Generation method and device of operation and maintenance operation guidance information and program product

Abstract

The invention discloses a generation method and device of operation and maintenance operation guidance information and a program product, relates to the technical field of operation and maintenance, and is applied to the field of financial science and technology. The method comprises the steps that operation and maintenance operation information of a target object is collected every preset duration, and an operation sequence is generated; state probability distribution is calculated according to a state transition matrix and the operation sequence, and the state transition matrix is determined according to an operation and maintenance operation document and historical operation and maintenance operation; predicting a target operation and maintenance operation executed by the target object in the next step according to the state probability distribution; and according to the operation information of the target operation and maintenance operation, performing retrieval in the operation and maintenance operation document to obtain operation guidance information, and pushing the operation guidance information to the target object. Through the method and the device, the problem that a traditional static document or keyword matching algorithm cannot provide operation guidance for a worker in real time according to the executed operation when the worker executes the operation and maintenance operation in the related technology is solved.

Description

Technical Field

[0001] This application relates to the field of operation and maintenance technology, and is applied to the financial technology field. Specifically, it relates to a method, device, and program product for generating operation and maintenance guidance information. Background Technology

[0002] In the field of banking system operation and maintenance, the accuracy and efficiency of operational procedures directly affect the stable operation and service quality of the system. In traditional operation and maintenance, guidance relies on static operation manuals or documents. When faced with complex multi-step processes, new employees often need to frequently consult documents, which is not only time-consuming but also prone to operational gaps or errors due to incomplete memorization or misunderstanding of the operation procedures. At the same time, although experienced operators can complete most operations from memory, they still need to refer back to the documents in certain situations to ensure compliance, which to some extent limits the improvement of operational efficiency.

[0003] There is currently no effective solution to the problem that traditional static documents or keyword matching algorithms cannot provide real-time operation guidance to staff when they perform maintenance operations in related technologies. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, and program product for generating operation and maintenance guidance information, so as to solve the problem in related technologies that traditional static documents or keyword matching algorithms cannot provide operation guidance to staff in real time based on the operations already performed when staff perform operation and maintenance operations.

[0005] To achieve the above objectives, according to one aspect of this application, a method for generating operation and maintenance guidance information is provided. The method includes: collecting operation and maintenance information of a target object after a preset time interval to generate an operation sequence; calculating a state probability distribution based on a state transition matrix and the operation sequence, wherein the state transition matrix is ​​determined based on operation and maintenance documents and historical operation and maintenance operations; predicting the next target operation and maintenance operation to be performed by the target object based on the state probability distribution; retrieving operation information from the operation and maintenance documents based on the operation information of the target operation and maintenance operation to obtain operation guidance information, and pushing the operation guidance information to the target object.

[0006] Furthermore, before collecting the operation and maintenance information of the target object after a preset time interval and generating the operation sequence, the method further includes: obtaining the operation and maintenance operation document of the business system; generating a target model based on the operation and maintenance operation document using a hidden Markov model, wherein the target model includes at least: a set of hidden states and a set of observed events, wherein the hidden states in the set of hidden states represent the state of the operation and maintenance process executed by the target object, and the observed events in the set of observed events represent the operation and maintenance operations executed by the target object; collecting historical operation and maintenance operation logs of the business system, and calculating the transition frequency between the hidden states based on the historical operation and maintenance operation logs to obtain the state transition matrix; calculating the transition frequency between the hidden states and the observed events based on the historical operation and maintenance operation logs to obtain the state probability matrix.

[0007] Further, calculating the state probability distribution based on the state transition matrix and the operation sequence includes: determining a first probability distribution for transitioning from a first state at time t-1 to a second state at time t based on the state transition matrix, wherein both the first state and the second state belong to the set of hidden states, and the first state and the second state are different, and t is a positive integer; determining a second probability distribution for the occurrence of a first observation event when the target object is in the second state at time t based on the state probability matrix, wherein the first observation event belongs to the set of observation events; calculating a third probability distribution for the target object being in the first state at time t-1 based on the operation sequence; and determining the probability that the target object will be in any hidden state in the next step based on the first probability distribution, the second probability distribution, and the third probability distribution, thus obtaining the state probability distribution.

[0008] Further, the state transition matrix is ​​obtained by calculating the transition frequency between the hidden states based on the historical operation and maintenance logs, including: determining the historical operation and maintenance operation and the execution order information of the historical operation and maintenance operation based on the historical operation and maintenance logs; calculating the transition frequency from the fourth state to the fifth state based on the execution order information, wherein the fourth state and the fifth state both belong to the set of hidden states and the fourth state and the fifth state are different; calculating the transition probability from the fourth state to the fifth state based on the transition frequency from the fourth state to the fifth state; and constructing the state transition matrix based on the transition probability from the fourth state to the fifth state.

[0009] Furthermore, generating a target model based on the hidden Markov model and the operation and maintenance document includes: extracting operation and maintenance operations from the operation and maintenance document using artificial intelligence algorithms, and extracting the execution order of the operation and maintenance operations; constructing the set of observation events based on the operation and maintenance operations and the execution order of the operation and maintenance operations; determining the operation and maintenance stage of the operation and maintenance process based on the operation and maintenance operations in the operation and maintenance process, and determining the hidden state and the hidden state sequence based on the operation and maintenance stage; constructing the set of hidden states based on the hidden states and the hidden state sequence, and determining the target model based on the set of hidden states and then the set of observation events.

[0010] Further, predicting the next target operation and maintenance operation to be performed by the target object based on the state probability distribution includes: determining the sixth state with the highest probability based on the state probability distribution; determining a transition probability vector in the state transition matrix based on the sixth state; determining a seventh state based on the transition probability vector, wherein the seventh state is the state with the highest probability of the target object being executed next; and determining the target operation and maintenance operation based on the operation and maintenance operation corresponding to the seventh state.

[0011] Furthermore, after predicting the next target operation and maintenance operation to be performed by the target object based on the state probability distribution, the method further includes: if the target operation and maintenance operation is a production release operation, querying the approval system based on the operation and maintenance operation of the target object to obtain query results; if the query results contain approval information for the operation and maintenance operation of the target object, performing a step of searching the operation and maintenance document based on the target operation and maintenance operation to obtain operation guidance information; if the query results do not contain approval information for the operation and maintenance operation of the target object, intercepting the subsequent operation and maintenance operation performed by the target object, and pushing a target prompt to the target object, wherein the target prompt is used to remind the target object to perform the approval operation.

[0012] To achieve the above objectives, according to another aspect of this application, an apparatus for generating operation and maintenance guidance information is provided. The apparatus includes: a collection unit, configured to collect operation and maintenance information of a target object every preset time interval and generate an operation sequence; a first calculation unit, configured to calculate a state probability distribution based on a state transition matrix and the operation sequence, wherein the state transition matrix is ​​determined based on an operation and maintenance document and historical operation and maintenance operations; a prediction unit, configured to predict the next target operation and maintenance operation to be performed by the target object based on the state probability distribution; and a first retrieval unit, configured to retrieve operation guidance information from the operation and maintenance document based on the operation information of the target operation and maintenance operation, and push the operation guidance information to the target object.

