Rpa and blockchain-based psychological crisis intervention follow-up whole-process storage and auditing method and system
By using RPA and blockchain technology, the entire process of psychological crisis intervention follow-up is recorded and audited, solving the problems of verifiability and auditability of follow-up execution, ensuring the standardization and traceability of the follow-up path, and is applicable to grassroots psychological crisis intervention management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGDONG JIUYUE TECHNOLOGY CO LTD
- Filing Date
- 2026-03-20
- Publication Date
- 2026-06-19
Smart Images
Figure CN122245638A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of artificial intelligence, and in particular relates to a method and system for evidence storage and auditing of the entire process of psychological crisis intervention and follow-up based on RPA and blockchain. Background Technology
[0002] In the grassroots management of individuals with severe mental disorders and psychological crises, follow-up intervention is a crucial means of implementing risk control and preventing emergencies. The standardization and supervisory nature of follow-up work directly relate to public safety and the fulfillment of medical responsibilities. In current practice, grassroots medical institutions typically rely on provincial mental health information platforms for follow-up management. The generation, execution, and reporting of follow-up tasks largely depend on manual processes. Follow-up personnel arrange the follow-up time and method based on the patient list and risk levels provided by the platform, and manually enter a large amount of information into the system after the follow-up. Due to the significant differences in risk among follow-up subjects, the high frequency of follow-ups, the complexity of execution scenarios, and the fact that many follow-up operations occur offline or in weak network environments, the follow-up execution process is highly dependent on personal experience. There is a lack of technical mechanisms at the system level to constrain the execution sequence, key steps, and completion conditions, which easily leads to inconsistent execution paths, omissions of necessary steps, or retroactive data entry, ultimately resulting in the inability to achieve verifiable and auditable follow-up execution. Therefore, how to achieve verifiable and auditable follow-up execution has become an urgent technical problem to be solved. Summary of the Invention
[0003] The purpose of this invention is to provide a method and system for evidence storage and auditing of the entire process of psychological crisis intervention follow-up based on RPA and blockchain, which can achieve evidence storage and auditability of follow-up execution.
[0004] To achieve the above objectives, the first aspect of this invention provides a method for full-process evidence storage and auditing of psychological crisis intervention follow-up based on RPA and blockchain, the method comprising: Obtain the follow-up task and the corresponding risk classification identifier, and select the corresponding standard template from the preset psychological crisis follow-up standard template library according to the risk classification identifier; wherein, each standard template includes a set of candidate step nodes and a corresponding sequential constraint relationship, and the set of candidate step nodes includes candidate step nodes; The minimum necessary step selection result is calculated based on the standard template and the risk classification identifier; wherein, the candidate step node set, the sequence constraint relationship and the minimum necessary step selection result constitute a follow-up execution path instance; Obtain the execution information of the candidate step nodes, and generate node execution records based on the execution information; wherein, all the node execution records constitute a node execution record set; A list of node entries is constructed based on the set of node execution records, and a path-level coverage fingerprint is calculated based on the set of node execution records and the follow-up execution path instance; A path-level execution proof object is constructed based on the list of node entries and the path-level overlay fingerprint; wherein, the path-level execution proof object is used for evidence storage and audit review.
[0005] Furthermore, after constructing the path-level execution proof object based on the list of node entries and the path-level coverage fingerprint, the method further includes: The required step mask is obtained based on the selection result of the minimum required steps. Based on the required step mask and the path-level execution proof object, a position-level constraint check is performed to obtain the audit judgment result.
[0006] Furthermore, after performing position-level constraint checks based on the required step mask and the path-level execution proof object to obtain the audit judgment result, the method further includes: If the audit judgment result indicates that the follow-up has met the standard requirements, then a storage payload is generated based on the follow-up execution path instance and the path-level execution proof object, and the storage payload is submitted to the preset blockchain storage module. If the audit result indicates that the follow-up did not meet the standard requirements, then the follow-up execution path instance and the path-level execution proof object will be recorded as subsequent processing status.
[0007] Further, the step of performing a positional constraint check based on the required step mask and the path-level execution proof object to obtain an audit judgment result includes: Perform a positional AND operation on the required step mask and the path-level overlay fingerprint in the path-level execution proof object to obtain the AND operation result. The audit determination result is obtained by determining whether the result of the AND operation is the same as the mask of the required steps based on the indicator function.
