Method, device and equipment for processing pending task cases and storage medium

Abstract

The present application relates to the field of cloud services, and discloses a pending task case processing method, device and equipment and a storage medium, which are used for improving the processing efficiency of the process breakpoint of the pending task case. The pending task case processing method comprises the following steps: obtaining a pending task case to be processed, performing data analysis on the pending task case according to a preset abnormal state analysis strategy, identifying an original abnormal case in the pending task case, and storing the original abnormal case in a preset monitoring pool; performing data analysis on the original abnormal case in the preset monitoring pool according to the abnormal state analysis strategy, identifying a target abnormal case in the preset monitoring pool; obtaining rule information and case information of the target abnormal case; comparing and analyzing the case information and the rule information to determine a target information element required for terminating the abnormal case; and sending the target information element to a user terminal. In addition, the present application also relates to blockchain technology, and the target information element can be stored in a blockchain node.

Description

Technical Field

[0001] This invention relates to the field of status monitoring, and more particularly to a method, apparatus, device, and storage medium for handling pending cases. Background Technology

[0002] In the business case handling process, there are multiple communications and exchanges of different data and materials between the client and the business case handler. The progress of many process steps depends on the previous process step. Due to the complexity and dependency of process interactions, breakpoints in multiple processes of the same business case can seriously affect the progress and quality of the business case. Therefore, it is necessary to monitor the process breakpoints in business cases.

[0003] In existing technologies, the manual offline periodic statistics of business cases or the non-real-time data monitoring through big data calculations have the problem of not being able to effectively and timely provide feedback to customers or business case handlers in order to effectively handle the breakpoints in the business case process, resulting in low efficiency in handling the process breakpoints of pending cases. Summary of the Invention

[0004] The main objective of this invention is to solve the problem of low efficiency in handling process breakpoints in pending cases.

[0005] The first aspect of this invention provides a method for handling pending cases, comprising:

[0006] Obtain pending task cases to be processed, wherein the pending task cases are cases that are at the target process node but have not terminated the process;

[0007] The pending task cases are analyzed according to the preset abnormal state analysis strategy to identify the original abnormal cases in the pending task cases and store the original abnormal cases in the preset monitoring pool. The abnormal state analysis strategy is used to analyze whether the information of the process node and the unprocessed delay time meet the preset conditions. The preset monitoring pool is a monitoring pool with multiple different preset time periods.

[0008] The original abnormal cases in the preset monitoring pool are analyzed according to the abnormal state analysis strategy to identify the target abnormal cases in the preset monitoring pool.

[0009] Obtain the rule information and case information of the target abnormal case, wherein the rule information is the condition required to terminate the target abnormal case, and the case information is the information elements included in the target abnormal case;

[0010] The case information and the rule information are compared and analyzed to determine the target information elements required to terminate the target abnormal case;

[0011] The target information element is sent to the user terminal.

[0012] Optionally, in a first implementation of the first aspect of the present invention, the step of performing data analysis on the original abnormal cases in the preset monitoring pool according to the abnormal state analysis strategy to identify target abnormal cases in the preset monitoring pool includes:

[0013] Based on the aforementioned abnormal state analysis strategy, data analysis is performed on the original abnormal cases stored in the monitoring pool during the first preset time period to obtain candidate abnormal cases;

[0014] The candidate abnormal cases are stored in a monitoring pool for a second preset time period, wherein the start time of the second preset time period is later than the end time of the first preset time period.

[0015] According to the abnormal state analysis strategy, iterative data analysis is performed on the candidate abnormal cases in the monitoring pool during the second preset time period to obtain the target abnormal case.

[0016] Optionally, in a second implementation of the first aspect of the present invention, the step of performing data analysis on the original abnormal cases stored in the monitoring pool during a first preset time period according to the abnormal state analysis strategy to obtain candidate abnormal cases includes:

[0017] Obtain the target time and determine whether the target time is the end time of the first preset time period;

[0018] If the target time is the end time of the first preset time period, then the original abnormal cases stored in the monitoring pool of the first preset time period are analyzed according to the abnormal state analysis strategy to obtain candidate abnormal cases.

[0019] Optionally, in a third implementation of the first aspect of the present invention, the step of comparing and analyzing the case information with the rule information to determine the target information elements required to terminate the target abnormal case includes:

[0020] Create a case knowledge graph of the case information and a rule knowledge graph of the rule information, and perform a random walk on the case knowledge graph and the rule knowledge graph to obtain the corresponding case information sequence and rule information sequence;

[0021] Calculate the cosine similarity between the case information sequence and the rule information sequence to obtain a similarity value;

[0022] Information elements in case information with similarity values ​​greater than a preset threshold are identified as target information elements, wherein the target information elements are stored in a blockchain.

[0023] Optionally, in a fourth implementation of the first aspect of the present invention, obtaining the rule information and case information of the target abnormal case includes:

[0024] Based on the case status of the target abnormal case, obtain the case number and rule number of the target abnormal case;

[0025] The case number and the rule number are sent to a preset rule engine. The rule engine then traverses the preset rule tree to obtain the rule information corresponding to the rule number.

[0026] The rule engine generates a key value for the case number and retrieves the case information corresponding to the key value from a pre-set case hash table.