[0013] Furthermore, the device further includes: an acquisition unit, configured to acquire an operation and maintenance document of the business system before collecting operation and maintenance information of the target object after a preset time interval and generating an operation sequence; a generation unit, configured to generate a target model based on the operation and maintenance document using a hidden Markov model, wherein the target model includes at least: a set of hidden states and a set of observed events, wherein the hidden states in the set of hidden states represent the state of the operation and maintenance process executed by the target object, and the observed events in the set of observed events represent the operation and maintenance operations executed by the target object; a second calculation unit, configured to collect historical operation and maintenance logs of the business system and calculate the transition frequency between the hidden states based on the historical operation and maintenance logs to obtain the state transition matrix; and a third calculation unit, configured to calculate the transition frequency between the hidden states and the observed events based on the historical operation and maintenance logs to obtain a state probability matrix.

[0014] Further, the first calculation unit includes: a first determining subunit, configured to determine a first probability distribution for transitioning from a first state at time t-1 to a second state at time t based on the state transition matrix, wherein both the first state and the second state belong to the set of hidden states, and the first state and the second state are different, and t is a positive integer; a second determining subunit, configured to determine a second probability distribution for the occurrence of a first observation event when the target object is in the second state at time t based on the state probability matrix, wherein the first observation event belongs to the set of observation events; a first calculation subunit, configured to calculate a third probability distribution for the target object being in the first state at time t-1 based on the operation sequence; and a third determining subunit, configured to determine the probability that the target object will be in any hidden state in the next step based on the first probability distribution, the second probability distribution, and the third probability distribution, thereby obtaining the state probability distribution.

[0015] Further, the second calculation unit includes: a fourth determining subunit, used to determine the historical operation and maintenance operation based on the historical operation and maintenance operation log, and the execution order information of the historical operation and maintenance operation; a second calculation subunit, used to calculate the transition frequency from the fourth state to the fifth state based on the execution order information, wherein the fourth state and the fifth state both belong to the hidden state set, and the fourth state and the fifth state are different; a third calculation subunit, used to calculate the transition probability from the fourth state to the fifth state based on the transition frequency from the fourth state to the fifth state; and a first construction subunit, used to construct the state transition matrix based on the transition probability from the fourth state to the fifth state.

[0016] Further, the generation unit includes: an extraction subunit, used to extract operation and maintenance operations from the operation and maintenance operation document using artificial intelligence algorithms, and to extract the execution order of the operation and maintenance operations; a second construction subunit, used to construct the set of observation events based on the operation and maintenance operations and the execution order of the operation and maintenance operations; a fifth determination subunit, used to determine the operation and maintenance stage of the operation and maintenance process based on the operation and maintenance operations in the operation and maintenance process, and to determine the hidden state and the hidden state sequence based on the operation and maintenance stage; and a sixth determination subunit, used to construct the set of hidden states based on the hidden states and the hidden state sequence, and to determine the target model based on the set of hidden states and the set of observation events.

[0017] Further, the prediction unit includes: a seventh determining subunit, used to determine the sixth state with the highest probability based on the state probability distribution; an eighth determining subunit, used to determine a transition probability vector in the state transition matrix based on the sixth state; a ninth determining subunit, used to determine a seventh state based on the transition probability vector, wherein the seventh state is the state with the highest probability of the target object executing the next step; and a tenth determining subunit, used to determine the target operation based on the operation and maintenance operation corresponding to the seventh state.

[0018] Furthermore, the device further includes: a query unit, configured to, after predicting the next target operation and maintenance operation to be performed by the target object based on the state probability distribution, and in the case that the target operation and maintenance operation is a production release operation, query the approval system based on the operation and maintenance operation of the target object to obtain a query result; a second retrieval unit, configured to, when the query result contains the approval information of the operation and maintenance operation of the target object, perform the step of retrieving the operation and maintenance document based on the target operation and maintenance operation to obtain operation guidance information; and a push unit, configured to, when the query result does not contain the approval information of the operation and maintenance operation of the target object, intercept the subsequent operation and maintenance operation performed by the target object and push a target prompt to the target object, wherein the target prompt is used to remind the target object to perform the approval operation.

[0019] To achieve the above objectives, according to one aspect of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements the method for generating operation and maintenance guidance information as described in any of the above-mentioned methods, and the computer program, when executed by a processor, implements the steps of the method for generating operation and maintenance guidance information as described in various embodiments of this application.

[0020] To achieve the above objectives, according to one aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including stored computer instructions, wherein, when the computer instructions are executed by a processor, the method for generating operation and maintenance guidance information as described above is implemented.

[0021] To achieve the above objectives, according to one aspect of this application, an electronic device is provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors cause the one or more processors to implement the method for generating operation and maintenance guidance information as described above.

[0022] In this embodiment, operation sequence is generated by collecting operation and maintenance information of the target object after a preset time interval; a state probability distribution is calculated based on the state transition matrix and the operation sequence, wherein the state transition matrix is ​​determined based on the operation and maintenance document and historical operation and maintenance operations; the next target operation and maintenance operation to be executed by the target object is predicted based on the state probability distribution; operation guidance information is obtained by searching the operation and maintenance document based on the operation information of the target operation and maintenance operation, and the operation guidance information is pushed to the target object, thereby solving the technical problem that traditional static document or keyword matching algorithms cannot provide operation guidance to staff in real time based on the executed operation when staff perform operation and maintenance operations.

[0023] By collecting operation and maintenance information of the target object at preset intervals and generating operation sequences, and calculating the state probability distribution based on the state transition matrix and this operation sequence, the operation and maintenance process can be monitored in real time, achieving the technical effect of dynamically capturing operation behavior. Simultaneously, based on probability theory, the current position of the target object in the operation and maintenance process can be accurately inferred, realizing intelligent identification of the operation process and further achieving the technical effect of accurately locating the operation stage. Based on the state probability distribution, the next operation and maintenance operation of the target object is predicted. By retrieving operation guidance information from the operation and maintenance document based on the predicted target operation and maintenance operation, and pushing it to the target object, operators can immediately receive detailed guidance for the next operation, achieving personalized operation guidance and further reducing operational errors and optimizing the operation and maintenance process. Attached Figure Description

[0024] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0025] Figure 1This is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for generating operation and maintenance guidance information according to Embodiment 1 of this application;

[0026] Figure 2 This is a flowchart of a method for generating optional operation and maintenance guidance information according to Embodiment 1 of this application;

[0027] Figure 3 This is a flowchart illustrating the optional operation guidance information for providing operation and maintenance to users according to Embodiment 1 of this application;

[0028] Figure 4 This is a schematic diagram of the device for generating operation and maintenance guidance information according to Embodiment 2 of this application;

[0029] Figure 5 This is a schematic diagram of an electronic device that generates operation and maintenance guidance information according to Embodiment 3 of this application. Detailed Implementation

[0030] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0031] It should be noted that the processing method, apparatus, storage medium, and electronic device specified in this application can be used in the financial technology field to provide real-time operation guidance to staff when they perform maintenance operations, thereby improving their work efficiency. They can also be used in any field other than financial technology. The application fields of the processing method, apparatus, storage medium, and electronic device specified in this application are not limited.

[0032] It should be noted that the information collected in this application (including but not limited to user device information, user personal information, collected data, used data, generated data, processed data, etc.) and the data (including but not limited to data used for analysis, stored data, displayed data, collected information, used information, generated information, processed information, etc.) are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse. For example, this system has interfaces with relevant users or organizations, providing users with corresponding operation entry points for users to choose to agree to or refuse automated decision results; if the user chooses to refuse, the process proceeds to the expert decision-making stage.