[0008] Furthermore, the calculation of the minimum necessary steps selection result based on the specification template and the risk classification identifier includes: The minimum compliance requirement vector is selected from the preset level mapping table based on the risk classification identifier. Based on the aforementioned specification template, the selection vector, availability vector, and coverage relation matrix are obtained. The minimum required step selection result is calculated based on the minimum compliance requirement vector, the selection vector, the availability vector, the coverage relation matrix, and the preset trade-off parameters.
[0009] Further, the step of calculating the path-level coverage fingerprint based on the node execution record set and the follow-up execution path instance includes: The node number and required marker of the candidate step node are obtained based on the follow-up execution path instance; The execution marker of the candidate step node is obtained based on the set of node execution records; The path-level coverage fingerprint is calculated based on the node number, the required marker, and the execution marker.
[0010] Further, calculating the path-level coverage fingerprint based on the node sequence number, the required marker, and the execution marker includes: For each candidate step node, the node index raised to a preset number is multiplied by the corresponding required flag and execution flag to obtain the first data corresponding to the candidate step node; The first data corresponding to all the candidate step nodes are summed to obtain the path-level coverage fingerprint.
[0011] In a second aspect, this invention provides a system for end-to-end evidence storage and auditing of psychological crisis intervention follow-up based on RPA and blockchain, the system comprising: The acquisition unit is used to acquire follow-up tasks and corresponding risk classification identifiers, and select corresponding standard templates from a preset psychological crisis follow-up standard template library according to the risk classification identifiers; wherein, each standard template includes a set of candidate step nodes and a corresponding sequential constraint relationship, and the set of candidate step nodes includes candidate step nodes; The first calculation unit is used to calculate the minimum necessary step selection result based on the specification template and the risk classification identifier; wherein, the candidate step node set, the sequence constraint relationship and the minimum necessary step selection result constitute a follow-up execution path instance; A construction unit is used to obtain the execution information of the candidate step nodes and generate node execution records based on the execution information; wherein, all the node execution records constitute a node execution record set; The second calculation unit is used to construct a list of node entries based on the set of node execution records, and to calculate a path-level coverage fingerprint based on the set of node execution records and the follow-up execution path instance; A construction unit is used to construct a path-level execution proof object based on the node entry list and the path-level overlay fingerprint; wherein the path-level execution proof object is used for evidence storage and audit review.
[0012] In a third aspect of the invention, an electronic device is provided, the electronic device including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the method described in the first aspect above.
[0013] In a fourth aspect of the invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.
[0014] The beneficial technical effects of the present invention are at least as follows: To address the aforementioned issues, this invention provides a method and system for end-to-end evidence storage and auditing of psychological crisis intervention follow-up based on RPA and blockchain. Its core lies in proposing a full-process technical solution centered on the "provable completion" of follow-up, starting from the follow-up execution process itself. This transforms the follow-up specifications, which originally relied on manual understanding and post-event checks, into an execution structure that can be identified, constrained, and verified by the system, making the follow-up execution itself a process that can be technically proven and audited. This solution solidifies the specification requirements into follow-up execution path instances before the follow-up begins and moves these path instances forward as execution permission conditions during the follow-up execution phase. This ensures that the follow-up personnel's actual operations can only proceed along the specification path, guaranteeing the compliance of the execution sequence and key links from the source. Furthermore, the node-level execution facts formed during the follow-up execution process are further integrated into path-level execution proof objects. Through unified encoding of the necessary structure of the specifications and the actual execution results, a holistic determination is achieved as to whether the follow-up covers all necessary steps. This technical approach avoids the repetitive collection or centralized disclosure of follow-up content itself. Instead, it verifies authenticity through an abstract representation of the execution structure and results, freeing follow-up audits from reliance on manual judgment or fragmented evidence. Furthermore, without altering the existing provincial platform's operational model, the system can directly use audit results as triggers for subsequent evidence preservation and result reporting. It only enters the subsequent processing flow when the follow-up reaches a verifiable completion status, thus significantly improving the auditability, credibility, and traceability of follow-up execution while ensuring low cost and ease of deployment. Through the above technical solution, this invention provides a feasible, end-to-end evidence preservation and auditing implementation path for psychological crisis intervention follow-ups, tailored to the actual conditions at the grassroots level. Attached Figure Description
[0015] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.
[0016] Figure 1 This is a flowchart of the whole process of evidence storage and auditing for psychological crisis intervention and follow-up based on RPA and blockchain provided in the embodiments of this application.