[0027] Optionally, in a fifth implementation of the first aspect of the present invention, sending the target information element to the user terminal includes:

[0028] A visual chart of the target information element is generated according to a preset feedback processing strategy, and the visual chart is rendered onto a preset display page;

[0029] Determine whether a case processing request based on the visual chart is received from the preset display page during the third preset time period;

[0030] If no case processing request based on the visual chart is received from the preset display page within the third preset time period, the target information element is sent to the user terminal.

[0031] Optionally, in a sixth implementation of the first aspect of the present invention, before obtaining the pending task cases to be processed, the method further includes:

[0032] Obtain the case information, case number, and case status of pre-stored business cases that are at the target process node but have not terminated the process; set the rule information and feedback processing strategy of the business cases through a pre-set rule engine, as well as the rule number of the rule information.

[0033] Create a correspondence between the case information, the case number, the case status, the rule information, the processing strategy, and the rule number, and identify the business cases with the created correspondence as pending task cases to be processed.

[0034] A second aspect of the present invention provides an apparatus for processing pending cases, comprising:

[0035] The first acquisition module is used to acquire pending task cases to be processed, wherein the pending task cases are cases that are at the target process node but have not terminated the process;

[0036] The first data analysis module is used to perform data analysis on the pending task cases according to the preset abnormal state analysis strategy, so as to identify the original abnormal cases in the pending task cases and store the original abnormal cases in the preset monitoring pool. The abnormal state analysis strategy is used to analyze whether the information of the process node and the unprocessed delay time meet the preset conditions. The preset monitoring pool is a monitoring pool with multiple different preset time periods.

[0037] The second data analysis module is used to perform data analysis on the original abnormal cases in the preset monitoring pool according to the abnormal state analysis strategy, so as to identify the target abnormal cases in the preset monitoring pool.

[0038] The second acquisition module is used to acquire the rule information and case information of the target abnormal case, wherein the rule information is the condition required to terminate the target abnormal case, and the target case information is the information elements included in the abnormal case;

[0039] The comparison and analysis module is used to compare and analyze the case information and the rule information to determine the target information elements required to terminate the target abnormal case;

[0040] The sending module is used to send the target information element to the user terminal.

[0041] Optionally, in a first implementation of the second aspect of the present invention, the second data analysis module includes:

[0042] The first data analysis unit is used to perform data analysis on the original abnormal cases stored in the monitoring pool of the first preset time period according to the abnormal state analysis strategy, and obtain candidate abnormal cases.

[0043] A storage unit is used to store the candidate abnormal cases in a monitoring pool for a second preset time period, wherein the start time of the second preset time period is later than the end time of the first preset time period.

[0044] The second data analysis unit is used to perform iterative data analysis on candidate abnormal cases in the monitoring pool during the second preset time period according to the abnormal state analysis strategy, so as to obtain the target abnormal case.

[0045] Optionally, in a second implementation of the second aspect of the present invention, the first data analysis unit is specifically used for:

[0046] Obtain the target time and determine whether the target time is the end time of the first preset time period;

[0047] If the target time is the end time of the first preset time period, then the original abnormal cases stored in the monitoring pool of the first preset time period are analyzed according to the abnormal state analysis strategy to obtain candidate abnormal cases.

[0048] Optionally, in a third implementation of the second aspect of the present invention, the comparison analysis module is specifically used for:

[0049] Create a case knowledge graph of the case information and a rule knowledge graph of the rule information, and perform a random walk on the case knowledge graph and the rule knowledge graph to obtain the corresponding case information sequence and rule information sequence;

[0050] Calculate the cosine similarity between the case information sequence and the rule information sequence to obtain a similarity value;

[0051] Information elements in case information with similarity values ​​greater than a preset threshold are identified as target information elements, wherein the target information elements are stored in a blockchain.

[0052] Optionally, in a fourth implementation of the second aspect of the present invention, the second acquisition module is specifically used for:

[0053] Based on the case status of the target abnormal case, obtain the case number and rule number of the target abnormal case;

[0054] The case number and the rule number are sent to a preset rule engine. The rule engine then traverses the preset rule tree to obtain the rule information corresponding to the rule number.

[0055] The rule engine generates a key value for the case number and retrieves the case information corresponding to the key value from a pre-set case hash table.

[0056] Optionally, in a fifth implementation of the second aspect of the present invention, the sending module includes:

[0057] The generation unit is used to generate a visual chart of the target information element according to a preset feedback processing strategy, and render the visual chart onto a preset display page;

[0058] The judgment unit is used to determine whether a case processing request based on the visualization chart is received from the preset display page during the third preset time period;

[0059] The sending unit is used to send the target information element to the user terminal when no case processing request based on the visual chart is received from the preset display page within a third preset time period.

[0060] Optionally, in a sixth implementation of the second aspect of the present invention, the apparatus for processing pending cases includes:

[0061] The setting module is used to obtain the case information, case number and case status of pre-stored business cases that are at the target process node but have not terminated the process, and to set the rule information and feedback processing strategy of the business cases through a preset rule engine, as well as the rule number of the rule information.

[0062] A creation module is used to create a correspondence between the case information, the case number, the case status, the rule information, the processing strategy, and the rule number, and to identify business cases with the created correspondence as pending task cases to be processed.

[0063] A third aspect of the present invention provides an apparatus for processing pending cases, comprising: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a circuit; the at least one processor invokes the instructions in the memory to cause the apparatus for processing pending cases to perform the aforementioned method for processing pending cases.

[0064] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described method for handling pending cases.