[0033] Example 1

[0034] According to an embodiment of this application, a method embodiment for generating operation and maintenance guidance information is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] The method embodiment provided in Embodiment 1 of this application can be executed in a mobile terminal, computer terminal or similar computing device. Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for generating operation and maintenance guidance information, according to Embodiment 1 of this application. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0036] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0037] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for generating operation and maintenance guidance information in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the above-mentioned method for generating operation and maintenance guidance information. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0038] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0039] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0040] Under the aforementioned operating environment, this application provides the following: Figure 2 The method for generating operation and maintenance guidance information is shown. Figure 2 This is a flowchart of a method for generating optional operation and maintenance guidance information according to Embodiment 1 of this application.

[0041] Step S201: Collect the operation and maintenance information of the target object after a preset time interval and generate an operation sequence.

[0042] In this embodiment 1, in order to provide real-time guidance information on operation and maintenance (O&M) to staff (i.e., the target objects mentioned above), it is necessary to collect O&M operation information from the target objects in real time (i.e., periodically collect it after a preset time interval as mentioned above) to construct an operation sequence. O&M operation information includes, but is not limited to, specific operation events such as login behavior, auditing actions, and uploading activities. Through continuous collection, the dynamic flow of O&M operations can be captured, forming an ordered sequence of operation events.

[0043] Step S202: Calculate the state probability distribution based on the state transition matrix and operation sequence. The state transition matrix is ​​determined based on the operation and maintenance documents and historical operation and maintenance operations.

[0044] In this embodiment 1, the state probability distribution at the current moment is calculated based on the state transition matrix and the current operation sequence. The obtained state probability distribution is used to assist decision-makers in understanding the real-time state; for example, the hidden state with the highest probability is selected as the current state, thereby generating dynamic operation guidance. The state transition matrix A is a set of transition probabilities between various hidden states, statistically derived by analyzing operation and maintenance documents and past operation and maintenance records. It reflects the statistical patterns of state changes in the operation and maintenance process.

[0045] Step S203: Predict the next target operation and maintenance operation to be performed on the target object based on the state probability distribution.

[0046] In this embodiment 1, after calculating the state probability distribution at the current moment, the next operation and maintenance operation of the target object is further predicted. By analyzing the obtained state probability distribution, the current state (or stage) of the operation and maintenance process currently being executed by the target object is determined, thereby determining the most common operation and maintenance operation in this state, i.e., the aforementioned target operation and maintenance operation, as the prediction result of the next operation to be executed by the target object.

[0047] Step S204: Based on the operation information of the target operation, search the operation document to obtain operation guidance information, and push the operation guidance information to the target object.

[0048] In this embodiment 1, to assist the target object in safely executing the operation and maintenance process, a search is performed in the operation and maintenance operation document based on the predicted target operation and maintenance operation to obtain specific operation guidance information. For example, key information of the target operation and maintenance operation, such as the index information of the hidden state corresponding to the target operation and maintenance operation, the operation name or type of the operation and maintenance operation, is used as search conditions to query the operation and maintenance operation document. The operation steps and precautions matching the target operation are extracted from the document to generate the aforementioned operation guidance information.

[0049] Optionally, in the method for generating operation and maintenance guidance information provided in Embodiment 1 of this application, before collecting the operation and maintenance information of the target object after each preset time interval and generating the operation sequence, the method further includes: obtaining the operation and maintenance document of the business system; generating a target model based on the operation and maintenance document using a hidden Markov model, wherein the target model includes at least: a set of hidden states and a set of observed events, wherein the hidden states in the set of hidden states represent the state of the operation and maintenance process executed by the target object, and the observed events in the set of observed events represent the operation and maintenance operations executed by the target object; collecting historical operation and maintenance logs of the business system, and calculating the transition frequency between hidden states based on the historical operation and maintenance logs to obtain a state transition matrix; and calculating the transition frequency between hidden states and observed events based on the historical operation and maintenance logs to obtain a state probability matrix.

[0050] In this embodiment 1, in order to build an adaptive guided model suitable for the operation and maintenance of business systems, detailed operation and maintenance documents are first collected. These documents contain specific steps and specifications for various operation and maintenance operations, aiming to provide a basic data source for the target model based on Hidden Markov Model (HMM).

[0051] Then, based on the collected operation and maintenance documents, an Hidden Model (HMM) is constructed to obtain the target model described above. The core elements of this model are a set of hidden states and a set of observed events. Each state in the hidden state set represents a specific stage in the operation and maintenance process, such as the preparation stage, review stage, or execution stage; while the events in the observed event set describe the directly observable behaviors of the target object when performing operation and maintenance operations, such as logging in, submitting for approval, or executing scripts. This modeling process achieves an abstract representation of the operation and maintenance process, facilitating subsequent state inference and operation prediction.

[0052] Secondly, historical operation logs from the business system are collected. These logs record the detailed process of past operations, including timestamps, operators, operation types, and results. By parsing the historical logs, the transition frequency between hidden states is calculated, thus deriving the state transition matrix A. Each element a_ij of matrix A represents the frequency of operations transitioning from hidden state s_i to hidden state s_j, providing a quantitative description of the state transition pattern and serving as a key basis for model training and inference.

[0053] Finally, historical operation logs are further analyzed to calculate the transition frequency between hidden states and observed events, generating a state probability matrix B. Each element b_jk of matrix B represents the probability of observing event o_k in hidden state s_j. This matrix reflects the statistical characteristics of the operation operations that may be observed in each hidden state, enhancing the model's understanding and predictive ability of the actual operation operation process.

[0054] Through the steps described above, a target model is constructed that can infer the target object's position in the operation and maintenance process in real time based on its operational behavior and predict the likelihood of the next operation. This model not only provides dynamic operational guidance information, effectively improving the efficiency and security of operation and maintenance operations, but also, in practical applications, can provide customized guidance and interventions for different scenarios and specific operator behaviors.

[0055] Optionally, in the method for generating operation and maintenance guidance information provided in Embodiment 1 of this application, the calculation of the state probability distribution based on the state transition matrix and the operation sequence includes: determining a first probability distribution for transitioning from a first state at time t-1 to a second state at time t based on the state transition matrix, wherein both the first state and the second state belong to the set of hidden states and are different, and t is a positive integer; determining a second probability distribution for the occurrence of a first observation event when the target object is in the second state at time t based on the state probability matrix, wherein the first observation event belongs to the set of observation events; calculating a third probability distribution for the target object being in the first state at time t-1 based on the operation sequence; and determining the probability that the target object will be in any hidden state in the next step based on the first probability distribution, the second probability distribution, and the third probability distribution, thereby obtaining the state probability distribution.

[0056] In this embodiment 1, in order to accurately predict the next state of the target object in the operation and maintenance process, the first probability distribution of the transition from the current hidden state (denoted as the first state) to the next hidden state (denoted as the second state) at a certain time t-1 is first determined based on the state transition matrix. This distribution quantifies the frequency of transition from the first state to each possible second state in historical data, providing basic probability information of state transition for prediction.