[0017] Figure 2This is a flowchart of a method for evidence storage and auditing of the entire process of psychological crisis intervention follow-up based on RPA and blockchain, provided in another embodiment of this application.
[0018] Figure 3 This is a schematic diagram of the structure of the psychological crisis intervention follow-up full-process evidence storage and auditing system based on RPA and blockchain provided in the embodiments of this application. Detailed Implementation
[0019] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.
[0020] Please refer to Figure 1 , Figure 1 This is a flowchart of the whole process of evidence storage and auditing for psychological crisis intervention and follow-up based on RPA and blockchain provided in the embodiments of this application, namely Robotic Process Automation (RPA). Figure 1 The method may include, but is not limited to, steps S101 to S105.
[0021] Step S101: Obtain the follow-up task and the corresponding risk classification identifier, and select the corresponding standard template from the preset psychological crisis follow-up standard template library according to the risk classification identifier; wherein, each standard template includes a set of candidate step nodes and the corresponding sequential constraint relationship, and the set of candidate step nodes includes candidate step nodes. Step S102: Calculate the minimum necessary step selection result based on the standard template and risk classification identifier; wherein, the candidate step node set, the sequence constraint relationship and the minimum necessary step selection result constitute the follow-up execution path instance; Step S103: Obtain the execution information of the candidate step nodes, and generate node execution records based on the execution information; wherein, all node execution records constitute a node execution record set; Step S104: Construct a list of node entries based on the set of node execution records, and calculate a path-level coverage fingerprint based on the set of node execution records and the follow-up execution path instances; Step S105: Construct a path-level execution proof object based on the node entry list and path-level overlay fingerprint; wherein, the path-level execution proof object is used for evidence storage and audit review.
[0022] In steps S101 to S102 of some embodiments, the goal is to transform the requirements of the psychological crisis intervention follow-up guidelines, which exist in textual and institutional form, into a follow-up execution path instance that can be directly executed for a specific follow-up task before the follow-up begins. The follow-up execution path instance clarifies which steps must be completed in the context of the guidelines for this follow-up, and the sequential relationship between these steps, thereby providing a unified and unambiguous basis for subsequent execution control, execution record generation, and completion auditing. Unlike simply copying the entire template, this step emphasizes minimizing the number of steps selected for the follow-up execution path instance while meeting the guidelines, to avoid unnecessary data processing and execution burdens in subsequent steps.
[0023] Specifically, at the system implementation level, the management backend pre-maintains a library of psychological crisis follow-up guidelines templates. This library includes multiple templates, each corresponding to a risk level identifier. Each template is broken down into several candidate step nodes, each with a fixed node number and a corresponding set of guidelines. For example, some nodes fulfill "identity verification requirements," others "risk status confirmation requirements," and still others serve only as supplementary explanations and do not directly correspond to mandatory requirements. The templates also configure sequential constraints between candidate step nodes, specifying which steps must be completed before others. All template configurations are completed during system deployment or maintenance and do not involve dynamic adjustments to the template structure during the follow-up process.
[0024] When the system receives a specific follow-up task and its corresponding risk classification identifier, it selects a uniquely matching standard template from the psychological crisis follow-up standard template library based on the risk classification identifier. Subsequently, it generates a dedicated follow-up execution path instance based on this standard template. To ensure that the follow-up execution path instance meets the standard requirements, a "minimum necessary step selection result" is calculated from the candidate step node set. This involves filtering the minimum compliance requirement vector from a preset level mapping table based on the risk classification identifier; obtaining the selection vector, availability vector, and coverage relation matrix based on the standard template; and calculating the minimum necessary step selection result based on the minimum compliance requirement vector, selection vector, availability vector, coverage relation matrix, and preset trade-off parameters. This calculation is expressed in discrete optimization form as follows: ; in, This represents the selection vector for candidate step nodes during this follow-up. Each bit corresponds to a candidate step node. A value of 1 indicates that the candidate step node has been selected as a necessary step in this follow-up, and a value of 0 indicates that the candidate step node has not been selected into the set of necessary steps. This represents the availability vector of candidate step nodes in the specification template. This vector originates from the specification template configuration. When a candidate step node is allowed to be used under this risk level, the corresponding bit is set to 1; otherwise, it is set to 0. (Constraint) This is used to prevent the system from selecting candidate step nodes that are not enabled in the specification template. This represents the coverage relationship matrix between candidate step nodes and specification requirements. This matrix is determined during the specification template configuration phase. When a candidate step node can meet a certain specification requirement, the corresponding element in the matrix takes the value of 1, otherwise it takes the value of 0. This represents the minimum compliance requirement vector for this follow-up under this risk level. This vector is mapped from the risk level identifier and is used to indicate which regulatory requirements must be met. To balance the relationship between "reducing the number of steps" and "meeting specification requirements", the value of the parameter is determined by the system parameter configuration. This indicates the number of nodes selected as a required step. This indicates the extent to which regulatory requirements are not covered. By minimizing the combination of both, we obtain the minimum necessary steps that satisfy the minimum compliance requirements while minimizing the number of steps. .
