is selected to reconstruct the path.

8. A business management method based on a transactional perspective according to claim 7, characterized in that, The generated resource occupancy table includes: Based on the attribute graph topology connection, other transactions constituting the influence domain of shared resource nodes or job nodes are identified. Extract the current load snapshot of all transactions within the impact domain, and overlay the incremental load prediction of the recalibration path to form a comprehensive load; By comparing the overall load with the safety thresholds stipulated by regulations, for nodes that exceed the threshold, select similar transactions that are lagging behind in progress and perform elastic segment execution window shifting to generate the final path and supporting resource usage table verified by the impact domain.

9. A business management method based on a transactional perspective according to claim 8, characterized in that, The step of injecting the full transaction trajectory into the historical trajectory database includes: Once the archiving completion event is verified, the final path full trajectory is packaged to form a structured archive package; Write the structured archive package, along with the original forms, signature hashes, recalibration hashes, and impact domain operation summaries, into the long-term archive. This transaction is parsed into a trajectory instance with an attached environmental background vector and the ratio of the actual time consumed to the statutory maximum period, and appended to the end of the historical trajectory database in the form of an instance-context-result triple. Complete the transaction lifecycle loop and provide a reference sample for subsequent similar transactions.

10. A transaction-based enterprise management system for implementing the method as described in any one of claims 1-9, characterized in that, include: The transaction registration unit is used to perform structured registration and context anchoring of transaction elements, and to generate attribute graphs, normalized state evolution sets and environmental background vectors. The trend extrapolation unit is used to extrapolate the trend of multi-scale temporal evolution based on the normalized state evolution set, identify the candidate set of inflection points and calculate the inflection point potential energy index. The path initial wave unit is used to perform preliminary screening of the feasible path space based on the inflection point candidate set and inflection point potential energy index, and generate a path set. The risk assessment unit is used to score the risk of a set of paths based on environmental background vectors and inflection point potential energy indicators, and to form a set of preferred paths. The intent confirmation unit is used to perform human-machine collaborative confirmation of the preferred path set, thereby making the execution intent explicit and solidified. The deviation calibration unit is used to monitor the execution process, capture real-time deviations, and perform path adaptive recalibration based on the initial screening path set. The impact verification unit is used to perform cross-transaction impact domain convergence verification on the recalibrated path and generate a resource usage table. The archiving and accumulating unit is used to complete the closed-loop archiving of transaction execution, injecting the full transaction trajectory into the historical trajectory library.

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