[0057] Then, based on the state probability matrix, the second probability distribution of observing a specific operation (denoted as the first observation event) when the target object is in a specific second state at time t is calculated. This distribution reflects the probability of executing each observation event in a given hidden state and can help identify the typical behavior of the target operation in the current hidden state.

[0058] Secondly, based on the continuously acquired operation sequence, a third probability distribution of the target object being in the first state at time t-1 is calculated. This distribution, through statistical analysis of the actual operation sequence, reflects the probability of the target object being in each hidden state at previous times, providing real-time data support for state inference.

[0059] Finally, by integrating the first, second, and third probability distributions mentioned above, and using the probability multiplication principle, the probability that the target object is in any hidden state at time t is determined, thus obtaining the real-time updated state probability distribution. This distribution not only includes state transition information based on historical data but also incorporates the specific details of the target object's current operation sequence, achieving an organic combination of dynamic and static data and improving prediction accuracy.

[0060] For example, the sequence of operation events can be represented as O = [o1, o2, o3]. The probability distribution of the states can be calculated as: alpha[t][s_j] = Σ[alpha[t-1][s_i] A[i][j] B[j][o_t]], where alpha[t][s_j] represents the probability that the operation and maintenance process is in the hidden state s_j given that the sequence o_1, o_2, ..., o_t is observed at time t, Σ represents the summation symbol, which is the summation of the probabilities of all possible hidden states s_i at the previous time step, alpha[t-1][s_i] represents the probability that the operation and maintenance process is in the hidden state s_i given that the sequence o_1, o_2, ..., o_{t-1} is observed at time t-1, A[i][j] represents the element in the state transition matrix A, representing the probability of transitioning from the hidden state s_i at time t-1 to the hidden state s_j at time t, and B[j][o_t] represents the element in the observation probability matrix B, representing the probability of observing event o_t given that the process is in the hidden state s_j at time t.

[0061] Through the above steps, the technical effect of inferring the possible states of target objects in real time during the operation and maintenance process is achieved. Simultaneously, by dynamically evaluating the relationship between operation sequences and hidden states, the hidden state of the next operation can be predicted in real time based on the target object's operational behavior, realizing intelligent operation and maintenance prediction and further improving the efficiency and accuracy of the operation process. Furthermore, this prediction mechanism provides a data foundation for subsequent operational guidance and compliance verification, ensuring that operation and maintenance operations are both efficient and compliant, significantly optimizing the overall operation and maintenance process.

[0062] Optionally, in the method for generating operation and maintenance guidance information provided in Embodiment 1 of this application, the transition frequency between hidden states is calculated based on historical operation and maintenance operation logs to obtain a state transition matrix, including: determining historical operation and maintenance operations and their execution order information based on historical operation and maintenance operation logs; calculating the transition frequency from the fourth state to the fifth state based on the execution order information, wherein the fourth state and the fifth state both belong to the set of hidden states and are different; calculating the transition probability from the fourth state to the fifth state based on the transition frequency from the fourth state to the fifth state; and constructing a state transition matrix based on the transition probability from the fourth state to the fifth state.

[0063] In this embodiment 1, in order to construct a model reflecting the state transition patterns in historical operation and maintenance processes, historical operation and maintenance operations and their execution order information were first extracted from historical operation and maintenance operation logs. This process involves deep parsing of the logs, aiming to identify the type of each operation and maintenance operation and the order in which these operations are executed in the process, providing the original data source for subsequent transition probability calculations.

[0064] Then, using the extracted execution order information, the frequency of transitions from any hidden state (taking the fourth state as an example) to another different hidden state (referred to as the fifth state) was calculated. This frequency statistic is essentially a quantification of state transition events in the historical operation and maintenance process. It reveals the actual number of times the fourth state migrates to the fifth state, providing the basic numerical values ​​for constructing the state transition matrix A.

[0065] Secondly, based on the aforementioned frequency of transitions from the fourth state to the fifth state, the transition probability of this state transition was calculated. The transition probability refers to the proportion of all transition events starting from the fourth state that lead to the fifth state. This calculation process not only reflects the regularity of historical operations but also lays the theoretical foundation for subsequent adaptive guidance of operation and maintenance operations based on probabilistic reasoning.

[0066] Finally, based on the transition probabilities from the fourth to the fifth state, and between all other hidden states, a complete state transition matrix A was constructed. Matrix A comprehensively summarizes the probabilistic characteristics of the transition between any two hidden states in the operation and maintenance process. Using this matrix, it is possible to predict the possible range of the next hidden state and its corresponding probability given the current hidden state, thus providing a key tool for real-time state inference and future operation prediction.

[0067] For example, the process of calculating the state transition matrix A can be represented as A[i][j]=count(s_i→s_j) / Σ_count(s_i→ ), where count(s_i→s_j) is the number of times the state s_i directly transitions to state s_j in the historical operation and maintenance, and Σ represents the summation. Then it represents any state, Σ_count(s_i→) A[i][j] represents the total number of transitions from state s_i to any other state (including s_j). A[i][j] is an element in the state transition matrix A, representing the probability of transitioning from state s_i to state s_j.

[0068] Through the above steps, the technical effect of accurately quantifying the state transition patterns in historical operation and maintenance processes is achieved. In particular, by constructing a state transition matrix, it is possible to intelligently predict and guide the operation and maintenance processes of the target object based on probability theory, effectively improving the efficiency and security of operation and maintenance operations, overcoming the uncertainties in traditional operation and maintenance process guidance, and the limitations of rule-based static guidance methods.

[0069] Optionally, in the method for generating operation and maintenance guidance information provided in Embodiment 1 of this application, the target model is generated based on the operation and maintenance operation document using a hidden Markov model, including: extracting operation and maintenance operations from the operation and maintenance operation document using an artificial intelligence algorithm, and extracting the execution order of the operation and maintenance operations; constructing an observation event set based on the operation and maintenance operations and the execution order of the operation and maintenance operations; determining the operation and maintenance stage of the operation and maintenance process based on the operation and maintenance operations in the operation and maintenance process, and determining the hidden state and the hidden state sequence based on the operation and maintenance stage; constructing a hidden state set based on the hidden state and the hidden state sequence, and determining the target model based on the hidden state set and the observation event set.

[0070] In this first embodiment, to construct a predictive model that matches the characteristics of the operation and maintenance process, artificial intelligence algorithms, with a particular focus on natural language processing, are first applied to automatically extract key operation and maintenance operations and their execution sequence information from the operation and maintenance documents. This process involves deep analysis and understanding of the document content, aiming to identify and record all operation steps and their sequential relationships in the operation and maintenance process, providing accurate data support for subsequent model construction.

[0071] Then, having clarified the operational and maintenance operations and their execution sequence, a set of observation events is constructed. This set summarizes the characteristics of all observable operations in the operational and maintenance process. By encoding operational and maintenance operations as observation events, the model can perform state estimation based on these events, that is, it can infer the current possible operational and maintenance stage from operational behavior.

[0072] Secondly, by analyzing the structure of the operation and maintenance process, the characteristic operations of each stage were identified, thereby determining the hidden states and their sequences. Hidden states represent internal stages in the operation and maintenance process that cannot be directly observed but are crucial for understanding the overall process, such as login, approval, and configuration. The sequence of hidden states depicts the potential evolution paths of these stages, providing the model with a deep description of the dynamic structure of the operation and maintenance process.