[0065] The technical solution provided by this invention involves: acquiring pending task cases to be processed; performing data analysis on the pending task cases according to a preset anomaly analysis strategy to identify original anomalies in the pending task cases, and storing the original anomalies in a preset monitoring pool; performing data analysis on the original anomalies in the preset monitoring pool according to the anomaly analysis strategy to identify target anomalies in the preset monitoring pool; acquiring rule information and case information of the target anomalies; comparing and analyzing the case information and rule information to determine the target information elements required to terminate the anomalies; and sending the target information elements to the user terminal. In this invention, by using a preset anomaly analysis strategy to monitor and judge the status of pending task cases in real time, it can flexibly respond to changes in business, monitor the process breakpoint data of pending task cases in near real-time, and promptly provide feedback on the monitoring results, thereby improving the efficiency of handling process breakpoints in pending task cases. Attached Figure Description

[0066] Figure 1 This is a schematic diagram of one embodiment of the method for handling pending cases in this invention;

[0067] Figure 2This is a schematic diagram of another embodiment of the method for handling pending cases in this invention;

[0068] Figure 3 This is a schematic diagram of one embodiment of the device for processing pending cases in this invention;

[0069] Figure 4 This is a schematic diagram of another embodiment of the device for processing pending cases in this invention;

[0070] Figure 5 This is a schematic diagram of one embodiment of a device for processing pending cases in this invention. Detailed Implementation

[0071] This invention provides a method, apparatus, device, and storage medium for processing pending cases. Through anomaly analysis strategies, it performs real-time monitoring and status judgment on pending cases, enabling flexible response to business changes, near real-time monitoring of process breakpoint data of pending cases, and timely feedback of monitoring results, thereby improving the efficiency of processing process breakpoints of pending cases.

[0072] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” or “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0073] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the method for handling pending cases in this invention includes:

[0074] 101. Obtain pending unresolved cases, where pending cases are cases that are at the target process node but have not terminated the process;

[0075] It is understood that the executing entity of this invention can be a device for processing pending cases, a terminal, or a server; no specific limitation is made here. This embodiment of the invention will be described using a server as an example of the executing entity.

[0076] The server can detect the status of the process nodes of a case. When it detects that the current process node has not been terminated and cannot proceed to the next process node, the corresponding case is designated as an unresolved case. For example, in a claim case, documents need to be uploaded to close the case, but if the customer or surveyor fails to upload the documents, causing the claim case to not proceed to the next process node, then the claim case is an unresolved case.

[0077] 102. Perform data analysis on pending task cases according to the preset abnormal state analysis strategy to identify the original abnormal cases in the pending task cases and store the original abnormal cases in the preset monitoring pool. The abnormal state analysis strategy is used to analyze whether the information of process nodes and unprocessed delay time meet the preset conditions. The preset monitoring pool consists of multiple monitoring pools with different preset time periods.

[0078] The server performs anomaly analysis on the current process node of the pending task case according to the preset anomaly analysis strategy. It analyzes whether the materials or information required to be submitted in the current process node are complete and correct, and whether the unprocessed retention time of the current process node exceeds the preset time. Pending task cases with incomplete and / or incorrect materials or information required to be submitted in the current process node, and unprocessed retention time of the current process node exceeding the preset time are identified as original anomaly cases.

[0079] After the server obtains the original exception case, it uses a pre-configured Redis caching mechanism to store the original exception case in a pre-configured monitoring pool for a certain duration and different preset time periods in key-value format. For example, if the preset monitoring pool is set to a duration of 1 hour and the two different preset time periods are 14:00-15:00 and 15:00-16:00, then the original exception case is stored in the preset monitoring pool for the 14:00-15:00 preset time period. After performing data analysis (which can be exception state analysis) on the original exception cases in the preset monitoring pool for the 14:00-15:00 preset time period, cases to be processed are obtained, and cases to be processed are stored in the preset monitoring pool for the 15:00-16:00 preset time period.

[0080] 103. Based on the abnormal state analysis strategy, perform data analysis on the original abnormal cases in the pre-set monitoring pool to identify the target abnormal cases in the pre-set monitoring pool.

[0081] The server performs anomaly analysis on the current process node status of original anomaly cases in the pre-set monitoring pool for different preset time periods based on the anomaly analysis strategy. It analyzes whether the materials or information required to be submitted in the current process node of the original anomaly case are complete and correct, and whether the unprocessed retention time of the current process node of the original anomaly case exceeds the preset time. The server identifies pending cases where the materials or information required to be submitted in the current process node of the original anomaly case are incomplete and / or incorrect, or where the unprocessed retention time of the current process node of the original anomaly case exceeds the preset time as target anomaly cases.

[0082] Optionally, the server can scan the pre-set database in the pre-set monitoring pool through a pre-set scheduled task mechanism to obtain newly added cases in the pre-set monitoring pool. The server can then perform further abnormal state analysis and processing on the newly added cases and the original abnormal cases stored in the pre-set monitoring pool before the newly added cases, thereby obtaining the target abnormal cases. For example, if the time period of the pre-set monitoring pool corresponds to 12:00-13:00, the server can scan and detect the pre-set database corresponding to the pre-set monitoring pool through the pre-set scheduled task mechanism to obtain the newly stored original abnormal cases within the time period of 12:00-13:00.

