Electric power marketing digitization method based on business platform

Abstract

The invention provides an electric power marketing digitalization method based on a business platform, and the method comprises the steps: recognizing an electric power marketing business scene, and generating a scene feature identifier; calling a capability matching engine of a service platform according to the scene feature identifier; matching a target atomic power set in an atomic power library of a business platform through the capability matching engine; dynamically arranging and generating a digital marketing process instance based on the target atomic power set; executing the digital marketing process instance, and collecting process operation data in real time; inputting the process operation data into a quality monitoring module of a service platform for anomaly detection; according to the anomaly detection result, triggering a process optimization instruction to a capability matching engine; and updating the atomic power matching strategy based on the process optimization instruction and re-executing the matching operation. The risk management and control capability and the operation efficiency of power marketing can be improved, and the economic benefits and the service stability of power enterprises are guaranteed.

Description

Technical Field

[0001] This application relates to the field of digital electricity marketing technology, and more specifically, to a digital electricity marketing method based on a business middle platform. Background Technology

[0002] In the field of electricity marketing, with the continuous development of the electricity market and the increasing diversification of customer needs, traditional electricity marketing management models face numerous challenges. Regarding process architecture, traditional systems adopt a rigidly designed fixed process model, requiring the separate development of customized processes for each new business scenario. This architecture cannot adapt to rapidly changing marketing needs, especially exhibiting significant lag when handling dynamic business scenarios such as personalized promotional activities and real-time electricity price adjustments. In terms of business capability management, although modern business platforms have accumulated a large number of atomic capability resources, such as basic components like electricity bill calculation engines and user profile generators, three core defects exist: First, a lack of intelligent capability matching mechanisms, failing to automatically select the most suitable combination of atomic capabilities based on specific business scenario characteristics; second, insufficient dynamic orchestration capabilities, frequently resulting in version incompatibility or broken dependencies when manually combining atomic capabilities; and finally, a lack of an effective health monitoring system, making it difficult to promptly detect and replace faulty components.

[0003] The shortcomings in the anomaly handling stage are particularly prominent. Existing technologies mainly rely on offline log analysis, which has the following problems: anomaly response is severely delayed, often requiring the fault to propagate to the system level before it is discovered; optimization and improvement cycles are too long, requiring the entire business process to be redeveloped and deployed each time; historical experience cannot be effectively reused, with the same type of fault repeatedly occurring in different business processes. Furthermore, existing solutions suffer from a disconnect between security control and performance optimization. Access verification typically only performs coarse-grained checks at the process entry point, failing to precisely control access permissions down to each atomic capability level; simultaneously, historical performance data is ignored when selecting capabilities, leading to inefficient components continuously dragging down overall system performance. Summary of the Invention

[0004] The purpose of this application is to provide a digital marketing method for electricity based on a business middle platform, which has the advantages of improving business processing efficiency by dynamically matching atomic capabilities and optimizing processes in real time.

[0005] This application provides a digital power marketing method based on a business middle platform. The technical solution is as follows: A digital power marketing method based on a business middle platform includes the following steps: identifying power marketing business scenarios and generating scenario feature identifiers; calling the capability matching engine of the business middle platform according to the scenario feature identifiers; matching a target atomic capability set in the atomic capability library of the business middle platform through the capability matching engine; dynamically orchestrating and generating digital marketing process instances based on the target atomic capability set; executing the digital marketing process instances and collecting process operation data in real time; inputting the process operation data into the quality monitoring module of the business middle platform for anomaly detection; triggering process optimization instructions to the capability matching engine based on the anomaly detection results; updating the atomic capability matching strategy based on the process optimization instructions and re-executing the matching operation.

[0006] Furthermore, identifying electricity marketing business scenarios includes: receiving business request data packets initiated by user terminals; verifying the protocol legality and data integrity of the business request data packets; parsing the verified business request data packets to extract the business type code and user identity identifier; querying the historical database of the business middleware to obtain historical behavior trajectory data associated with the user identity identifier; inputting the business type code, user identity identifier, and historical behavior trajectory data into the feature fusion processor; generating multi-dimensional scenario feature identifiers through the feature fusion processor; and normalizing the scenario feature identifiers and attaching a timestamp.

[0007] Furthermore, matching the target atomic capability set includes: reading the capability description text and capability dependency table of all atomic capabilities from the atomic capability library; converting the capability description text into standardized capability description vectors; calculating the semantic similarity between the feature vector of the scene feature identifier and each capability description vector; filtering atomic capabilities whose semantic similarity reaches the matching threshold to form a primary candidate set; verifying the call compatibility of each atomic capability in the primary candidate set according to the capability dependency table; removing atomic capabilities with call conflicts or missing dependencies; and packaging the verified atomic capabilities into a target atomic capability set and generating a capability list.

[0008] Furthermore, the dynamic orchestration and generation of digital marketing process instances includes: parsing the input and output parameter formats of each atomic capability in the target atomic capability set; loading the basic process template from the process orchestration rule base of the business middle platform; selecting an appropriate basic process template based on the compatibility of the input and output parameter formats; injecting the atomic capabilities in the target atomic capability set into the node slots of the basic process template in execution order; generating a process instance configuration file and assigning a unique version identifier; performing logical correctness verification on the process instance configuration file; and generating an executable digital marketing process instance after passing the verification.

[0009] Furthermore, real-time acquisition of process execution data includes: deploying data acquisition probes at each execution node of the digital marketing process instance; recording the start and end times of atomic capability calls through the data acquisition probes; capturing parameter passing data and exception event codes during atomic capability execution; combining call time differences, parameter passing data, and exception event codes into process execution data units; serializing process execution data units into process execution data blocks according to the execution order; and associating the process execution data blocks with corresponding process instance version identifiers.

[0010] Furthermore, the process optimization instruction includes: generating an abnormal warning signal when the quality monitoring module detects the continuous occurrence of the same abnormal event code in the process execution data block; extracting the process instance version identifier corresponding to the abnormal warning signal; retrieving historical process execution data blocks based on the process instance version identifier; analyzing the call success rate of atomic capabilities in the historical process execution data blocks; locating faulty atomic capability nodes with a call success rate lower than the preset standard; generating a process optimization instruction containing the identifier of the faulty atomic capability node; and sending the process optimization instruction to the instruction receiving port of the capability matching engine.