[0025] After completing the above calculations, The results are written into the follow-up execution path instance. Candidate step nodes with a value of 1 are marked as required nodes, while the remaining nodes are retained in the follow-up execution path instance as non-required nodes but do not participate in the completion judgment. Simultaneously, the order constraints configured in the specification template are completely copied into the follow-up execution path instance for subsequent restriction of execution order. To support subsequent auditing and traceability, this step also embeds the specification template version identifier and path instance identifier into the follow-up execution path instance, ensuring that any subsequent execution record or audit result can be clearly associated with the specification template version on which the follow-up execution path instance was based. Ultimately, the follow-up execution path instance output by this step contains at least three parts: first, the set of candidate step nodes and their order constraints; second, the... The identified required step node marker results are displayed as a required step mask in the follow-up execution path instance. The first is to solidify the form and use it as a structural constraint for subsequent audit decisions; the second is to use it as a path instance identifier to distinguish different follow-up tasks.
[0026] In step S103 of some embodiments, this step is carried out during the follow-up execution phase. Its core function is to implement the step structure, sequence constraints, and necessary step markers defined in the follow-up execution path instance generated and solidified in the previous step into the actual operation process at the follow-up site, and generate node execution records at the same time as execution occurs. This step transforms the "how it should be executed" path structure formed in the above steps into an operating mechanism of "how the system allows execution", and is the sole fact entry point for all subsequent execution facts, path-level proofs, and audit judgments.
[0027] At the start of the follow-up visit, the offline follow-up terminal loads the follow-up execution path instance and its path instance identifier bound to the current task, and constructs a path execution status table on the terminal side. The path execution status table uses the fixed node sequence number in the path instance as an index, combined with the predecessor-successor relationship between nodes, to identify whether each candidate step node currently has the conditions for execution. For any node, the terminal will only mark the node as executable and display it on the interface if all its corresponding predecessor nodes in the path instance have been completed, and the node is allowed to execute in the path instance. This ensures that the follow-up execution process strictly follows the sequential structure defined in the path instance at the operational level. When the follow-up personnel complete a specific step on-site and confirm the completion of the step through the terminal interface, the terminal system immediately generates a node execution record locally. This node execution record includes at least the path instance identifier and the candidate step node identifier, indicating that the corresponding node under this path instance has undergone execution. The generation of node execution records is automatically completed by the terminal system, corresponding one-to-one with the on-site operations of the follow-up personnel, and is immediately written into the path execution status table after generation, marking the node as completed. Subsequently, the terminal recalculates the executable status of successor nodes based on the updated path execution status table, ensuring that steps in the path instance that are preceded by that node enter the executable state when the conditions are met. In one example, nodes 1 and 2 have already been executed and generated corresponding node execution records; therefore, nodes 1 and 2 are marked as completed in the path execution status table. Node 3 is marked as executable in the path execution status table.
[0028] In the conditions for generating node execution records, the necessary step markers determined by step S102 in the path instance are directly embedded as part of the execution gating rules. For nodes marked as necessary steps, their node execution records participate in subsequent completion determination; for nodes not marked as necessary, their node execution records are mainly used to reflect the actual execution process and support audit documentation, but their execution results are not considered necessary conditions in subsequent completion determination. In this way, necessity constraints and sequence constraints do not exist in the form of additional calculation results, but are solidified as preconditions for "whether node execution records can be generated".