[0083] 104. Obtain the rule information and case information of the target abnormal case, wherein the rule information is the conditions required to terminate the target abnormal case, and the case information is the information elements included in the target abnormal case;

[0084] The server can extract the case number and rule number from the tag information of the target abnormal case using a tag extraction algorithm. These case number and rule number are pre-configured. The server then sends these two numbers as parameters to a pre-configured rule engine. The rule engine retrieves the corresponding case information based on the case number and the corresponding rule information based on the rule number.

[0085] The rule information comprises the conditions required to terminate the process node of the target abnormal case. For example, the process node of the target abnormal case requires the submission of complete and correct claim documents, and both parties (A and B) must submit the documents, with clear images. The case information can be the information of the current process node in the target abnormal case, or it can be the information of the current process node in the target abnormal case and the corresponding termination conditions that must be met. For example, the case information of the target abnormal case may be the specific information of the claim documents submitted at the current process node of the target abnormal case; or, the case information of the target abnormal case may be the specific information of the claim documents submitted at the current process node of the target abnormal case and the conditions (information elements) that the claim documents must meet to terminate the current process node.

[0086] 105. Compare and analyze the case information and rule information to determine the target information elements required to terminate the target abnormal case;

[0087] The server analyzes and compares the case information against the rules to determine whether the case requires further processing, thus identifying the target information elements. For example, the case information might be that at the current process node, on [date] at [time], company X submitted investment document A and user X submitted investment document B. Investment document A contains information C (specifically including C1, C2, and C3), and investment document B contains information D (specifically including D1, D2, and D3). After comparing and analyzing C and D with the rule information (conditions E1, E2, E3, E4, and E5 required to terminate the current process node), it is determined that... C1, C2, and C3 are the same as E1, E2, and E3 (same information elements), and D1, D2, and D3 are the same as E2, E4, and E5 (same information elements). Therefore, E4 and E5 are target information element 1 corresponding to financial document A, and E1 and E3 are target information element 2 corresponding to financial document B. Target information element 1 and target information element 2 are target information elements of the target abnormal case. In the comparative analysis, the vector similarity between case information and rule information can be calculated, and information elements with a vector similarity greater than the first threshold are regarded as the same information elements.

[0088] 106. Send the target information element to the user terminal.

[0089] The server retrieves the corresponding processing strategy by matching the rule information or by retrieving the root rule information. This processing strategy includes the sending method of the target information element, such as SMS, telephone, WeChat, email, and E-handheld notification. After obtaining the processing strategy, the server generates a report, text message, email format content, or visual chart for the target information element according to the notification method (processing strategy). For example, if the processing strategy is email notification, the target information element is generated as text information, and the text information is formatted according to email format. The target information element (report, text message, email format content, or visual chart) is sent to the user terminal, which can be a mobile terminal or the server, according to the sending method corresponding to the notification method.

[0090] It should be emphasized that, in order to further ensure the privacy and security of the aforementioned target information elements, these target information elements can also be stored in a node of a blockchain.

[0091] In this embodiment of the invention, an abnormal state analysis strategy is used to monitor and determine the status of pending cases in real time. This allows for flexible response to changes in business operations, near real-time monitoring of process breakpoint data for pending cases, and timely feedback of monitoring results, thereby improving the efficiency of handling process breakpoints in pending cases.

[0092] Please see Figure 2 Another embodiment of the method for handling pending cases in this invention includes:

[0093] 201. Obtain pending unresolved cases, where pending cases are cases that are at the target process node but have not terminated the process;

[0094] It is understood that the executing entity of this invention can be a device for processing pending cases, a terminal, or a server; no specific limitation is made here. This embodiment of the invention will be described using a server as an example of the executing entity.

[0095] Specifically, before the server obtains pending task cases to be processed, it may also include: obtaining pre-stored case information, case number, and case status of business cases that are at the target process node but have not terminated the process; setting the rule information and feedback processing strategy of the business cases through a pre-configured rule engine, as well as the rule number of the rule information; creating a correspondence between case information, case number, case status, rule information, processing strategy, and rule number; and identifying the business cases with the created correspondence as pending task cases to be processed.

[0096] The server creates a pre-stored mapping relationship between case information, case number, case status, rule information, feedback processing strategy, and rule number for business cases that are at the target process node but have not terminated. This mapping binds the case information, case number, case status, feedback processing strategy, and rule information and rule number of the pre-built rule engine to the business cases. Specifically, there is a correspondence between rule number and rule information, allowing retrieval of the corresponding rule information based on the rule number; similarly, there is a correspondence between case number and case information for the business cases, allowing retrieval of the corresponding business case information based on the case number. This mapping relationship facilitates efficient retrieval and acquisition of the relevant content. The feedback processing strategy refers to the feedback method that sends the target information elements back to the user terminal.

[0097] 202. Perform data analysis on pending task cases according to the preset abnormal state analysis strategy to identify the original abnormal cases in the pending task cases and store the original abnormal cases in the preset monitoring pool. The abnormal state analysis strategy is used to analyze whether the information of process nodes and unprocessed delay time meet the preset conditions. The preset monitoring pool consists of multiple monitoring pools with different preset time periods.

[0098] The server performs anomaly analysis on the current process node of the pending task case according to the preset anomaly analysis strategy. It analyzes whether the materials or information required to be submitted in the current process node are complete and correct, and whether the unprocessed retention time of the current process node exceeds the preset time. Pending task cases with incomplete and / or incorrect materials or information required to be submitted in the current process node, and unprocessed retention time of the current process node exceeding the preset time are identified as original anomaly cases.