[0011] Furthermore, updating the atomic capability matching strategy includes: receiving process optimization instructions and parsing the faulty atomic capability node identifier and fault type code; extracting the faulty node resource utilization curve from the real-time operation monitoring dashboard of the associated quality monitoring module; constructing an atomic capability health assessment model and setting comprehensive assessment indicators including call success rate, response latency, and resource consumption rate; activating the forced degradation procedure when the comprehensive health score is lower than the survival threshold; creating a multi-level degradation routing strategy: Level 1 degradation: retrieving functionally equivalent backup atomic capabilities from the atomic capability library; Level 2 degradation: splitting the faulty atomic capability into multiple sub-capabilities and reorganizing the call chain; Level 3 degradation: calling the cross-domain capability gateway of the business middle platform to obtain external alternative capabilities; implementing gray-scale traffic switching: setting the initial traffic allocation ratio for degradation routing; migrating request traffic to the degradation capability according to a preset incremental ratio; real-time monitoring of the operational health indicators of the degradation capability; freezing the original faulty capability when the degradation capability's health continuously meets the standard within a preset time period; and updating the degradation routing strategy library of the capability matching engine.

[0012] Furthermore, it also includes: deploying a process knowledge graph construction module containing a causal reasoning engine in the business middle platform; importing process execution data blocks into the data cleaning pipeline; extracting fault propagation chains through reverse causal analysis technology: identifying the first trigger node of the abnormal event code; tracing the capability call path dependency relationship upstream of the node; marking all related atomic capabilities in the path as potential fault sources; constructing a multi-dimensional process knowledge graph: using atomic capabilities as entity nodes and attaching historical call success rate attributes; using the call order between capabilities as relation edges and attaching parameter transmission frequency attributes; achieving cross-scenario knowledge reuse through a scenario transfer learner: comparing the similarity matrix of current and historical scenario feature identifiers; pre-filling historical optimization schemes when the similarity exceeds the transfer threshold; generating optimization suggestion packages with confidence ratings: locating the atomic capability groups that need to be replaced; retrieving alternative groups with high capability utility scores from the atomic capability library; generating a topology compatibility verification report for the alternative groups; and pushing the optimization suggestion packages to the strategy configuration interface of the capability matching engine.

[0013] Furthermore, it also includes: before executing a business process instance, invoking the security policy engine of the business middle platform; obtaining the security access permission requirements for each atomic capability in the target atomic capability set; comparing the matching degree between the identity of the user to be operated and the security access permission requirements; when an unauthorized access to an atomic capability is detected, suspending the execution of the digital marketing process instance; generating a permission violation log and returning security alarm information to the user terminal; and updating the blacklist rule base of the security policy engine.

[0014] Furthermore, this application also proposes to include: constructing a capability utility evaluation matrix in the business middle platform; counting the total number of times each atomic capability in the atomic capability library is called in historical process instances; calculating the average call success rate and average response latency of each atomic capability; calculating the capability utility score based on the average call success rate and average response latency; dynamically sorting the atomic capabilities in the atomic capability library according to the capability utility score; converting the sorting result into priority weight coefficients for the capability matching engine; and prioritizing the selection of atomic capabilities with high weight coefficients during the capability matching process.

[0015] As can be seen from the above, the digital marketing method for electricity based on a business middle platform provided in this application solves the problems of rigid processes and low efficiency of capability matching in traditional systems by dynamically matching atomic capability sets, monitoring process operation data in real time, and triggering optimization instructions. It has the advantages of improving business response speed and self-optimization capabilities. Attached Figure Description

[0016] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein:

[0017] Figure 1 This is a flowchart illustrating a digital electricity marketing method based on a business middle platform, as provided in an embodiment of the present invention. Detailed Implementation

[0018] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make the invention more thorough and complete, and to fully convey the scope of the invention to those skilled in the art.

[0019] Those skilled in the art will understand that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0020] It should be noted that the number of any elements in the accompanying drawings is for illustrative purposes only and not as a limitation, and any naming is for distinction only and has no limiting meaning.

[0021] like Figure 1 As shown, this application proposes a digital power marketing method based on a business middle platform, comprising the following steps: identifying power marketing business scenarios and generating scenario feature identifiers; invoking the capability matching engine of the business middle platform according to the scenario feature identifiers; matching a target atomic capability set in the atomic capability library of the business middle platform through the capability matching engine; dynamically orchestrating and generating digital marketing process instances based on the target atomic capability set; executing the digital marketing process instances and collecting process operation data in real time; inputting the process operation data into the quality monitoring module of the business middle platform for anomaly detection; triggering process optimization instructions to the capability matching engine based on the anomaly detection results; updating the atomic capability matching strategy based on the process optimization instructions and re-executing the matching operation.

[0022] It should be noted that scene feature identifiers refer to standardized scene feature vectors generated by parsing business request data and integrating historical behavior data. Specifically, a feature fusion device can be used to perform multi-dimensional feature fusion of business type encoding, user identity identifier and historical behavior trajectory data, and then generate the vectors by adding timestamps after normalization processing. This is used to accurately represent business scene requirements and solve the limitations of traditional manually defined scenes.

[0023] The capability matching engine is a system module that intelligently filters atomic capabilities based on semantic similarity calculation. It uses natural language processing technology to convert atomic capability description text into vectors, calculates their cosine similarity with scene feature vectors, and verifies call compatibility by combining capability dependency relationships, thus solving the problem of discrete capabilities not being able to be intelligently matched. The atomic capability library is a repository storing standardized business function units. Specifically, it can adopt a microservice architecture to encapsulate independent functional modules. Each atomic capability has a clear capability description text, input / output parameter format, and dependency table, supporting dynamic combination and invocation.

[0024] Dynamic orchestration for generating digital marketing process instances refers to automatically constructing executable processes based on the compatibility of atomic capability parameters. Specifically, process template injection technology is used to insert matching atomic capabilities into preset template node slots according to the execution order, generating configuration files with version identifiers, thus solving the problem of hard-coded processes being unable to adapt flexibly. Real-time collection of process execution data involves deploying probes at process execution nodes to capture runtime metrics, including using distributed log collection technology to record atomic capability call time differences, parameter transmission data, and abnormal event codes. These data are serialized into data blocks according to the execution order and associated with process version identifiers, supporting online anomaly detection.

[0025] The quality monitoring module is a functional component that detects anomalies in real time based on operational data. It uses a rule engine to configure trigger thresholds for anomaly event codes. When the same anomaly occurs consecutively, it generates an early warning signal, locates the faulty atomic capability node, and enables rapid anomaly response. The process optimization command is the control signal that triggers the update of the capability matching strategy. It parses the fault node identifier and fault type code, combines them with a health assessment model to generate a multi-level degradation routing strategy, and updates the capability matching priority through gray-scale traffic switching, forming a closed-loop optimization mechanism.

[0026] This application achieves intelligent screening of atomic capabilities through feature vector matching, automatically generates executable processes by combining parameter compatibility, and dynamically updates the matching strategy based on real-time running data, forming a self-healing capability for the process, thus solving the technical bottlenecks of rigid processes, inefficient capability matching, and lagging anomaly handling in traditional solutions.

[0027] Specifically, the system identifies electricity marketing business scenarios and generates scenario feature identifiers. By analyzing user request data packets, extracting business type codes and user identity identifiers, and combining historical behavioral data, multi-dimensional scenario feature identifiers are generated. These scenario feature identifiers serve as input for subsequent capability matching, avoiding the limitations of traditional manually defined scenarios.