[0029] As the follow-up process progresses, the terminal continuously generates node execution records and updates the path execution status table synchronously. Follow-up personnel consistently complete the follow-up task along the set of steps and sequential trajectory defined by the path instance. It's important to note that the path execution status table represents the running status of a node at the current execution moment, used to determine whether a node is executable and completed, thus supporting sequential control on the terminal side. Node execution records indicate that the execution behavior of a certain node has actually occurred, serving as a record of this execution fact. Subsequent path-level execution proof objects and auditing processes utilize node execution records. All node execution records are aggregated into a complete set, where each record corresponds one-to-one with the path instance identifier and specific step node. Its existence directly reflects the actual execution fact of that node in this follow-up. Through this step, the structural constraints defined in the follow-up execution path instance are moved forward to the execution stage. Only when the path structure and execution behavior are satisfied can a node execution record be formed, ensuring that all execution facts processed in subsequent steps originate from the actual execution process on the compliant path. This provides a stable, reliable, and directly usable foundation for constructing path-level execution proof objects.
[0030] In steps S104 to S105 of some embodiments, the path execution status table generated in the previous step is transformed into a path-level execution proof object that can be directly used for subsequent auditing and evidence preservation. The path execution status table reflects the execution facts at the "node" granularity, while the follow-up execution path instance defines the structure and order to be executed at the "path" granularity. This step uses the path instance identifier as the primary key, aligns the two in the same structural coordinate system, embeds the execution facts into the path structure, and thus forms a single object expressing "the true progress result of this psychological crisis follow-up on the standardized path". This object can be directly used for completion determination and reporting triggering in subsequent steps. Therefore, the processing result of this step must not only maintain computability but also facilitate long-term retention and audit review.
[0031] In terms of processing flow, firstly, the follow-up execution path instances are loaded using the path instance identifier as an index, and the set of nodes in the path instance is constructed into an ordered sequence view to express the positional relationship of nodes in the path, facilitating the subsequent generation of path-level execution proof objects node by node. Simultaneously, an index mapping is established for the set of node execution records according to the step node identifier, thereby determining whether any node has a corresponding execution record in constant time. Next, the background process iterates through the ordered sequence view node by node and generates node entries for path-level execution proof objects sequentially according to the index mapping, resulting in a list of node entries. Each node entry contains at least a node identifier, a node sequence number, and a node completion flag. The node completion flag is uniquely derived from the set of node execution records; the node's completion flag is valid if and only if an execution record corresponding to that node exists in the set of node execution records. The node identifier uniquely identifies a specific node, and the node sequence number indicates the node's position order in the current path instance. The former addresses the question of "which node it is," while the latter addresses the question of "which position this node is in." Since the node execution record generation process is already subject to sequential constraints on the terminal side, this step further solidifies this "sequential consistency" into a structural attribute within the path-level object, so that subsequent auditing only needs to read the path-level object to obtain the overall outline of the path progression.
[0032] To ensure that the path-level execution proof object possesses both "auditability" and "compressibility," this step constructs a path-level coverage fingerprint within the object to express the coverage of required nodes in a compact form. This coverage fingerprint is not a simple count; instead, it encodes required node markers and execution facts together, maintaining a binding relationship with node indices to prevent the same batch of node records from generating identical fingerprints in different orders. The node indices and required markers of candidate step nodes are obtained based on follow-up execution path instances; the execution markers of candidate step nodes are obtained based on node execution records; and the path-level coverage fingerprint is calculated based on the node indices, required markers, and execution markers. Specifically, for each candidate step node, the node indices are raised to a preset power and multiplied by the corresponding required marker and execution marker to obtain the first data corresponding to the candidate step node; the first data corresponding to all candidate step nodes are summed to obtain the path-level coverage fingerprint. As shown in the following formula: ; in, This indicates a path-level overlay fingerprint; This represents the set of all nodes defined in the execution path instance. Indicates candidate step nodes The node sequence number is pre-fixed in the follow-up execution path instance and used as a unique index for the node position; Represents a node The required markers in the follow-up execution path instance are derived from And stored within the path instance; Represents a node The execution fact flag (completed flag) is set to 1 when the execution record of the corresponding node exists in the node execution record set. This formula ensures that a node contributes to the fingerprint only when it is marked as required and its execution record exists, and the contribution is bound to the node's position in the path. In practice, the backend calculates this synchronously while traversing the path nodes. and will Write it into the path-level execution proof object, which serves as the unique path-level summary representation in the path-level execution proof object, and is used for subsequent audit judgment, evidence indexing, and processing action triggering.