[0099] After the server obtains the original exception case, it uses a pre-configured Redis caching mechanism to store the original exception case in key-value format in a pre-defined monitoring pool for a certain duration and different preset time periods. For example, if the preset monitoring pool is set to a duration of 1 hour, and the two different preset time periods are 14:00-15:00 and 15:00-16:00, then the original exception case is stored in the preset monitoring pool for the 14:00-15:00 preset time period. After performing data analysis (which may be exception state analysis) on the original exception cases in the preset monitoring pool for the 14:00-15:00 preset time period, pending cases are obtained, and pending cases are stored in the preset monitoring pool for the 15:00-16:00 preset time period.

[0100] 203. Based on the abnormal state analysis strategy, perform data analysis on the original abnormal cases in the pre-set monitoring pool to identify the target abnormal cases in the pre-set monitoring pool.

[0101] Specifically, the server performs data analysis on the original abnormal cases stored in the monitoring pool of the first preset time period according to the abnormal state analysis strategy to obtain candidate abnormal cases; the candidate abnormal cases are stored in the monitoring pool of the second preset time period, wherein the start time of the second preset time period is later than the end time of the first preset time period; the server performs iterative data analysis on the candidate abnormal cases in the monitoring pool of the second preset time period according to the abnormal state analysis strategy to obtain the target abnormal case.

[0102] For example: Based on the anomaly analysis strategy, the original anomaly cases in the monitoring pool during the first preset time period of 10:00-11:00 are analyzed to obtain candidate anomaly cases (original anomaly cases that are still in an abnormal state). These candidate anomaly cases are stored in the monitoring pool during the second preset time period of 11:00-12:00. Based on the anomaly analysis strategy, the candidate anomaly cases in the monitoring pool during the second preset time period of 11:00-12:00 are analyzed to obtain anomaly cases to be analyzed (candidate anomaly cases that are still in an abnormal state). This process is repeated iteratively until the third preset time period of 20:00-21:00. The anomaly cases that are still in an abnormal state in the monitoring pool during the 20:00-21:00 period are taken as target anomaly cases.

[0103] Specifically, the server obtains the target time and determines whether the target time is the end time of the first preset time period. If the target time is the end time of the first preset time period, the server performs data analysis on the original abnormal cases stored in the monitoring pool of the first preset time period according to the abnormal state analysis strategy to obtain candidate abnormal cases.

[0104] After the server stores the original anomaly cases in the monitoring pool for the first preset time period, it performs real-time detection and judgment on the time (target time) in the monitoring pool for the first preset time period. If the target time is the end time of the first preset time period, the server performs data analysis on the original anomaly cases stored in the monitoring pool for the first preset time period according to the anomaly state analysis strategy to obtain candidate anomaly cases. For example, if the first preset time period is 10:00-11:00 and the target time is 11:00, then the target time is the end time of the first preset time period. In this case, the detection task in the scheduled task mechanism is started (analyzing the original anomaly cases stored in the monitoring pool for the first preset time period according to the anomaly state analysis strategy to obtain candidate anomaly cases). The detection task runs for 20 minutes. After the detection task finishes running, the candidate anomaly cases are stored in the monitoring pool for 11:20-12:20. And so on. The same operation steps are used for judging and analyzing the time of other preset time periods in the preset monitoring pool.

[0105] 204. Obtain the rule information and case information of the target abnormal case, wherein the rule information is the conditions required to terminate the target abnormal case, and the case information is the information elements included in the target abnormal case;

[0106] Specifically, the server obtains the case number and rule number of the target abnormal case based on the case status; sends the case number and rule number to the pre-set rule engine; the rule engine traverses the pre-set rule tree to obtain the rule information corresponding to the rule number; the rule engine generates a key value for the case number and retrieves the case information corresponding to the key value from the pre-set case hash table.

[0107] The server pre-creates a rule tree (i.e., a pre-configured rule tree) and stores the case information of pending cases in a pre-configured case hash table. When the server obtains a rule number through the pre-configured rule engine, it uses the rule engine to traverse the rule numbers of each node in the pre-configured rule tree to obtain the corresponding rule number in the rule tree node and retrieve the rule information of the node corresponding to that rule number. When the server obtains a case number through the pre-configured rule engine, it creates a key-value pair for that case number and uses the rule engine to retrieve the corresponding case number from the case hash table based on that key-value pair, thereby obtaining the case information that corresponds to that case number.

[0108] 205. Compare and analyze the case information and rule information to determine the target information elements required to terminate the target abnormal case;

[0109] Specifically, the server creates a case knowledge graph of case information and a rule knowledge graph of rule information, and performs a random walk on the case knowledge graph and rule knowledge graph to obtain the corresponding case information sequence and rule information sequence; calculates the cosine similarity between the case information sequence and the rule information sequence to obtain the similarity value; and identifies the information elements in the case information with similarity values ​​greater than a preset threshold as target information elements.

[0110] For example, by performing random walks on both the case knowledge graph and the rule knowledge graph, case information sequence A, case information sequence B, rule information sequence A, and rule information sequence B are obtained. The cosine similarity between case information sequence A and rule information sequences A and B is calculated, yielding cosine similarity 1 and cosine similarity 2. Similarly, the cosine similarity between case information sequence B and rule information sequences A and B is calculated, yielding cosine similarity 3 and cosine similarity 4. The average of cosine similarity 1, cosine similarity 2, cosine similarity 3, and cosine similarity 4 is calculated to obtain the final similarity value. This similarity value is subtracted from a preset threshold to obtain the difference. Information elements in case information with a difference greater than 0 are selected as target information elements. The server uses the knowledge graph and cosine similarity to compare and analyze case information and rule information, improving the accuracy of target information elements.