[0028] Based on the generated scene feature identifiers, the capability matching engine of the business middleware is invoked. The capability matching engine is responsible for matching the target atomic capability set in the atomic capability library. During the matching process, the capability description text is converted into a standardized capability description vector, the semantic similarity with the scene feature identifier is calculated, and atomic capabilities with similarity reaching the matching threshold are selected. At the same time, the compatibility between atomic capabilities is verified, and capabilities with conflicts or missing dependencies are removed, ultimately forming the target atomic capability set.

[0029] Based on the matched set of target atomic capabilities, a digital marketing process instance is dynamically orchestrated and generated. The orchestration process parses the input and output parameter formats of each atomic capability, selects a suitable basic process template, and injects the atomic capabilities into the template node slots in execution order. After logical correctness verification, the generated process instance configuration file forms an executable digital marketing process instance.

[0030] While executing a digital marketing process instance, real-time process execution data is collected. Data collection probes are deployed at each execution node of the process to record atomic capability call times, parameter passing data, and exception event codes. The collected data is combined into process execution data units and serialized into process execution data blocks according to the execution order.

[0031] Process execution data is input into the quality monitoring module of the business middleware for anomaly detection. When the same abnormal event code is detected consecutively, an anomaly warning signal is generated. Historical process execution data is analyzed to locate faulty atomic capability nodes with a call success rate lower than the preset standard.

[0032] Based on the anomaly detection results, a process optimization instruction is triggered and sent to the capability matching engine. The optimization instruction includes the identifier of the faulty atomic capability node and the fault type code. Upon receiving the optimization instruction, the capability matching engine updates its atomic capability matching policy. The update process includes steps such as building an atomic capability health assessment model, creating a multi-level degradation routing policy, and implementing canary traffic switching.

[0033] Finally, the matching operation is re-executed based on the updated matching strategy to generate an optimized set of target atomic capabilities, which in turn re-orchestrate the digital marketing process instance. This closed-loop mechanism enables continuous process optimization and self-healing capabilities.

[0034] In some embodiments, a business middleware platform is deployed in the electricity marketing system, including components such as a capability matching engine, an atomic capability library, and a quality monitoring module. When a user initiates a real-time electricity price adjustment request via a mobile terminal, the system receives the business request data packet and performs protocol verification.

[0035] After successful verification, the service type code "REAL_TIME_PRICE_ADJUST" and the user identity identifier "USER_123456" are extracted. The historical database is queried to obtain the electricity consumption behavior data of user "USER_123456" for the past 30 days. The service type code, user identity identifier, and historical electricity consumption behavior data are input into the feature fusion processor to generate a scene feature identifier vector.

[0036] The capability matching engine is invoked to read the description text and dependency table of all capabilities in the atomic capability library. The capability description text is converted into a standardized vector, and the cosine similarity with the scene feature identifier vector is calculated. Atomic capabilities with a similarity greater than 0.8 are selected to form a preliminary candidate set, including capabilities such as "user profile generation", "load pattern recognition", and "electricity price strategy calculation".

[0037] Verify the call compatibility of capabilities in the candidate set and remove conflicting capabilities. Generate a capability list of the target atomic capability set, including capability ID, input / output parameter format, and other information.

[0038] Load the basic process template and select the appropriate serial execution template based on the compatibility of the atomic capabilities' input and output parameters. Inject the target atomic capabilities into the template node in the order of "User Profile Generation" → "Load Pattern Recognition" → "Electricity Price Strategy Calculation". Generate a process instance configuration file and assign a unique version identifier "FLOW_20230615_001".

[0039] Deploy data acquisition probes at each execution node of the process instance. Execute the process instance and record the start time, end time, parameter passing data, and exception event code for each atomic capability call. Combine the acquired data into process execution data units and serialize them into process execution data blocks.

[0040] The quality monitoring module analyzed the process execution data blocks and detected that the "load pattern recognition" capability had the "TIMEOUT_ERROR" exception code three times consecutively. An exception warning signal was generated, and the corresponding process instance version identifier "FLOW_20230615_001" was extracted.

[0041] Analysis of historical data revealed that the "load pattern recognition" capability had a recent success rate of only 75%, lower than the preset standard of 90%. A process optimization instruction containing fault node identifiers was generated and sent to the capability matching engine.

[0042] The capability matching engine receives optimization instructions and constructs an atomic capability health assessment model. A multi-level degradation routing strategy is created, with the first level retrieving the functionally equivalent backup capability, "Electricity Behavior Analysis." An initial traffic allocation ratio is set, migrating 20% ​​of request traffic to the "Electricity Behavior Analysis" capability.

[0043] Monitor the operational health metrics of the degradation capability in real time. When the "Electricity Behavior Analysis" capability consistently meets health standards for 30 minutes, gradually increase the traffic allocation ratio. Finally, freeze the original "Load Pattern Recognition" capability and completely switch to the new capability. Update the degradation routing policy library of the capability matching engine for subsequent capability matching processes.

[0044] This application further proposes a method for identifying electricity marketing business scenarios, including receiving business request data packets initiated by user terminals; verifying the protocol legality and data integrity of the business request data packets; parsing the verified business request data packets to extract business type codes and user identity identifiers; querying the historical database of the business middleware to obtain historical behavior trajectory data associated with user identity identifiers; inputting the business type codes, user identity identifiers, and historical behavior trajectory data into a feature fusion processor; generating multi-dimensional scenario feature identifiers through the feature fusion processor; and normalizing and attaching timestamps to the scenario feature identifiers.

[0045] The protocol legitimacy verification of the business request data packet is achieved by comparing the digital signature in the packet header with the public key of the pre-stored certificate. The SHA-256 hash algorithm can be used to verify data integrity. The extraction of the user identity identifier is accomplished by parsing the structured fields in the data packet payload. The business type encoding adopts a four-digit numeric encoding system, for example, "0101" represents the electricity bill payment business.

[0046] The query scope of historical behavior trajectory data is limited to the most recent 12 months, including user operation type, frequency, and associated business codes. The feature fusion processor employs a multilayer perceptron model, with an input layer dimension of 64, a ReLU activation function in the hidden layer, and an output layer that generates multidimensional feature labels in the form of a probability distribution using softmax. Normalization is performed using Z-score standardization to eliminate scale differences between data of different dimensions. The additional precision of the timestamp is set to milliseconds; for example, "20240320143005888" represents March 20, 2024, at 14:30:05:888 milliseconds.

[0047] Specifically, business request data packets are transmitted to the business middleware via the HTTPS protocol. The X.509 digital certificate carried in the packet header is verified for legitimacy through a certificate chain verification mechanism, effectively preventing man-in-the-middle attacks. Data integrity is verified by comparing the hash value calculated by the receiving end with the checksum carried in the packet tail. When the hash value matching error exceeds ±0.5%, a data retransmission mechanism is triggered.