[0073] Finally, based on the defined set of hidden states and the set of observed events, the target model, namely the Hidden Markov Model (HMM), is constructed. The core of this model lies in its ability to learn the transition probabilities between states and the probability of observing a specific event in each state based on historical operational operations. This allows for real-time analysis of the current operation sequence, inference of the most likely hidden state, prediction of the next operation, and compliance verification when necessary, ensuring the accuracy and security of the operational process.

[0074] For example, the set of hidden states can be represented as S={S1: initial preparation stage, S2: review process stage, S3: solution design and upload stage, S4: service document preparation and submission stage}, and the set of observed events can be represented as O={O1: system login, O2: pipeline approval request, O3: solution upload, O4: SD form creation and filling}.

[0075] Through the above steps, not only can key information about operation and maintenance (O&M) processes be automatically extracted from documents, but also a Hidden Markov Model can be dynamically constructed based on this information, enabling intelligent prediction and guidance of O&M processes. This series of operations significantly improves the efficiency of O&M personnel in performing operations, while effectively ensuring the compliance of O&M operations and the stability of production systems through real-time risk monitoring and intervention.

[0076] Optionally, in the method for generating operation and maintenance guidance information provided in Embodiment 1 of this application, predicting the target operation and maintenance operation to be performed next by the target object based on the state probability distribution includes: determining the sixth state with the highest probability based on the state probability distribution; determining the transition probability vector in the state transition matrix based on the sixth state; determining the seventh state based on the transition probability vector, wherein the seventh state is the state with the highest probability of being performed next by the target object; and determining the target operation and maintenance operation based on the operation and maintenance operation corresponding to the seventh state.

[0077] In this embodiment 1, in order to determine the most likely next maintenance operation to be performed by the target object in the maintenance process, the probability distribution of all possible hidden states is first calculated using the forward algorithm of a Hidden Markov Model (HMM) based on the real-time collected sequence of operation events. This probability distribution reflects the likelihood of the target object being in each maintenance stage at the current moment, with the highest probability corresponding to the most likely state, namely the sixth state.

[0078] Then, based on the determined sixth state, the corresponding transition probability vector is found in the state transition matrix. The state transition matrix records the probability of transitioning from each hidden state to all other hidden states. This vector provides the transition possibilities from the sixth state to all subsequent states and is the key basis for predicting the next state transition.

[0079] Secondly, by analyzing the transition probability vectors obtained above, the seventh state with the highest probability is identified. This state represents the next stage that the target object is most likely to proceed to after completing the maintenance operations of the sixth state. The process of determining the seventh state is based on the principle of maximizing probability, ensuring the accuracy and reliability of the prediction.

[0080] Finally, based on the operational phase represented by the seventh state, the corresponding operational operations were further identified and determined as target operational operations. This operation is the specific task that the target object should perform in the predicted next phase, providing clear guidance for operational personnel and avoiding blind and uncertain operations.

[0081] Through the above steps, the technical effect of dynamically predicting and guiding the next step of operation and maintenance (O&M) is achieved, significantly improving the efficiency and compliance of O&M operations, avoiding potential risks caused by operational errors, and making the adaptive guidance mechanism of the O&M process more intelligent and precise based on the probabilistic prediction characteristics of HMM. This series of operations not only improves the work efficiency of O&M personnel but also strengthens the security control of the O&M process.

[0082] Optionally, in the method for generating operation and maintenance guidance information provided in Embodiment 1 of this application, after predicting the next target operation and maintenance operation to be performed by the target object based on the state probability distribution, the method further includes: if the target operation and maintenance operation is a production release operation, querying the approval system based on the operation and maintenance operation of the target object to obtain the query result; if the query result contains the approval information of the operation and maintenance operation of the target object, performing a step of searching the operation and maintenance document based on the target operation and maintenance operation to obtain the operation guidance information; if the query result does not contain the approval information of the operation and maintenance operation of the target object, intercepting the subsequent operation and maintenance operation performed by the target object, and pushing a target prompt to the target object, wherein the target prompt is used to remind the target object to perform the approval operation.

[0083] In this embodiment 1, to ensure the compliance and security of the operation and maintenance process, especially when the target operation and maintenance operation involves sensitive production release operations, the operation and maintenance operation currently being performed by the target object is first identified and confirmed. After confirming the nature of the operation, a query request is automatically initiated in the approval system based on the relevant operation information of the target operation and maintenance operation, in order to verify whether there is a corresponding approval record.

[0084] Then, if the query results show valid approval information, it indicates that the production release operation for the target object has been authorized. Further, based on the specific needs of the target operation, relevant operational guidance information is retrieved from the operation and maintenance documentation. This step provides operation and maintenance personnel with precise operating procedures and precautions, ensuring the smooth completion of the operation process.

[0085] Secondly, if the query results fail to find the corresponding approval information, measures will be taken immediately to prevent the target from continuing to execute production release operations. This immediate intervention avoids unauthorized changes to the production environment and effectively prevents potential operational risks and data security issues.

[0086] Finally, to explain why the operation was blocked and to guide the next correct action, a target notification was sent to the target group. This notification clearly informed the operations personnel that the production release operation required an approval process beforehand, and included instructions on how to perform the approval process, including a link to the approval process and relevant documentation, so that the operations personnel could promptly complete the necessary compliance procedures.

[0087] For example, a user issues a command to the operations and maintenance system to enter the production environment, triggering subsequent automated process monitoring and guidance mechanisms. The operations and maintenance system then captures the user's operation events and passes them as input to the HMM engine (i.e., the target model mentioned above) for real-time state inference. Based on the received event sequence, the HMM engine calculates and returns the most probable state as "production release," with a probability of 92%. Secondly, once it is confirmed that the user is in the critical "production release" stage, the system immediately calls the compliance engine to automatically verify whether the current operation has obtained the necessary approval status. Finally, in the event of missing approval, the system takes action: on the one hand, it intercepts subsequent production release operations attempted by the user, preventing non-compliant operations; on the other hand, the system proactively pushes an approval link to the user, providing a direct path to remedy the approval process and ensuring the integrity of the pre-operation compliance check and approval process.

[0088] Through the above steps, not only can real-time monitoring of operations and maintenance related to production releases be achieved, ensuring that all critical operations are properly approved, but also non-compliant behaviors can be immediately stopped and the correct actions can be guided to be taken by operations and maintenance personnel. This reduces human error and improves the stability and security of the production environment, thereby achieving the technical effects of optimizing operations and maintenance processes, strengthening risk management, and improving operational security.

[0089] Optionally, in this embodiment 1, Figure 3 This is a schematic diagram of an optional process for providing operation guidance information to users based on Embodiment 1 of this application. For example... Figure 3As shown, firstly, the operation capture layer monitors and collects the behavior logs of users (i.e., the target objects mentioned above) in real time during operation and maintenance. These logs are transmitted as event sequences to the HMM state inference engine (i.e., the target model mentioned above). The engine uses a pre-trained model to analyze the logs and infer the most likely stage of the current operation and maintenance.

[0090] Then, the state matching engine receives the inference results from the HMM state inference engine and determines the corresponding operation and maintenance operation based on the operation and maintenance stage, i.e., the target operation and maintenance operation mentioned above. Next, the dynamic guidance generator generates suggestions for the next operation based on the target operation and maintenance operation. These suggestions are based on the next state transition probability predicted by the HMM model, providing the user with the most suitable operational guidance.