[0111] 206. Generate a visual chart of the target information element according to the preset feedback processing strategy, and render the visual chart onto the preset display page;

[0112] After the server obtains the preset feedback processing strategy, it generates a visual chart of the target information element according to the strategy, and renders the visual chart onto the preset display page through a floating window or display box. Optionally, links can be added to the visual chart to the corresponding case status and process node information of the target information element.

[0113] 207. Determine whether a case processing request based on a visual chart is received from the preset display page during the third preset time period;

[0114] The server determines whether a user has processed a case on the preset display page within the third preset time period by determining whether a case processing request based on a visual chart is received from the preset display page. For example, within 10 minutes (the third preset time period) after the visual chart is displayed on the preset display page, if the user clicks to view the visual chart on the preset display page, terminates the process at the corresponding case flow node, or fills in or selects any problems or missing information in the case processing content of the pop-up window, a corresponding case processing request based on the visual chart will be generated and sent to the server. This case processing request indicates that the user has viewed the visual chart on the preset display page and performed the corresponding case flow operation based on the visual chart.

[0115] 208. If no case processing request based on a visual chart is received from the preset display page within the third preset time period, the target information element is sent to the user terminal.

[0116] If the server does not receive a processing request from the preset display page within the third preset time, it obtains the feedback method and generates text or a visual chart corresponding to the feedback method for the target information element. For example, if the feedback method is SMS notification, the server generates the corresponding text content for the target information element. The server then sends a case processing request based on the visual chart to the user terminal via the Hypertext Transfer Protocol. After receiving the returned Hypertext Transfer Protocol request, the server sends the generated text or visual chart to the user terminal as feedback.

[0117] In this embodiment of the invention, based on the ability to flexibly respond to changes in business, monitor the process breakpoint data of pending cases in near real-time and provide timely feedback on the monitoring results, thereby improving the efficiency of handling process breakpoints of pending cases, the invention also determines whether a processing request is received from a preset display page within a preset time period, so as to send the target case information to the user's terminal. This reduces the operation of feedback on the target case information and increases the autonomy of the feedback operation on the target case information, thereby improving the efficiency of handling process breakpoints of pending cases.

[0118] The above describes the method for handling pending cases in embodiments of the present invention. The following describes the apparatus for handling pending cases in embodiments of the present invention. Please refer to [link / reference]. Figure 3 One embodiment of the device for processing pending cases in this invention includes:

[0119] The first acquisition module 301 is used to acquire pending task cases to be processed, wherein the pending task cases are cases that are at the target process node but have not terminated the process;

[0120] The first data analysis module 302 is used to perform data analysis on pending task cases according to a preset abnormal state analysis strategy, so as to identify the original abnormal cases in the pending task cases and store the original abnormal cases in a preset monitoring pool. The abnormal state analysis strategy is used to analyze whether the information of the process node and the unprocessed delay time meet the preset conditions. The preset monitoring pool consists of multiple monitoring pools with different preset time periods.

[0121] The second data analysis module 303 is used to perform data analysis on the original abnormal cases in the preset monitoring pool according to the abnormal state analysis strategy, so as to identify the target abnormal cases in the preset monitoring pool.

[0122] The second acquisition module 304 is used to acquire rule information and case information of the target abnormal case, wherein the rule information is the conditions required to terminate the target abnormal case, and the case information is the information elements included in the target abnormal case.

[0123] The comparison and analysis module 305 is used to compare and analyze case information and rule information to determine the target information elements required to terminate the target abnormal case;

[0124] The sending module 306 is used to send the target information element to the user terminal.

[0125] In this embodiment of the invention, an abnormal state analysis strategy is used to monitor and determine the status of pending cases in real time. This allows for flexible response to changes in business operations, near real-time monitoring of process breakpoint data for pending cases, and timely feedback of monitoring results, thereby improving the efficiency of handling process breakpoints in pending cases.

[0126] Please see Figure 4 Another embodiment of the device for processing pending cases in this invention includes:

[0127] The first acquisition module 301 is used to acquire pending task cases to be processed, wherein the pending task cases are cases that are at the target process node but have not terminated the process;

[0128] The first data analysis module 302 is used to perform data analysis on pending task cases according to a preset abnormal state analysis strategy, so as to identify the original abnormal cases in the pending task cases and store the original abnormal cases in a preset monitoring pool. The abnormal state analysis strategy is used to analyze whether the information of the process node and the unprocessed delay time meet the preset conditions. The preset monitoring pool consists of multiple monitoring pools with different preset time periods.

[0129] The second data analysis module 303 is used to perform data analysis on the original abnormal cases in the preset monitoring pool according to the abnormal state analysis strategy, so as to identify the target abnormal cases in the preset monitoring pool.

[0130] The second acquisition module 304 is used to acquire rule information and case information of the target abnormal case, wherein the rule information is the conditions required to terminate the target abnormal case, and the case information is the information elements included in the target abnormal case.