[0048] During the parsing process, the business type code is extracted from the fixed offset address of the data packet, and the user identification is achieved by matching the 18-digit ID card number or 10-digit customer number using regular expressions. Historical database queries employ a columnar storage structure, with a response time controlled within 50ms, and the returned data includes the user's most recent 30 business operation records. The feature fusion model is trained using transfer learning, fine-tuned based on pre-training with 1 million historical business data entries, achieving a feature fusion error rate of less than 2%. The normalized feature identifier values ​​are constrained to the [-1, 1] range, eliminating the influence of different dimensional parameters such as voltage level and electricity consumption. The timestamp appending is synchronized with the NTP time server of the business middleware, with a time deviation controlled within ±10ms, providing a precise timing benchmark for the dynamic adjustment of process instances.

[0049] Scene feature identifiers are normalized and timestamps are appended. Normalization can be achieved using the min-max method, scaling each feature dimension to the [0,1] range. Timestamps are accurate to the millisecond level.

[0050] This application further proposes the following steps: reading the capability description text and capability dependency table of all atomic capabilities from the atomic capability library; converting the capability description text into standardized capability description vectors; calculating the semantic similarity between the feature vector of the scene feature identifier and each capability description vector; filtering atomic capabilities whose semantic similarity reaches the matching threshold to form a primary candidate set; verifying the call compatibility of each atomic capability in the primary candidate set according to the capability dependency table; removing atomic capabilities with call conflicts or missing dependencies; and packaging the verified atomic capabilities into a target atomic capability set and generating a capability list.

[0051] Vectorization of capability description text can be achieved using pre-trained language models, such as generating 768-dimensional semantic vectors through the BERT model, thus eliminating ambiguity in natural language expressions. Semantic similarity calculation can employ a cosine similarity algorithm, with a matching threshold set to 0.85 to balance recall and precision. The capability dependency table can include constraints on the calling order between atomic capabilities, parameter format requirements, and version compatibility rules.

[0052] During compatibility verification, the system can check whether the input and output parameter types of candidate capabilities match. For example, it can check whether the floating-point number output by the electricity bill calculation capability is compatible with the string format required by the bill generation capability. When removing conflicting atomic capabilities, if two capabilities are detected to depend on the same resource and have a mutex lock mechanism, the capability with higher priority is retained.

[0053] Specifically, after generating scenario feature identifiers, the business scenario requirements are quantitatively compared with the semantics of atomic capabilities through standardized vector transformation. As an example, when the similarity between the feature vector of the user profile analysis scenario and the description vector of the electricity cost prediction capability reaches 0.9, the capability is included in the initial candidate set.

[0054] Subsequently, topological sorting verification is performed based on the dependency table. If it is found that the real-time electricity price query capability in the candidate set lacks corresponding data encryption capability support, a dependency missing alarm is triggered and the capability is removed.

[0055] In the final packaged target set, the input parameter format of each atomic capability perfectly matches the output format of its predecessor capability, and the resource utilization index meets the requirements for concurrent execution. Through a dual screening mechanism, mismatches caused by semantic deviations are avoided, and execution anomalies caused by dependency conflicts are eliminated, making the generated process instances self-consistent and executable.

[0056] Preferably, when matching the target set of atomic capabilities, the capability description text and capability dependency table of all atomic capabilities are first read from the atomic capability library. The capability description text contains information such as capability name, functional overview, input parameters, and output parameters. The capability dependency table records the calling dependencies and conflict relationships between each atomic capability.

[0057] Furthermore, the capability description text is converted into standardized capability description vectors. Specifically, natural language processing techniques are used to segment the text and remove stop words. Then, a word embedding model is used to map the text to a high-dimensional vector space to obtain fixed-dimensional capability description vectors.

[0058] Therefore, the semantic similarity between the feature vector of the scene feature identifier and the vectors describing each capability is calculated. For example, the cosine similarity algorithm is used to calculate the cosine value of the angle between the vectors, and the closer this value is to 1, the higher the semantic similarity.

[0059] Furthermore, the call compatibility of each atomic capability in the primary candidate set is verified based on the capability dependency table. Specifically, it checks whether there are conflicting call relationships between candidate capabilities and whether any necessary dependent capabilities are missing.

[0060] Subsequently, atomic capabilities with call conflicts or missing dependencies are removed. For example, if capability A and capability B are mutually exclusive, the one with higher similarity is retained; if capability C depends on capability D, which is not in the candidate set, capability C is removed.

[0061] Finally, the verified atomic capabilities are packaged into a target atomic capability set and a capability list is generated. The capability list contains key information such as the identifier, name, and version number of each atomic capability, which is used for subsequent process orchestration.

[0062] This application further proposes a dynamic orchestration method for generating digital marketing process instances, including: parsing the input and output parameter formats of each atomic capability in the target atomic capability set; loading basic process templates from the process orchestration rule base of the business middle platform; selecting an appropriate basic process template based on the compatibility of the input and output parameter formats; injecting the atomic capabilities in the target atomic capability set into the node slots of the basic process template in execution order; generating a process instance configuration file and assigning a unique version identifier; performing logical correctness verification on the process instance configuration file; and generating an executable digital marketing process instance after passing the verification.

[0063] Parameter format parsing can be achieved using regular expression matching or pattern recognition algorithms, and can convert JSON-formatted input parameters and XML-formatted output parameters into a unified data model. Loading of basic process templates can be based on template priority, prioritizing the template with the highest historical success rate.

[0064] Parameter compatibility checks are implemented by establishing a type mapping table, for example, automatically converting string type parameters to enumeration types. Node slot injection uses a dependency injection framework to ensure that the execution order of atomic capabilities matches the pre-defined flow branch conditions in the template. Version identifier generation uses a combination of timestamps and hash values.

[0065] Once the target atomic capability set is matched, metadata parsing is performed on each atomic capability within the set. Parsing the input parameter format includes identifying the parameter name, data type, and constraints. For example, the electricity bill calculation capability requires the input format {User ID: string, Electricity Consumption: float}. Parsing the output parameter format extracts the return value structure. For example, the user profile generation capability outputs {Profile Tags: array, Confidence Score: decimal}. Basic templates in the process orchestration rule base are stored categorized by business type, and each template defines the input and output interface specifications for node slots. During the template selection phase, the output parameter format of the atomic capability is chained and matched with the input format of the downstream node slots. When the conversion power of parameters from three consecutive nodes is below 80%, a template replacement mechanism is triggered. During atomic capability injection, the execution order is automatically arranged according to capability dependencies. For example, the electricity bill calculation capability must be executed after user profile generation. The process instance configuration file uses YAML format to store node configuration information, and the version identifier includes the process generation time and template hash value. During the logic verification phase, static analysis is used to detect unconnected parameter transmission paths. For example, an alarm is generated when it is found that the electricity bill calculation result is not used by the marketing strategy module. The final generated process instance is encapsulated as an executable DAG workflow, with each node bound to a specific atomic capability call interface.