[0091] Secondly, the generated dynamic guidance information must pass the compliance verification module before reaching the user interface. This module verifies whether the suggested operation complies with preset compliance rules, such as whether the necessary approval process has been completed, based on the characteristics and stage of the operation.

[0092] Finally, based on the compliance verification results, a decision is made on whether to allow the user to execute the dynamically generated operation guide, or to block the operation due to compliance issues. If the operation is compliant, the guide will be allowed to reach the user's interface, directly instructing the user to perform the maintenance operation; if there is a compliance risk, the relevant operation will be blocked, and specific compliance verification information will be pushed to the user to guide the user through the compliance process.

[0093] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0094] In summary, the method for generating operation and maintenance guidance information provided in this application embodiment generates an operation sequence by collecting operation and maintenance information of the target object every preset time interval; calculates the state probability distribution based on the state transition matrix and the operation sequence, wherein the state transition matrix is ​​determined based on the operation and maintenance document and historical operation and maintenance operations; predicts the next target operation and maintenance operation to be executed by the target object based on the state probability distribution; retrieves the operation guidance information from the operation and maintenance document based on the operation information of the target operation and maintenance operation; and pushes the operation guidance information to the target object. This solves the problem in related technologies where traditional static documents or keyword matching algorithms cannot provide real-time operation guidance to staff based on the executed operations when staff perform operation and maintenance operations.

[0095] By collecting operation and maintenance information of the target object at preset intervals and generating operation sequences, and calculating the state probability distribution based on the state transition matrix and this operation sequence, the operation and maintenance process can be monitored in real time, achieving the technical effect of dynamically capturing operation behavior. Simultaneously, based on probability theory, the current position of the target object in the operation and maintenance process can be accurately inferred, realizing intelligent identification of the operation process and further achieving the technical effect of accurately locating the operation stage. Based on the state probability distribution, the next operation and maintenance operation of the target object is predicted. By retrieving operation guidance information from the operation and maintenance document based on the predicted target operation and maintenance operation, and pushing it to the target object, operators can immediately receive detailed guidance for the next operation, achieving personalized operation guidance and further reducing operational errors and optimizing the operation and maintenance process.

[0096] Example 2

[0097] This application also provides an apparatus for generating operation and maintenance guidance information. It should be noted that this apparatus can be used to execute the method for generating operation and maintenance guidance information provided in this application. The apparatus for generating operation and maintenance guidance information provided in this application is described below.

[0098] According to an embodiment of this application, an apparatus for implementing the above-described method for generating operation and maintenance guidance information is also provided. Figure 4 This is a schematic diagram of a device for generating operation and maintenance guidance information according to Embodiment 2 of this application. Figure 4 As shown, the device includes: a data acquisition unit 401, a first calculation unit 402, a prediction unit 403, and a first retrieval unit 404.

[0099] Specifically, the acquisition unit 401 is used to collect the operation and maintenance information of the target object after a preset time interval and generate an operation sequence.

[0100] The first calculation unit 402 is used to calculate the state probability distribution based on the state transition matrix and the operation sequence, wherein the state transition matrix is ​​determined based on the operation and maintenance operation documents and historical operation and maintenance operations.

[0101] The prediction unit 403 is used to predict the next target operation and maintenance operation to be performed by the target object based on the state probability distribution.

[0102] The first retrieval unit 404 is used to search the operation and maintenance document based on the operation information of the target operation and maintenance operation, obtain operation guidance information, and push the operation guidance information to the target object.

[0103] The device for generating operation and maintenance guidance information provided in this application embodiment collects operation and maintenance information of the target object every preset time interval by the collection unit 401 and generates an operation sequence; the first calculation unit 402 calculates the state probability distribution based on the state transition matrix and the operation sequence, wherein the state transition matrix is ​​determined based on the operation and maintenance document and historical operation and maintenance operations; the prediction unit 403 predicts the next target operation and maintenance operation to be performed by the target object based on the state probability distribution; the first retrieval unit 404 retrieves the operation and maintenance information in the operation and maintenance document based on the operation information of the target operation and maintenance operation to obtain operation guidance information and pushes the operation guidance information to the target object. This solves the problem in related technologies that when staff perform operation and maintenance operations, traditional static documents or keyword matching algorithms cannot provide real-time operation guidance to staff based on the operations already performed.

[0104] By collecting operation and maintenance information of the target object at preset intervals and generating operation sequences, and calculating the state probability distribution based on the state transition matrix and this operation sequence, the operation and maintenance process can be monitored in real time, achieving the technical effect of dynamically capturing operation behavior. Simultaneously, based on probability theory, the current position of the target object in the operation and maintenance process can be accurately inferred, realizing intelligent identification of the operation process and further achieving the technical effect of accurately locating the operation stage. Based on the state probability distribution, the next operation and maintenance operation of the target object is predicted. By retrieving operation guidance information from the operation and maintenance document based on the predicted target operation and maintenance operation, and pushing it to the target object, operators can immediately receive detailed guidance for the next operation, achieving personalized operation guidance and further reducing operational errors and optimizing the operation and maintenance process.

[0105] Optionally, in the device for generating operation and maintenance guidance information provided in Embodiment 2 of this application, the device further includes: an acquisition unit, used to acquire the operation and maintenance operation documents of the business system before collecting the operation and maintenance operation information of the target object after a preset time interval and generating the operation sequence; a generation unit, used to generate a target model based on the operation and maintenance operation documents using a hidden Markov model, wherein the target model includes at least: a set of hidden states and a set of observed events, wherein the hidden states in the set of hidden states represent the state of the operation and maintenance process executed by the target object, and the observed events in the set of observed events represent the operation and maintenance operations executed by the target object; a second calculation unit, used to collect historical operation and maintenance operation logs of the business system and calculate the transition frequency between hidden states based on the historical operation and maintenance operation logs to obtain a state transition matrix; and a third calculation unit, used to calculate the transition frequency between hidden states and observed events based on the historical operation and maintenance operation logs to obtain a state probability matrix.

[0106] Optionally, in the device for generating operation and maintenance guidance information provided in Embodiment 2 of this application, the first calculation unit 402 includes: a first determining subunit, used to determine a first probability distribution for transitioning from a first state at time t-1 to a second state at time t based on a state transition matrix, wherein both the first state and the second state belong to the set of hidden states, and the first state and the second state are different, and t is a positive integer; a second determining subunit, used to determine a second probability distribution for the occurrence of a first observation event when the object is in the second state at time t based on a state probability matrix, wherein the first observation event belongs to the set of observation events; a first calculation subunit, used to calculate a third probability distribution for the object in the first state at time t-1 based on an operation sequence; and a third determining subunit, used to determine the probability that the target object will be in any hidden state in the next step based on the first probability distribution, the second probability distribution, and the third probability distribution, thereby obtaining a state probability distribution.

[0107] Optionally, in the device for generating operation and maintenance guidance information provided in Embodiment 2 of this application, the second calculation unit includes: a fourth determining subunit, used to determine historical operation and maintenance operations and the execution order information of historical operation and maintenance operations based on historical operation and maintenance operation logs; a second calculation subunit, used to calculate the transition frequency from the fourth state to the fifth state based on the execution order information, wherein the fourth state and the fifth state both belong to the set of hidden states, and the fourth state and the fifth state are different; a third calculation subunit, used to calculate the transition probability from the fourth state to the fifth state based on the transition frequency from the fourth state to the fifth state; and a first construction subunit, used to construct a state transition matrix based on the transition probability from the fourth state to the fifth state.