[0131] The comparison and analysis module 305 is used to compare and analyze case information and rule information to determine the target information elements required to terminate the target abnormal case;

[0132] The sending module 306 is used to send the target information element to the user terminal;

[0133] Specifically, the sending module 306 includes:

[0134] The generation unit 3061 is used to generate a visual chart of the target information element according to a preset feedback processing strategy, and render the visual chart to a preset display page.

[0135] The judgment unit 3062 is used to determine whether a case processing request based on a visual chart is received from the preset display page during the third preset time period;

[0136] The sending unit 3063 is used to send the target information element to the user terminal when no case processing request based on a visual chart is received from the preset display page during the third preset time period.

[0137] Optionally, the second data analysis module 303 includes:

[0138] The first data analysis unit 3031 is used to perform data analysis on the original abnormal cases stored in the monitoring pool of the first preset time period according to the abnormal state analysis strategy to obtain candidate abnormal cases.

[0139] Storage unit 3032 is used to store candidate abnormal cases in a monitoring pool for a second preset time period, wherein the start time of the second preset time period is later than the end time of the first preset time period.

[0140] The second data analysis unit 3033 is used to perform iterative data analysis on candidate abnormal cases in the monitoring pool during the second preset time period according to the abnormal state analysis strategy, so as to obtain the target abnormal case.

[0141] Optionally, the first data analysis unit 3031 can also be specifically used for:

[0142] Obtain the target time and determine whether the target time is the end time of the first preset time period;

[0143] If the target time is the end time of the first preset time period, then the original abnormal cases stored in the monitoring pool of the first preset time period are analyzed according to the abnormal state analysis strategy to obtain candidate abnormal cases.

[0144] Optionally, the comparison analysis module 305 can also be specifically used for:

[0145] Create a case knowledge graph of case information and a rule knowledge graph of rule information, and perform a random walk on the case knowledge graph and rule knowledge graph to obtain the corresponding case information sequence and rule information sequence;

[0146] Calculate the cosine similarity between the case information sequence and the rule information sequence to obtain the similarity value;

[0147] Information elements in case information with similarity values ​​greater than a preset threshold are identified as target information elements.

[0148] Optionally, the second acquisition module 304 can also be specifically used for:

[0149] Based on the case status of the target abnormal case, obtain the case number and rule number of the target abnormal case;

[0150] The case number and rule number are sent to the pre-set rule engine. The rule engine then traverses the pre-set rule tree to obtain the rule information corresponding to the rule number.

[0151] The rule engine generates a key-value pair for the case number and retrieves the case information corresponding to the key-value pair from a pre-set case hash table.

[0152] Optionally, the device for handling pending cases may also include:

[0153] The setting module 307 is used to obtain the case information, case number and case status of pre-stored business cases that are at the target process node but have not terminated the process, and to set the rule information and feedback processing strategy of the business cases through a pre-set rule engine, as well as the rule number of the rule information.

[0154] Module 308 is used to create the correspondence between case information, case number, case status, rule information, processing strategy and rule number, and to identify business cases with corresponding relationships as pending task cases to be processed.

[0155] In this embodiment of the invention, based on the ability to flexibly respond to changes in business, monitor the process breakpoint data of pending cases in near real-time and provide timely feedback on the monitoring results, thereby improving the efficiency of handling process breakpoints of pending cases, the invention also determines whether a processing request is received from a preset display page within a preset time period, so as to send the target case information to the user's terminal. This reduces the operation of feedback on the target case information and increases the autonomy of the feedback operation on the target case information, thereby improving the efficiency of handling process breakpoints of pending cases.

[0156] above Figure 3and Figure 4 The processing device for pending cases in the embodiments of the present invention will be described in detail from the perspective of modular functional entities. The processing equipment for pending cases in the embodiments of the present invention will be described in detail from the perspective of hardware processing.

[0157] Figure 5 This is a schematic diagram of a pending case processing device 500 provided in an embodiment of the present invention. The pending case processing device 500 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 510 (e.g., one or more processors) and a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) for storing application programs 533 or data 532. The memory 520 and storage media 530 can be temporary or persistent storage. The program stored in the storage media 530 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the pending case processing device 500. Furthermore, the processor 510 may be configured to communicate with the storage media 530 and execute the series of instruction operations in the storage media 530 on the pending case processing device 500.

[0158] The pending case processing device 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input / output interfaces 560, and / or one or more operating systems 531, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 5 The illustrated processing equipment structure for pending cases does not constitute a limitation on the processing equipment for pending cases. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0159] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the method for processing the pending case.