[0033] After the path-level execution proof object is formed, the backend binds and stores it with the path instance identifier, which can be used for evidence preservation and audit review. The path-level execution proof object simultaneously contains a list of node entries and a path-level overlay fingerprint. The node entry list is used to locate specific missing nodes and their locations during auditing; path-level overlay fingerprints. Used to carry the execution facts of the node execution record set in the path-level execution proof object, enabling the node-level completion state. This step results in a unified encapsulation into a path-level overlay fingerprint, which serves as the sole source of execution facts for subsequent auditing and processing. For example, in a psychological crisis follow-up, several essential nodes in the path instance are marked as... If the execution record set of a node is missing the execution record of a required node, then the corresponding node... If the value is 0, the node covers the fingerprint at the path level. This will not generate a contribution at the corresponding position, and the node will be marked as incomplete in the node entry list, thus allowing subsequent steps to proceed by reading... It can quickly identify coverage gaps and accurately locate missing essential nodes through the list of entries.
[0034] Steps S101 to S105 as illustrated in this embodiment involve obtaining the follow-up task and its corresponding risk classification identifier, and selecting a corresponding standard template from a pre-set psychological crisis follow-up standard template library based on the risk classification identifier. Each standard template includes a set of candidate step nodes and a corresponding sequential constraint relationship; the set of candidate step nodes includes candidate step nodes. The minimum necessary step selection result is calculated based on the standard template and the risk classification identifier. The set of candidate step nodes, the sequential constraint relationship, and the minimum necessary step selection result constitute a follow-up execution path instance. Execution information of the candidate step nodes is obtained, and node execution records are generated based on the execution information; wherein, all node execution records constitute a node execution record set. A node entry list is constructed based on the node execution record set, and a path-level coverage fingerprint is calculated based on the node execution record set and the follow-up execution path instance. A path-level execution proof object is constructed based on the node entry list and the path-level coverage fingerprint. The path-level execution proof object is used for evidence preservation and audit review, realizing the verifiability and auditability of the follow-up execution.
[0035] Please refer to Figure 2 In some embodiments, after step S105, the method for evidence storage and auditing of the entire process of psychological crisis intervention follow-up based on RPA and blockchain may also include, but is not limited to, steps S201 to S202: Step S201: Obtain the required step mask based on the minimum required step selection result; Step S202: Perform position-level constraint checks based on the required step mask and path-level execution proof object to obtain the audit judgment result.
[0036] In some embodiments, steps S201 to S202 are performed by The determined required step node marking results, which are masked in the path instance. The form is fixed. The input is a path-level execution proof object, which internally contains at least a path instance identifier and a path-level overlay fingerprint. The path instance identifier is used to uniquely locate the corresponding follow-up execution path instance in the management backend, while the path-level overriding fingerprint... This is a compressed representation of the actual execution results of this follow-up, encoding the necessary step markers, node execution facts, and the positional relationships of nodes in the path into a deterministic value. By using the path instance identifier, the follow-up execution path instance is read synchronously, directly retrieving the already determined mask of necessary steps passed down with the path instance. The mask is based on the node index in the path instance. As a bit index, it is used to indicate which nodes are mandatory steps in this follow-up. Its bit encoding rules are the same as those of the path-level coverage fingerprint. Maintain consistency.
[0037] During the audit determination phase, fingerprints are covered at the path level. and required step mask As the sole criterion, a positional constraint check is performed to determine whether the actual execution result covers all the necessary steps defined in the path instance. A positional AND operation (bitwise AND) is performed on the necessary step mask and the path-level overlay fingerprint in the path-level execution proof object to obtain the AND result. The result is then compared with the required step mask based on an indicator function to obtain the audit determination result. This is illustrated in the following formula: ; in, This indicates the audit findings of this follow-up visit. When the follow-up period reaches the required standard, it indicates that the follow-up has been completed. When this occurs, it indicates that the follow-up did not meet the required standards. This represents a path-level coverage fingerprint, where each bit corresponds to a node position in the path. When the necessary steps at that position have been actually executed, the corresponding bit is in a valid coverage state. This represents the mask of required steps carried in the follow-up execution path instance, where each bit corresponds to a required node position in the path; symbol This indicates a positional AND operation, used for bit-by-bit checking. Are the positions marked as required nodes in the middle? All nodes are covered. A fingerprint is covered at the path level if and only if all required node locations are covered at the path level. When both are in the executed state, the judgment result is... The value indicates a valid completion. Specifically, if the result of the operation matches the required step mask... Same, indicating The positions marked as required nodes are in Since the middle has already been covered, the indicator function takes a value of 1. If the result of the operation matches the mask of the required steps. The difference indicates At least one of the positions marked as required is in If the value is not covered, the indicator function takes the value of 0. .