[0066] This application further proposes real-time acquisition of process operation data, including: deploying data acquisition probes at each execution node of the digital marketing process instance; recording the start and end times of atomic capability calls through the data acquisition probes; capturing parameter passing data and exception event codes during the execution of atomic capabilities; combining the call time difference, parameter passing data, and exception event codes into process operation data units; serializing the process operation data units into process operation data blocks according to the execution order; and associating the process operation data blocks with corresponding process instance version identifiers.

[0067] Data acquisition probes are deployed between the node execution engine and atomic capability interfaces of the process instance, and can be implemented using a lightweight proxy pattern or an embedded code injection pattern. The recording precision of start and end times can be controlled to the millisecond level, and a system clock synchronization mechanism can be used to ensure timestamp consistency. The scope of parameter transmission data capture can include input parameter hash values, output parameter byte lengths, and data checksums. Matching rules for exception event codes can be based on regular expressions or predefined error code mapping tables. The combination of process execution data units adopts a nested key-value pair structure, for example, using the time difference as the base key, parameter data as the subkey, and exception codes as additional attributes. Serialization can use a binary protocol or JSON format, and the execution order is marked by an incrementing sequence number. The process instance version identifier can be associated by embedding version metadata in the data block header or establishing a mapping relationship through a distributed tracking ID.

[0068] When a process instance executes, data acquisition probes deployed on each node synchronously start monitoring. When an atomic capability is invoked, the probe intercepts the call request and records the start timestamp accurate to milliseconds. When the capability returns a result, it captures the end timestamp and the hash value of the output parameter. If an exception event is triggered during execution, the probe matches the exception code with a preset rule base to generate a standardized error identifier. The time difference is calculated by the difference between the end time and the start time; for example, if a node's call takes 35 milliseconds. Parameter-passed data is anonymized, retaining the hash values ​​of key fields; for example, the hash of the input parameter is 0x7d3f, and the hash of the output parameter is 0x9a2e. Exception event codes are stored hierarchically by type; for example, E1001 indicates a database connection timeout.

[0069] The process execution data unit integrates time differences, parameter hashes, and exception codes into a structured data packet, as shown in the example below:

[0070] {"duration":35,"input_hash":"0x7d3f","error_code":"E1001"}.

[0071] Multiple data units are appended to the memory buffer in the order of node execution, forming an ordered data stream. Node position indices are added during serialization; for example, the second node data unit is marked as seq=2. The final generated process execution data block is appended with a process instance version identifier, such as v2.1.5_20231001. This identifier strictly corresponds to the version metadata generated during the process orchestration phase.

[0072] This application further proposes that when the quality monitoring module detects the continuous occurrence of the same abnormal event code in the process running data block, it generates an abnormal warning signal, extracts the corresponding process instance version identifier and retrieves historical process running data blocks, analyzes the success rate of atomic capability calls to locate the fault node, and generates and sends a process optimization instruction containing the fault node identifier to the capability matching engine.

[0073] The continuity detection of exception event codes can employ a sliding window mechanism, for example, triggering an alert signal when the same exception code appears in three consecutive data blocks. The process instance version identifier can include a combination of timestamp and hash value encoding to ensure accurate matching in historical data retrieval. The call success rate can be calculated based on the ratio of successful calls within a time window to the total number of calls, with a preset standard set to a threshold where the success rate is below 90%. Fault node location is achieved by traversing the success rate metrics of all atomic capabilities in the process instance; when a node's success rate is consistently below the threshold, it is automatically marked as a fault node. The generation of process optimization instructions includes a unique identifier for the fault node and an exception type encoding; the instruction receiving port can utilize a message queue middleware for asynchronous transmission.

[0074] Specifically, when the quality monitoring module detects the continuous occurrence of the same abnormal event code in the process execution data block, it triggers the generation of an early warning signal through a sliding window counting mechanism. After extracting the process instance version identifier associated with the early warning signal, it initiates a precise query request to the historical database to obtain all historical execution records of that process instance. Statistical analysis is performed on the atomic capability call records in the historical data block to calculate the call success rate index of each node and compare it with a preset threshold. When the success rate of a specific node is identified as consistently lower than the standard value, an optimization instruction containing the node's identifier is automatically generated. The instruction is transmitted to the capability matching engine via a message queue, triggering subsequent atomic capability replacement or call strategy adjustment. This process achieves rapid location of faulty nodes and policy updates through an automated closed-loop mechanism, avoiding the time delay of manual analysis. At the same time, it effectively identifies recurring failure patterns by utilizing longitudinal comparison of historical data, ensuring the consistency of handling the same abnormality in different process instances.

[0075] This application further proposes an updated atomic capability matching strategy, including: receiving process optimization instructions and parsing the faulty atomic capability node identifier and fault type code; extracting the faulty node resource utilization curve from the real-time operation monitoring dashboard of the associated quality monitoring module; constructing an atomic capability health assessment model and setting comprehensive assessment indicators including call success rate, response latency, and resource consumption rate; activating a forced degradation procedure when the comprehensive health score is lower than the survival threshold; creating a multi-level degradation routing strategy: Level 1 degradation: retrieving functionally equivalent backup atomic capabilities from the atomic capability library; Level 2 degradation: splitting the faulty atomic capability into multiple sub-capabilities and reorganizing the call chain; Level 3 degradation: calling the cross-domain capability gateway of the business middle platform to obtain external alternative capabilities; implementing gray-scale traffic switching: setting the initial traffic allocation ratio for degradation routing; migrating request traffic to the degradation capability according to a preset incremental ratio; real-time monitoring of the operational health indicators of the degradation capability; freezing the original faulty capability when the degradation capability's health continuously meets the standard within a preset time period; and updating the degradation routing strategy library of the capability matching engine.

[0076] The parsing of fault type codes can be achieved through regular expression matching or pre-trained classification models, for example, mapping fault type codes to resource overload, logic error, or external dependency failure. Resource utilization curves can be extracted based on CPU utilization, memory utilization, and network throughput data from a time-series database, with sampling intervals set from 5 to 30 seconds.

[0077] The comprehensive score calculation of the health assessment model can adopt a weighted summation method. For example, the success rate of the call accounts for 40%, the response latency accounts for 30%, and the resource consumption rate accounts for 30%. The survival threshold can be dynamically adjusted based on historical failure data, and the initial value can be set to 60 points.

[0078] In a multi-level degradation routing strategy, the retrieval of functionally equivalent backup atomic capabilities can be achieved through a semantic similarity matching algorithm, with a cosine similarity threshold set to 0.85. When splitting faulty atomic capabilities into sub-capabilities, the segmentation can be based on the dependency relationship between input and output parameters. In implementation, the electricity cost calculation capability can be divided into three sub-modules: basic rate calculation, tiered pricing calculation, and discount deduction calculation.