[0108] Optionally, in the device for generating operation and maintenance guidance information provided in Embodiment 2 of this application, the above-mentioned generation unit includes: an extraction subunit, used to extract operation and maintenance operations in the operation and maintenance process from the operation and maintenance operation document using an artificial intelligence algorithm, and to extract the execution order of the operation and maintenance operations; a second construction subunit, used to construct an observation event set based on the operation and maintenance operations and the execution order of the operation and maintenance operations; a fifth determination subunit, used to determine the operation and maintenance stage of the operation and maintenance process based on the operation and maintenance operations in the operation and maintenance process, and to determine the hidden state and the hidden state sequence based on the operation and maintenance stage; and a sixth determination subunit, used to construct a hidden state set based on the hidden state and the hidden state sequence, and to determine the target model based on the hidden state set and the observation event set.

[0109] Optionally, in the device for generating operation and maintenance guidance information provided in Embodiment 2 of this application, the prediction unit 403 includes: a seventh determining subunit, used to determine the sixth state with the highest probability based on the state probability distribution; an eighth determining subunit, used to determine the transition probability vector in the state transition matrix based on the sixth state; a ninth determining subunit, used to determine the seventh state based on the transition probability vector, wherein the seventh state is the state with the highest probability of the target object executing the next step; and a tenth determining subunit, used to determine the target operation and maintenance operation based on the operation and maintenance operation corresponding to the seventh state.

[0110] Optionally, in the device for generating operation and maintenance guidance information provided in Embodiment 2 of this application, the device further includes: a query unit, used to, after predicting the next target operation and maintenance operation to be performed by the target object based on the state probability distribution, and in the case that the target operation and maintenance operation is a production release operation, query the approval system based on the operation and maintenance operation of the target object to obtain a query result; a second retrieval unit, used to, when the query result contains the approval information of the operation and maintenance operation of the target object, perform the step of retrieving the operation and maintenance operation document based on the target operation and maintenance operation to obtain operation guidance information; and a push unit, used to, when the query result does not contain the approval information of the operation and maintenance operation of the target object, intercept the subsequent operation and maintenance operation performed by the target object and push a target prompt to the target object, wherein the target prompt is used to remind the target object to perform the approval operation.

[0111] It should be noted that the acquisition unit 401, the first calculation unit 402, the prediction unit 403, and the first retrieval unit 404 mentioned above correspond to steps S201 to S204 in Embodiment 1. The two modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules can also be part of the device and run in the computer terminal 10 provided in Embodiment 1.

[0112] Example 3

[0113] Embodiments of this application may provide an electronic device. Figure 5 This is a schematic diagram of an electronic device that generates operation and maintenance guidance information according to Embodiment 3 of this application. For example... Figure 5 As shown, the electronic device may include: one or more ( Figure 5 (Only one is shown) processor 502, memory 504, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0114] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0115] The processor can access the information and application programs stored in the memory via the transmission device to execute the following steps: collect the operation and maintenance information of the target object after a preset time interval and generate an operation sequence; calculate the state probability distribution based on the state transition matrix and the operation sequence, wherein the state transition matrix is ​​determined based on the operation and maintenance document and historical operation and maintenance operations; predict the next target operation and maintenance operation to be executed by the target object based on the state probability distribution; retrieve the operation information of the target operation and maintenance operation from the operation and maintenance document to obtain operation guidance information, and push the operation guidance information to the target object.

[0116] The processor can invoke information and applications stored in the memory via a transmission device to execute the following steps: Before collecting the operation and maintenance information of the target object after a preset time interval and generating the operation sequence, the above method further includes: obtaining the operation and maintenance operation documents of the business system; generating a target model based on the operation and maintenance operation documents using a hidden Markov model, wherein the target model includes at least: a set of hidden states and a set of observed events, where the hidden states in the set of hidden states represent the state of the operation and maintenance process executed by the target object, and the observed events in the set of observed events represent the operation and maintenance operations executed by the target object; collecting historical operation and maintenance operation logs of the business system, and calculating the transition frequency between hidden states based on the historical operation and maintenance operation logs to obtain a state transition matrix; calculating the transition frequency between hidden states and observed events based on the historical operation and maintenance operation logs to obtain a state probability matrix.

[0117] The processor can access information and application programs stored in memory via a transmission device to execute the following steps: calculating the state probability distribution based on the state transition matrix and operation sequence, including: determining a first probability distribution for transitioning from a first state at time t-1 to a second state at time t based on the state transition matrix, wherein both the first and second states belong to the set of hidden states and are different, and t is a positive integer; determining a second probability distribution for the occurrence of a first observation event when the target object is in the second state at time t based on the state probability matrix, wherein the first observation event belongs to the set of observation events; calculating a third probability distribution for the target object being in the first state at time t-1 based on the operation sequence; and determining the probability that the target object will be in any hidden state in the next step based on the first, second, and third probability distributions, thus obtaining the state probability distribution.

[0118] The processor can access information and applications stored in memory via a transmission device to execute the following steps: Calculate the transition frequency between hidden states based on historical operation logs to obtain a state transition matrix, including: determining historical operation logs and their execution order information based on the historical operation logs; calculating the transition frequency from the fourth state to the fifth state based on the execution order information, where both the fourth and fifth states belong to the hidden state set and are different; calculating the transition probability from the fourth state to the fifth state based on the transition frequency; and constructing the state transition matrix based on the transition probability from the fourth state to the fifth state.

[0119] The processor can access information and applications stored in memory via a transmission device to execute the following steps: generating a target model based on a hidden Markov model and operation and maintenance (O&M) documents, including: extracting O&M operations from the O&M documents using artificial intelligence algorithms, and extracting the execution order of O&M operations; constructing an observation event set based on the O&M operations and their execution order; determining the O&M stage of the O&M process based on the O&M operations, and determining hidden states and hidden state sequences based on the O&M stages; constructing a hidden state set based on the hidden states and their sequences, and then determining the target model based on the hidden state set and the observation event set.

[0120] The processor can access information and applications stored in the memory via a transmission device to perform the following steps: predicting the next target operation and maintenance operation of the target object based on the state probability distribution, including: determining the sixth state with the highest probability based on the state probability distribution; determining the transition probability vector in the state transition matrix based on the sixth state; determining the seventh state based on the transition probability vector, wherein the seventh state is the state with the highest probability of the target object's next execution; and determining the target operation and maintenance operation based on the operation and maintenance operation corresponding to the seventh state.

[0121] The processor can access information and applications stored in the memory via a transmission device to execute the following steps: After predicting the next target operation and maintenance operation to be performed by the target object based on the state probability distribution, the method further includes: if the target operation and maintenance operation is a production release operation, querying the approval system based on the target object's operation and maintenance operation to obtain query results; if the query results contain approval information for the target object's operation and maintenance operation, retrieving operation guidance information from the operation and maintenance operation document based on the target operation and maintenance operation; if the query results do not contain approval information for the target object's operation and maintenance operation, intercepting the subsequent operation and maintenance operation performed by the target object and pushing a target prompt to the target object, wherein the target prompt is used to remind the target object to perform the approval operation.