[0038] The aforementioned determination method eliminates the need for the audit process to re-traverse node execution records or recalculate the expected structure of the specification. Instead, it directly utilizes the existing necessary step structure in the path instance to perform a binding interpretation of the path-level coverage fingerprint. In actual operation, this determination can be completed in constant time in the management backend, making it suitable for centralized auditing scenarios involving large-scale psychological crisis follow-up tasks.
[0039] After obtaining the audit findings Then, the result is linked to subsequent processing actions. When When the follow-up meets the specification requirements, the path instance identifier and path-level overlay fingerprint are used. The data is used as input to generate a storage payload, which is then submitted to the blockchain storage module, creating an immutable audit credential for the follow-up visit. Simultaneously, the follow-up task identifier is associated with the path instance identifier, triggering the workflow automation tool to report the follow-up results, completing the data entry and submission according to the page structure of the provincial mental health information platform. When follow-up fails to meet the specifications, the path instance identifier and path-level overlay fingerprint will be used. This information is recorded as part of the subsequent processing status, and is used to display the execution gap location in the management backend and support subsequent handling.
[0040] In this embodiment, steps S201 to S202, based on the fixed structural constraints in the path instance, perform a path-level overlay fingerprint on the path-level execution proof object. A consistent interpretation is performed, and the necessary steps already established in the follow-up execution path instance are used to audit and determine the nature of this psychological crisis follow-up, triggering subsequent evidence preservation and reporting actions accordingly. This step does not regenerate a new path structure or path-level coverage fingerprint, but rather interprets and constrains the structural information and execution results formed in the previous steps within the same judgment framework, enabling the audit conclusions to be directly transformed into executable business results, thereby completing the closed loop of the entire solution at the actual operational level.
[0041] Please see Figure 3 This application also provides a system for evidence storage and auditing of the entire process of psychological crisis intervention follow-up based on RPA and blockchain. This system can realize the aforementioned method for evidence storage and auditing of the entire process of psychological crisis intervention follow-up based on RPA and blockchain. The system includes: The acquisition unit 301 is used to acquire the follow-up task and the corresponding risk classification identifier, and select the corresponding standard template from the preset psychological crisis follow-up standard template library according to the risk classification identifier; wherein, each standard template includes a set of candidate step nodes and a corresponding sequential constraint relationship, and the set of candidate step nodes includes candidate step nodes; The first calculation unit 302 is used to calculate the minimum necessary step selection result based on the standard template and risk classification identifier; wherein, the candidate step node set, the sequential constraint relationship and the minimum necessary step selection result constitute a follow-up execution path instance; Construction unit 303 is used to obtain the execution information of candidate step nodes and generate node execution records based on the execution information; wherein, all node execution records constitute a node execution record set; The second calculation unit 304 is used to construct a list of node entries based on the set of node execution records, and to calculate a path-level coverage fingerprint based on the set of node execution records and the follow-up execution path instance. Construction unit 305 is used to construct a path-level execution proof object based on the node entry list and path-level overlay fingerprint; wherein, the path-level execution proof object is used for evidence storage and audit review.
[0042] The specific implementation of the RPA and blockchain-based psychological crisis intervention follow-up full-process evidence storage and auditing system is basically the same as the specific implementation of the RPA and blockchain-based psychological crisis intervention follow-up full-process evidence storage and auditing method, and will not be repeated here.
[0043] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.
Claims
1. A method for end-to-end evidence storage and auditing of psychological crisis intervention follow-up based on RPA and blockchain, characterized in that: The method includes: Obtain the follow-up task and the corresponding risk classification identifier, and select the corresponding standard template from the preset psychological crisis follow-up standard template library according to the risk classification identifier; wherein, each standard template includes a set of candidate step nodes and a corresponding sequential constraint relationship, and the set of candidate step nodes includes candidate step nodes; The minimum necessary step selection result is calculated based on the standard template and the risk classification identifier; wherein, the candidate step node set, the sequence constraint relationship and the minimum necessary step selection result constitute a follow-up execution path instance; Obtain the execution information of the candidate step nodes, and generate node execution records based on the execution information; wherein, all the node execution records constitute a node execution record set; A list of node entries is constructed based on the set of node execution records, and a path-level coverage fingerprint is calculated based on the set of node execution records and the follow-up execution path instance; A path-level execution proof object is constructed based on the list of node entries and the path-level overlay fingerprint; wherein, the path-level execution proof object is used for evidence storage and audit review.