[0160] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0161] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0162] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0163] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of processing a pending task case, characterized by, The method for processing the pending task case comprises the following steps: acquiring a pending task case to be processed, wherein the pending task case is a case that is at a target process node but has not terminated the process; performing data analysis on the pending task case according to a preset abnormal state analysis strategy to identify an original abnormal case in the pending task case and store the original abnormal case in a preset monitoring pool, wherein the abnormal state analysis strategy is used to analyze whether information of a process node and an unprocessed residence time meet preset conditions, and the preset monitoring pool is a monitoring pool of multiple different preset time periods; after obtaining the original abnormal case, the original abnormal case is stored in the preset monitoring pool of a certain time length and different preset time periods in the form of key-value through a preset data structure server Redis caching mechanism; performing data analysis on the original abnormal case in the preset monitoring pool according to the abnormal state analysis strategy to analyze whether required submission materials or information in the information of the current process node of the original abnormal case are complete and correct and whether the unprocessed residence time of the current process node of the original abnormal case exceeds a preset time, so as to identify a target abnormal case in the preset monitoring pool; acquiring rule information and case information of the target abnormal case, wherein the rule information is a condition required for terminating the target abnormal case, and the case information is an information element included in the target abnormal case; performing comparative analysis on the case information and the rule information to determine a target information element required for terminating the target abnormal case; when performing the comparative analysis, information elements with a vector similarity between the case information and the rule information greater than a first threshold value are taken as same information elements, and information elements other than the same information elements in the rule information are taken as the target information element; sending the target information element to a user terminal; the data analysis on the original abnormal case in the preset monitoring pool according to the abnormal state analysis strategy to identify the target abnormal case in the preset monitoring pool comprises the following steps: performing data analysis on the original abnormal case stored in a monitoring pool of a first preset time period according to the abnormal state analysis strategy to obtain a candidate abnormal case; storing the candidate abnormal case in a monitoring pool of a second preset time period, wherein a starting time of the second preset time period is later than an ending time of the first preset time period; performing iterative data analysis on the candidate abnormal case in the monitoring pool of the second preset time period according to the abnormal state analysis strategy to obtain the target abnormal case.

2. The method of claim 1, wherein, the data analysis on the original abnormal case stored in the monitoring pool of the first preset time period according to the abnormal state analysis strategy to obtain the candidate abnormal case comprises the following steps: acquiring a target time and judging whether the target time is the ending time of the first preset time period; if the target time is the ending time of the first preset time period, performing data analysis on the original abnormal case stored in the monitoring pool of the first preset time period according to the abnormal state analysis strategy to obtain the candidate abnormal case.

3. The method of claim 1, wherein, the acquisition of the rule information and the case information of the target abnormal case comprises the following steps: According to the case state of the target abnormal case, obtain the case number and the rule number of the target abnormal case; Send the case number and the rule number to a preset rule engine, traverse a preset rule tree through the rule engine, and obtain rule information corresponding to the rule number; Generate a key value of the case number through the rule engine, and retrieve a preset case hash table to obtain case information corresponding to the key value.

4. The method of claim 1, wherein, The sending of the target information element to the user terminal comprises: According to a preset feedback processing strategy, a visualization chart of the target information element is generated, and the visualization chart is rendered to a preset display page; Determine whether a case processing request based on the visualization chart returned by the preset display page is received within a third preset time period; When the case processing request based on the visualization chart returned by the preset display page is not received within the third preset time period, the target information element is sent to the user terminal.

5. The method of claim 1-4, wherein, Before the obtaining of the pending task case to be processed, further comprising: Obtain the case information, case number and case state of a business case at a target process node but not terminated in a process, set the rule information and feedback processing strategy of the business case through a preset rule engine, and the rule number of the rule information; Create a correspondence relationship of the case information, the case number, the case state, the rule information, the processing strategy and the rule number, and determine the business case created with the correspondence relationship as the pending task case to be processed.

6. A pending case processing apparatus characterized by comprising: The processing device of the pending task case comprises: A first obtaining module is configured to obtain a pending task case to be processed, wherein the pending task case is a case at a target process node but not terminated in a process; A first data analysis module is configured to perform data analysis on the pending task case according to a preset abnormal state analysis strategy, to identify an original abnormal case in the pending task case, and store the original abnormal case in a preset monitoring pool, wherein the abnormal state analysis strategy is used to analyze whether information of a process node and unprocessed retention time meet a preset condition, and the preset monitoring pool is a monitoring pool of multiple different preset time periods; after obtaining the original abnormal case, the original abnormal case is stored in the preset monitoring pool of a certain time length and different preset time periods in the form of a key value through a preset Redis cache mechanism of a data structure server; A second data analysis module is configured to perform data analysis on the original abnormal case in the preset monitoring pool according to the abnormal state analysis strategy, to analyze whether required submission materials or information in the information of the current process node of the original abnormal case are complete and correct, and analyze whether the unprocessed retention time of the current process node of the original abnormal case exceeds a preset time, to identify a target abnormal case in the preset monitoring pool; A second obtaining module is configured to obtain rule information and case information of the target abnormal case, wherein the rule information is a required condition for terminating the target abnormal case, and the case information is an information element included in the target abnormal case. The comparative analysis module is configured to compare and analyze the case information and the rule information to determine target information elements required for terminating the target abnormal case; when performing the comparative analysis, the vector similarity between the case information and the rule information is calculated, information elements with a vector similarity greater than a first threshold value are taken as same information elements, and information elements in the rule information other than the same information elements are taken as the target information elements; The sending module is configured to send the target information elements to a user terminal; The second data analysis module is further configured to perform data analysis on original abnormal cases stored in the monitoring pool of the first preset time period according to the abnormal state analysis strategy to obtain candidate abnormal cases; The candidate abnormal cases are stored in the monitoring pool of a second preset time period, wherein a starting time of the second preset time period is later than an ending time of the first preset time period; The candidate abnormal cases in the monitoring pool of the second preset time period are iteratively analyzed according to the abnormal state analysis strategy to obtain the target abnormal case.

7. A pending case processing apparatus characterized by comprising: The pending task case processing device comprises a memory and at least one processor, the memory stores instructions, and the memory and the at least one processor are interconnected through a circuit; The at least one processor invokes the instructions in the memory, so that the pending task case processing device performs the pending task case processing method in any one of claims 1-5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the pending task case processing method in any one of claims 1-5.

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