[0079] Calls to the cross-domain capability gateway require verification of interface protocol compatibility. During implementation, the SOAP protocol can be converted to a RESTful interface. The initial allocation ratio for canary traffic switching can be set to 10%, increasing by 20% daily. Session stickiness must be maintained during traffic migration to avoid data inconsistency. The criteria for continuously meeting health standards can be set to a comprehensive score above the survival threshold and no abnormal event codes generated for 30 consecutive minutes.

[0080] Specifically, when the quality monitoring module detects consecutive occurrences of the same abnormal event code in the process execution data block, a process optimization instruction is generated and sent to the capability matching engine. The fault atomic capability node identifier and fault type code are obtained by parsing the JSON format data in the instruction; for example, the node identifier is "AC-2031" and the fault type code is "E-507". After the real-time operation monitoring dashboard is associated, the resource utilization curve of the fault node is extracted from the Prometheus time-series database to generate a visual chart containing CPU peak values ​​and memory leak trends.

[0081] The atomic capability health assessment model reflects service reliability through call success rate, measures processing efficiency through response latency, and assesses hardware load balancing status through resource consumption rate. When the weighted overall health score falls below a dynamically adjusted survival threshold, a forced degradation procedure is activated. In practice, when the score drops to 55, the first-level retrieval process in the three-level degradation routing strategy is triggered.

[0082] In the absence of a functionally equivalent backup atomic capability, the faulty atomic capability is broken down into multiple sub-capabilities. For example, the user profile generation capability can be broken down into three sub-modules: data collection, feature extraction, and tag classification, and the core business logic can be maintained by reorganizing the call chain. If the breakdown still cannot meet the requirements, a cross-domain capability gateway is invoked to access alternative capabilities provided by external systems, such as calling a third-party credit assessment interface.

[0083] During the canary rollout, the initial 10% of request traffic is routed to the degraded capability, while the remaining traffic is handled by the original faulty capability. The traffic allocation ratio increases by 20% daily, while the health metrics of the degraded capability are monitored through a distributed tracing system. When the degraded capability maintains a health score above the threshold and exhibits no abnormal events for a preset 30-minute period, the original faulty capability is frozen and marked as disabled. The final updated degrade routing strategy is written to the capability matching engine's Redis cache, enabling dynamic policy implementation.

[0084] In some embodiments, creating a multi-level degradation routing policy includes:

[0085] Level 1 Degradation: Retrieves functionally equivalent spare atomic capabilities from the atomic capability library. The system searches for other atomic capabilities marked as "functionally equivalent".

[0086] The second level of degradation involves breaking down a faulty atomic capability into multiple sub-capabilities and reassembling the call chain. For example, a complex data processing capability can be broken down into sub-capabilities such as data cleaning, feature extraction, and model prediction.

[0087] Level 3 degradation: The system invokes the cross-domain capability gateway of the business middle platform to obtain external alternative capabilities. The system may request APIs from other business domains or third-party service providers.

[0088] Implementing a canary traffic switch includes:

[0089] Configure the initial traffic allocation ratio for the degradation route. Direct 10% of traffic to the degradation capability.

[0090] Migrate request traffic to degraded capabilities according to a preset incremental ratio. Increase traffic allocation by 10% every hour.

[0091] Monitor the operational health indicators of degradation capabilities in real time. Continuously collect and calculate the aforementioned health scores.

[0092] The original faulty capability is frozen when the health score of the downgraded capability remains at the target level for a preset period of time. A 24-hour observation period can be set; if the health score of the downgraded capability remains above 80, the original faulty capability is frozen.

[0093] Finally, update the fallback routing policy library of the capability matching engine. Persistently store the new fallback routing rules for reference in subsequent capability matching processes.

[0094] This application further proposes deploying a process knowledge graph construction module containing a causal reasoning engine in the business middle platform; importing process execution data blocks into the data cleaning pipeline; extracting fault propagation chains through reverse causal analysis technology; constructing a multi-dimensional process knowledge graph; realizing cross-scenario knowledge reuse through a scenario transfer learner; generating optimization suggestion packages with confidence ratings; and pushing the optimization suggestion packages to the strategy configuration interface of the capability matching engine.

[0095] Causal inference engines can establish causal relationship chains between nodes through Bayesian networks or structural equation models, unlike conventional statistical tools which can only identify correlations. The data cleaning pipeline uses regular expression matching and missing value imputation algorithms to transform unstructured logs in the raw data into standardized event sequences.

[0096] The reverse causal analysis technique starts from the first trigger node of the abnormal event code and traces upstream along the call path dependencies. For example, it uses a depth-first search algorithm to traverse the call tree and mark all related atomic capability nodes. When attaching historical call success rate attributes to entity nodes in the multidimensional process knowledge graph, a sliding window algorithm is used to calculate the average success rate over the past 30 days. The parameter transmission frequency attribute of relation edges is counted by a hash counter to count the number of interactions per hour. The scene transfer learner uses a cosine similarity algorithm to calculate the similarity matrix of scene feature identifiers. When the similarity exceeds 0.85, the historical optimization scheme migration is triggered. The confidence rating is calculated based on a weighted average of the capability utility score of the alternative group and the topology compatibility verification result, with the capability utility score accounting for 70% and the compatibility verification result accounting for 30%.

[0097] After the process execution data blocks are processed by the data cleaning pipeline, the exception event codes are parsed into standardized error type codes. The causal reasoning engine identifies the first trigger node and then performs reverse analysis along the call path dependencies. For example, it may discover that an anomaly at a certain node might originate from parameter passing errors in three upstream nodes. All associated atomic capabilities are marked as potential failure sources, forming a failure impact domain covering nodes that directly and indirectly affect them. During the construction of the multi-dimensional process knowledge graph, the historical call success rate attribute of entity nodes reveals capabilities with high failure rates, and the parameter passing frequency attribute of relationship edges exposes bottlenecks in high-frequency interaction paths.

[0098] The scenario transfer learner applies optimized solutions from historical promotional campaigns to the current holiday marketing scenario through feature similarity comparison, avoiding redundant solution development. When generating optimization suggestion packages, the topology compatibility verification of the alternative groups focuses on checking the data type matching of input parameters, such as verifying whether numerical parameters are consistent with the target capability interface definition. Finally, the optimization suggestion package pushed to the strategy configuration interface triggers the capability matching engine to automatically update the atomic capability matching strategy, forming a closed-loop optimization mechanism.