2. The method for full-process evidence storage and auditing of psychological crisis intervention follow-up based on RPA and blockchain as described in claim 1, characterized in that, After constructing the path-level execution proof object based on the list of node entries and the path-level overlay fingerprint, the method further includes: The required step mask is obtained based on the selection result of the minimum required steps. Based on the required step mask and the path-level execution proof object, a position-level constraint check is performed to obtain the audit judgment result.
3. The method for full-process evidence storage and auditing of psychological crisis intervention follow-up based on RPA and blockchain as described in claim 2, characterized in that, After performing a position-level constraint check based on the required step mask and the path-level execution proof object to obtain the audit judgment result, the method further includes: If the audit judgment result indicates that the follow-up has met the standard requirements, then a storage payload is generated based on the follow-up execution path instance and the path-level execution proof object, and the storage payload is submitted to the preset blockchain storage module. If the audit result indicates that the follow-up did not meet the standard requirements, then the follow-up execution path instance and the path-level execution proof object will be recorded as subsequent processing status.
4. The method for full-process evidence storage and auditing of psychological crisis intervention follow-up based on RPA and blockchain as described in claim 2, characterized in that, The step of performing a position-level constraint check based on the required step mask and the path-level execution proof object to obtain an audit judgment result includes: Perform a positional AND operation on the required step mask and the path-level overlay fingerprint in the path-level execution proof object to obtain the AND operation result. The audit determination result is obtained by determining whether the result of the AND operation is the same as the mask of the required steps based on the indicator function.
5. The method for full-process evidence storage and auditing of psychological crisis intervention follow-up based on RPA and blockchain as described in claim 1, characterized in that, The calculation of the minimum necessary steps based on the specification template and the risk classification identifier includes: The minimum compliance requirement vector is selected from the preset level mapping table based on the risk classification identifier. Based on the aforementioned specification template, the selection vector, availability vector, and coverage relation matrix are obtained. The minimum required step selection result is calculated based on the minimum compliance requirement vector, the selection vector, the availability vector, the coverage relation matrix, and the preset trade-off parameters.
6. The method for full-process evidence storage and auditing of psychological crisis intervention follow-up based on RPA and blockchain as described in claim 1, characterized in that, The step of calculating the path-level coverage fingerprint based on the node execution record set and the follow-up execution path instance includes: The node number and required marker of the candidate step node are obtained based on the follow-up execution path instance; The execution marker of the candidate step node is obtained based on the set of node execution records; The path-level coverage fingerprint is calculated based on the node number, the required marker, and the execution marker.
7. The method for full-process evidence storage and auditing of psychological crisis intervention follow-up based on RPA and blockchain as described in claim 6, characterized in that, The step of calculating the path-level coverage fingerprint based on the node sequence number, the required marker, and the execution marker includes: For each candidate step node, the node index raised to a preset number is multiplied by the corresponding required flag and execution flag to obtain the first data corresponding to the candidate step node; The first data corresponding to all the candidate step nodes are summed to obtain the path-level coverage fingerprint.
8. A psychological crisis intervention follow-up full-process evidence storage and auditing system based on RPA and blockchain, characterized in that: The system includes: The acquisition unit is used to acquire follow-up tasks and corresponding risk classification identifiers, and select corresponding standard templates from a preset psychological crisis follow-up standard template library according to the risk classification identifiers; wherein, each standard template includes a set of candidate step nodes and a corresponding sequential constraint relationship, and the set of candidate step nodes includes candidate step nodes; The first calculation unit is used to calculate the minimum necessary step selection result based on the specification template and the risk classification identifier; wherein, the candidate step node set, the sequence constraint relationship and the minimum necessary step selection result constitute a follow-up execution path instance; A construction unit is used to obtain the execution information of the candidate step nodes and generate node execution records based on the execution information; wherein, all the node execution records constitute a node execution record set; The second calculation unit is used to construct a list of node entries based on the set of node execution records, and to calculate a path-level coverage fingerprint based on the set of node execution records and the follow-up execution path instance; A construction unit is used to construct a path-level execution proof object based on the node entry list and the path-level overlay fingerprint; wherein the path-level execution proof object is used for evidence storage and audit review.
9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the method for evidence storage and auditing of the entire process of psychological crisis intervention follow-up based on RPA and blockchain as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for evidence storage and auditing of the entire process of psychological crisis intervention and follow-up based on RPA and blockchain, as described in any one of claims 1 to 7.