[0122] This application provides a method for generating operation and maintenance guidance information. The method involves collecting operation and maintenance information of a target object at preset intervals to generate an operation sequence; calculating a state probability distribution based on a state transition matrix and the operation sequence, wherein the state transition matrix is ​​determined based on operation and maintenance documents and historical operation and maintenance operations; predicting the next target operation and maintenance operation to be performed by the target object based on the state probability distribution; retrieving operation and maintenance guidance information from the operation and maintenance documents based on the operation information of the target operation and maintenance operation; and then pushing the operation and maintenance guidance information to the target object. This solves the technical problem that traditional static documents or keyword matching algorithms cannot provide real-time operation and maintenance guidance to staff based on already performed operations.

[0123] By collecting operation and maintenance information of the target object at preset intervals and generating operation sequences, and calculating the state probability distribution based on the state transition matrix and this operation sequence, the operation and maintenance process can be monitored in real time, achieving the technical effect of dynamically capturing operation behavior. Simultaneously, based on probability theory, the current position of the target object in the operation and maintenance process can be accurately inferred, realizing intelligent identification of the operation process and further achieving the technical effect of accurately locating the operation stage. Based on the state probability distribution, the next operation and maintenance operation of the target object is predicted. By retrieving operation guidance information from the operation and maintenance document based on the predicted target operation and maintenance operation, and pushing it to the target object, operators can immediately receive detailed guidance for the next operation, achieving personalized operation guidance and further reducing operational errors and optimizing the operation and maintenance process.

[0124] Those skilled in the art will understand that Figure 5 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 5This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 5 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 5 The different configurations shown.

[0125] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0126] Example 4

[0127] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the method for generating operation and maintenance guidance information provided in Embodiment 1.

[0128] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0129] This application also provides a computer program product that, when executed on a data processing device, is suitable for performing the steps of a method for generating operation and maintenance guidance information.

[0130] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0131] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0133] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0134] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0135] 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 this application, 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 this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0136] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for generating operation and maintenance guidance information, characterized in that, include: After a preset time interval, collect the operation and maintenance information of the target object and generate an operation sequence; The state probability distribution is calculated based on the state transition matrix and the operation sequence, wherein the state transition matrix is ​​determined based on the operation and maintenance operation documents and historical operation and maintenance operations; Based on the state probability distribution, predict the next target operation and maintenance operation to be performed on the target object; Based on the operation information of the target operation, the operation document is searched to obtain operation guidance information, and the operation guidance information is pushed to the target object.

2. The method according to claim 1, characterized in that, Before collecting operation and maintenance information of the target object after each preset time interval and generating an operation sequence, the method further includes: Obtain the operation and maintenance documentation for the business system; Based on the Hidden Markov Model, a target model is generated according to the operation and maintenance document. The target model includes at least: a set of hidden states and a set of observed events. The hidden states in the set of hidden states represent the state of the operation and maintenance process executed by the target object, and the observed events in the set of observed events represent the operation and maintenance operations executed by the target object. Collect historical operation logs of the business system, and calculate the transition frequency between the hidden states based on the historical operation logs to obtain the state transition matrix; The transition frequency between the hidden state and the observed event is calculated based on the historical operation and maintenance logs to obtain the state probability matrix.

3. The method according to claim 2, characterized in that, Calculating the state probability distribution based on the state transition matrix and the operation sequence includes: A first probability distribution for transitioning from the first state at time t-1 to the second state at time t is determined based on the state transition matrix, wherein the first state and the second state both belong to the set of hidden states, and the first state and the second state are different, and t is a positive integer; Based on the state probability matrix, determine the second probability distribution of the occurrence of the first observed event when the second state is in time t, wherein the first observed event belongs to the set of observed events; Based on the operation sequence, calculate the third probability distribution of the state at time t-1 in the first state; The probability of the target object being in any hidden state in the next step is determined based on the first probability distribution, the second probability distribution, and the third probability distribution, thus obtaining the state probability distribution.

4. The method according to claim 2, characterized in that, The state transition matrix is ​​obtained by calculating the transition frequency between the hidden states based on the historical operation and maintenance logs, including: The historical operation and maintenance operations, as well as the execution order information of the historical operation and maintenance operations, are determined based on the historical operation and maintenance operation logs. The transition frequency from the fourth state to the fifth state is calculated based on the execution order information, wherein both the fourth state and the fifth state belong to the hidden state set, and the fourth state and the fifth state are different. The transition probability from the fourth state to the fifth state is calculated based on the transition frequency of the transition from the fourth state to the fifth state. The state transition matrix is ​​constructed based on the transition probability from the fourth state to the fifth state.

5. The method according to claim 2, characterized in that, Based on the Hidden Markov Model and the aforementioned operation and maintenance documents, a target model is generated, including: The operation and maintenance operations in the operation and maintenance process are extracted from the operation and maintenance operation document using artificial intelligence algorithms, as well as the execution order of the operation and maintenance operations. The set of observed events is constructed based on the operation and maintenance operations and their execution order. The operation and maintenance stage of the operation and maintenance process is determined based on the operation and maintenance operations in the operation and maintenance process, and the hidden state and the hidden state sequence are determined based on the operation and maintenance stage. The hidden state set is constructed based on the hidden state and the hidden state sequence, and the target model is determined based on the hidden state set and the observed event set.

6. The method according to claim 1, characterized in that, Based on the state probability distribution, predict the next target operation and maintenance operation to be performed on the target object, including: The sixth state with the highest probability is determined based on the aforementioned state probability distribution; The transition probability vector is determined in the state transition matrix based on the sixth state; The seventh state is determined based on the transition probability vector, wherein the seventh state is the state in which the target object has the highest probability of executing the next step; The target maintenance operation is determined based on the maintenance operation corresponding to the seventh state.

7. The method according to claim 1, characterized in that, After predicting the next target operation and maintenance operation to be performed on the target object based on the state probability distribution, the method further includes: If the target operation is a production release operation, the system is queried based on the operation of the target object to obtain the query results. When the query results contain approval information for the operation and maintenance operations of the target object, the step of retrieving the operation and maintenance operation document based on the target operation and maintenance operation to obtain operation guidance information is executed; When the query results do not contain approval information for the operation and maintenance operations of the target object, the subsequent operation and maintenance operations performed by the target object are intercepted, and a target prompt is pushed to the target object, wherein the target prompt is used to remind the target object to perform the approval operation.

8. A device for generating operation and maintenance guidance information, characterized in that, include: The data acquisition unit is used to collect operation and maintenance information of the target object after a preset time interval and generate an operation sequence. The first calculation unit is used to calculate the state probability distribution based on the state transition matrix and the operation sequence, wherein the state transition matrix is ​​determined based on the operation and maintenance operation document and historical operation and maintenance operations. The prediction unit is used to predict the next target operation and maintenance operation to be performed on the target object based on the state probability distribution; The first retrieval unit is used to search the operation and maintenance document based on the operation information of the target operation and maintenance, obtain operation guidance information, and push the operation guidance information to the target object.

9. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program executes the method for generating operation and maintenance guidance information according to any one of claims 1 to 7 when it runs.

10. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method for generating operation and maintenance guidance information as described in any one of claims 1 to 7.

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