[0099] Preferably, a process knowledge graph construction module is deployed within the business middle platform. This module integrates a causal reasoning engine and a graph database. When process execution data blocks are imported into the data cleaning pipeline, noise filtering and field standardization are performed first, such as converting heterogeneous timestamps to UTC format. During reverse causal analysis, the first trigger node corresponding to the abnormal event code is determined through call stack depth parsing, and the upstream capability call path dependency is traced through the call chain identifier in the service mesh log. For example, in a promotional activity process instance, the abnormal event is traced back to the parameter verification failure of the user profile generation capability node. The entity nodes of the multidimensional process knowledge graph are attached with historical call success rate indicators, and the relationship edges record the parameter transmission frequency and data volume statistics, such as the edge between the electricity bill calculation capability and the bill generation capability recording the daily call frequency of millions. The scenario transfer learner uses the feature vector cosine similarity algorithm. When the similarity between the current marketing scenario and the historical inventory management scenario reaches the 0.85 threshold, the degradation scheme for payment interface anomalies in the historical scenario is automatically loaded. During the generation of the optimization suggestion package, the topology compatibility verification report includes the input parameter type matching degree detection results. For example, an alarm is triggered when it is detected that the output parameter dimension of the alternative group differs from that of the original capability group.

[0100] This application further proposes to construct a capability utility evaluation matrix in the business middle platform, count the total number of times each atomic capability in the atomic capability library is called in historical process instances, calculate the average call success rate and average response latency of each atomic capability, calculate the capability utility score based on the average call success rate and average response latency, dynamically sort the atomic capabilities in the atomic capability library according to the capability utility score, convert the sorting result into the priority weight coefficient of the capability matching engine, and prioritize the atomic capabilities with high weight coefficients during the capability matching process.

[0101] The capability utility evaluation matrix can be constructed by establishing a multi-dimensional data table. This table records the unique identifier of each atomic capability, the total number of calls, the success rate statistical period, and the response latency sampling window. A time decay factor can be set for the total number of calls; for example, call data from the most recent 30 days has a higher weight than historical data. The average call success rate can be calculated using a sliding window algorithm, taking the percentage of successful calls from the most recent 50 calls. The response latency calculation can exclude outliers; for example, only data falling within the 3σ range of a normal distribution can be counted.

[0102] The calculation of capability utility scores can incorporate a weighted summation model, for example, setting a weight coefficient of 0.7 for call success rate and 0.3 for response latency, and normalizing the latency data to a value between 0 and 1. Dynamic sorting can be implemented through a scheduled task, such as recalculating and updating the sorted list every 5 minutes. The conversion of priority weight coefficients can use a linear mapping algorithm to map the score values ​​to integer weight values ​​between 1 and 10.

[0103] In some embodiments, an atomic capability is marked as a stable candidate capability when the total number of calls exceeds 100. The average call success rate is calculated by the ratio of the number of successful responses to the total number of calls within a statistical period. For example, if a capability is successful 180 times in the last 200 calls, the success rate is 90%. The response latency is calculated using a moving average method, for example, taking the average latency of the last 20 calls. The scoring model multiplies the success rate by a weighting coefficient of 0.7 and the reciprocal of the latency by a weighting coefficient of 0.3, and then sums them. If a capability has a success rate of 90% and a latency of 200ms, the latency score is 1 / (200 / 1000) = 5, and the total score is 0.9 × 0.7 + 5 × 0.3 = 2.13.

[0104] The dynamic sorting module generates a priority list of capabilities by sorting the scores in descending order. When the matching engine receives a scene feature identifier, it prioritizes matching the top 20% of atomic capabilities in the list. Weighting coefficients are stored in a hash table and used as weighting factors in semantic similarity calculations; for example, high-weight capabilities automatically have their similarity scores increased by 10%. Through this mechanism, capabilities with a historical call success rate below 80% or an average latency exceeding 300ms are automatically downgraded, while high-efficiency capabilities receive a higher selection probability during workflow orchestration, thus forming a closed-loop optimization.

[0105] This application further proposes to construct a capability utility evaluation matrix in the business middle platform, count the total number of times each atomic capability in the atomic capability library is called in historical process instances, calculate the average call success rate and average response latency of each atomic capability, calculate the capability utility score based on the average call success rate and average response latency, dynamically sort the atomic capabilities in the atomic capability library according to the capability utility score, convert the sorting result into the priority weight coefficient of the capability matching engine, and prioritize the atomic capabilities with high weight coefficients during the capability matching process.

[0106] When constructing a capability utility evaluation matrix, total number of calls, average call success rate, and average response latency can be defined as evaluation dimensions. For example, the total number of calls can be set as the cumulative value of the last 30 days, and the average call success rate can be calculated by the ratio of the number of successful calls to the total number of calls.

[0107] When calculating the capability utility score, a weighted summation method can be used. The success rate weight can be set to 0.6, and the response latency weight to 0.4. The response latency value can be converted into a positive indicator using its reciprocal. A timed update mechanism can be set during dynamic sorting, such as recalculating the score and updating the sorted list every hour. When converting the sorting results into weight coefficients, a linear mapping method can be used. For example, the top 10% of atomic capabilities can be assigned the highest weight value of 1.0, and the weight value decreases by 0.1 for every subsequent 10% ranking interval. This scheme works in conjunction with the process optimization instruction update mechanism. When the quality monitoring module detects anomalies in atomic capabilities, the dynamic update of the capability utility score can accelerate the elimination of inefficient capabilities.

[0108] As a preferred embodiment, when creating a capability utility evaluation matrix in the business middle platform, a database table structure containing atomic capability identifiers, call counts, success counts, and cumulative response times is first established.

[0109] Whenever an atomic capability is invoked, the invocation probe writes the execution result to a database table. The number of invocations is incrementally updated using a counter, the success rate is calculated as the ratio of successful invocations to the total number of invocations, and the average response latency is obtained by dividing the cumulative response time by the number of invocations. The capability utility score uses a weighted summation algorithm, adding the success rate multiplied by a first weighting coefficient and the reciprocal of the response latency multiplied by a second weighting coefficient. The reciprocal of the response latency is standardized to eliminate dimensional differences. The scoring calculation module triggers a calculation task at preset intervals, generating a list of atomic capabilities sorted in descending order of score, mapping the scores to normalized weighting coefficients between 0 and 1, and writing them to the capability matching engine's configuration center.

[0110] When the capability matching engine receives a matching request, it prioritizes selecting candidate capabilities from the atomic capability library whose weight coefficient is higher than a preset threshold. If there are multiple candidate capabilities, a priority queue is generated according to the weight coefficient and the capabilities are called accordingly.

[0111] Through the above technical solution, this application effectively solves the problem of low execution efficiency of process instances caused by differences in atomic capability performance. By establishing a dynamic optimization mechanism through quantitative evaluation of historical call data, high-reliability and low-latency atomic capabilities are given higher call priority, thereby reducing the risk of abnormal interruption caused by inefficient capabilities during process execution and improving the overall execution stability and resource utilization of digital marketing processes.

[0112] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. The above description is merely an explanation of some preferred embodiments of the present invention and the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by specific combinations of the above-mentioned technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, technical solutions formed by mutually substituting the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A digital marketing method for electricity based on a business middle platform, characterized in that, Includes the following steps: Identify electricity marketing business scenarios and generate scenario feature identifiers; Based on the scenario feature identifier, invoke the capability matching engine of the business middle platform; The capability matching engine is used to match the target atomic capability set in the atomic capability library of the business middle platform. Based on the target atomic capability set, digital marketing process instances are dynamically orchestrated and generated. Execute the aforementioned digital marketing process instance and collect process operation data in real time; The process execution data is input into the quality monitoring module of the business middle platform for anomaly detection. Based on the anomaly detection results, a process optimization instruction is triggered and sent to the capability matching engine; The atomic capability matching strategy is updated based on the process optimization instructions, and the matching operation is re-executed.

2. The method according to claim 1, characterized in that, The identified electricity marketing business scenarios include: Receive service request data packets initiated by user terminals; Verify the protocol validity and data integrity of the service request data packet; Parse the verified business request data packets to extract the business type code and user identity identifier; Query the historical database of the business middle platform to obtain historical behavior trajectory data associated with user identity identifiers; Input the business type code, user identity identifier, and historical behavior trajectory data into the feature fusion processor; Multi-dimensional scene feature identifiers are generated through a feature fusion processor; The scene feature identifiers are normalized and timestamps are added.

3. The method according to claim 1, characterized in that, The set of matching target atomic capabilities includes: Read the capability description text and capability dependency table of all atomic capabilities from the atomic capability library; Convert the capability description text into a standardized capability description vector; Calculate the semantic similarity between the feature vector of the scene feature identifier and the semantic similarity between the feature vectors of each capability description; A preliminary candidate set is formed by filtering atomic abilities whose semantic similarity reaches the matching threshold; Verify the call compatibility of each atomic capability in the primary candidate set based on the capability dependency table; Remove atomic capabilities that have conflicting calls or missing dependencies; The verified atomic capabilities are packaged into a target atomic capability set and a capability list is generated.

4. The method according to claim 1, characterized in that, Examples of dynamically orchestrated digital marketing processes include: Parse the input and output parameter formats for each atomic capability in the target atomic capability set; Load the basic process templates from the process orchestration rule library of the business middle platform; Select the appropriate basic process template based on the compatibility of the input and output parameter formats; Inject the atomic capabilities from the target atomic capability set into the node slots of the basic process template in the order of execution; Generate a process instance configuration file and assign a unique version identifier; Perform logical correctness verification on the process instance configuration file; After verification, an executable digital marketing process instance is generated.

5. The method according to claim 1, characterized in that, The real-time data acquisition process includes: Deploy data collection probes at each execution node of the digital marketing process instance; The start and end times of atomic capability calls are recorded using data acquisition probes. Capture parameter passing data and exception event code during the execution of atomic capabilities; Combine the call time difference, parameter passing data, and exception event codes into process execution data units; Serialize the process execution data units into process execution data blocks according to the execution order; Associate the process instance version identifier with the process execution data block.

6. The method according to claim 1, characterized in that, The trigger process optimization instructions include: When the quality monitoring module detects that the same abnormal event code appears consecutively in the process execution data block, it generates an abnormal warning signal. Extract the process instance version identifier corresponding to the abnormal warning signal; Retrieve historical process execution data blocks based on the process instance version identifier; Analyze the success rate of atomic capability calls in historical process execution data blocks; Locate faulty atomic capability nodes with a call success rate lower than the preset standard; Generate process optimization instructions that include identifiers of faulty atomic capability nodes; Send the process optimization instructions to the instruction receiving port of the capability matching engine.

7. The method according to claim 6, characterized in that, The updated atomic capability matching strategy includes: Receive process optimization instructions and parse faulty atomic capability node identifiers and fault type codes; The resource occupancy rate curve of the fault node is extracted from the real-time operation monitoring dashboard of the associated quality monitoring module; Construct an atomic capability health assessment model and set comprehensive assessment indicators including call success rate, response latency, and resource consumption rate; When the overall health score falls below the survival threshold, a forced downgrade procedure is activated. Create a multi-level degradation routing policy: Level 1 Degradation: Retrieve functionally equivalent spare atomic capabilities from the atomic capability library; Second-level degradation: break down the faulty atomic capability into multiple sub-capabilities and reassemble the call chain; Level 3 degradation: Call the cross-domain capability gateway of the business middle platform to obtain external alternative capabilities; Implement grayscale traffic switching: Set the initial traffic allocation ratio for degraded routes; Migrate request traffic to degraded capabilities according to a preset incremental ratio; Real-time monitoring of the operational health indicators of degradation capabilities; When the degradation capability continues to meet the health standard within a preset time, the original faulty capability is frozen. Update the fallback routing policy library for the capability matching engine.

8. The method according to claim 1, characterized in that, Also includes: Deploy a process knowledge graph construction module with a causal reasoning engine in the business middle platform; Import process execution data blocks into the data cleaning pipeline; Extracting the fault propagation chain using reverse causal analysis techniques: Identify the first trigger node of the exception event code; Trace the capability call path dependencies upstream of this node; All associated atomic capabilities in the marked path are potential sources of failure; Constructing a multi-dimensional process knowledge graph: Use atomic capabilities as entity nodes and attach a historical call success rate attribute; The order of ability calls is used as the relation edge, and the frequency attribute is passed as an additional parameter. Achieving cross-scenario knowledge reuse through a scenario transfer learner: Compare the similarity matrix between the current and historical scene feature identifiers; When the similarity exceeds the migration threshold, historical optimization schemes are pre-filled. Generate an optimization suggestion package with confidence ratings: Locate the atomic capability group that needs to be replaced; Search for alternative groups with high capability utility scores in the atomic capability library; Generate a topology compatibility verification report for the alternative group; The optimization suggestion package is pushed to the strategy configuration interface of the capability matching engine.

9. The method according to claim 1, characterized in that, Also includes: Before executing a business process instance, the security policy engine of the business middleware is invoked. Obtain the security access permissions for each atomic capability in the target atomic capability set; Compare the degree of match between the user's identity and the security access permission requirements; When unauthorized access to an atomic capability is detected, the execution of the digital marketing process instance is aborted. Generate permission violation logs and return security alert information to the user's terminal; Update the blacklist rule base of the security policy engine.

10. The method according to claim 1, characterized in that, Also includes: Build a capability effectiveness evaluation matrix in the business middle platform; Count the total number of times each atomic capability in the atomic capability library is called in historical process instances; Calculate the average call success rate and average response latency for each atomic capability; A capability utility score is calculated based on average call success rate and average response latency. The atomic capabilities in the atomic capability library are dynamically sorted according to their capability utility score; Convert the sorting results into priority weight coefficients for the capability matching engine; During the capability matching process, atomic capabilities with high weighting coefficients are given priority.

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