Early warning processing and knowledge closed-loop method and device, electronic equipment and storage medium

Abstract

The invention provides an early warning processing and knowledge closed-loop method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a key field of early warning diagnosis information; matching is carried out in a fault knowledge base based on the key field to obtain at least one piece of successfully matched target knowledge, and the target knowledge comprises at least one treatment measure; generating a disposal work order based on the early warning diagnosis information and the target knowledge, and issuing the disposal work order to a corresponding disposal object; and obtaining feedback information, establishing new candidate knowledge based on the feedback information, and updating the new candidate knowledge to the fault knowledge base. Through an automatic means, potential risk points are found in the first time, corresponding emergency measures are started, event deterioration is avoided, expert experience and industry specifications are integrated into a fault knowledge base, a complete solution set is formed for operation and maintenance personnel to refer to and relieve the burden of the operation and maintenance personnel, common fault phenomena and processing methods are regularly sorted and summarized, and the operation and maintenance efficiency is improved. Content of the fault knowledge base is continuously improved, and an accurate data basis is provided for subsequent item processing.

Description

Technical Field

[0001] The present disclosure relates to the technical field of intelligent operation and maintenance of power generation equipment, and in particular to an early warning processing and knowledge closed-loop method, device, electronic equipment and storage medium. Background Art

[0002] The management of intelligent operation and maintenance of power generation equipment is a continuous cycle process, which requires continuous cycles of four stages: planning, execution, inspection and processing, to achieve efficient and reliable operation and maintenance management of power generation equipment, improve the availability and reliability of equipment, reduce operation and maintenance costs, and provide strong guarantees for the stable operation of power generation enterprises. The current intelligent operation and maintenance management of power generation equipment still requires a lot of manpower costs to maintain, so as to ensure that the current management can meet the needs of continuously updated power generation equipment and systems. Summary of the invention

[0003] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0004] To this end, one purpose of the present disclosure is to provide an early warning processing method.

[0005] The second objective of the present disclosure is to provide an early warning processing device.

[0006] A third objective of the present disclosure is to provide an electronic device.

[0007] A fourth object of the present disclosure is to provide a non-transitory computer-readable storage medium.

[0008] A fifth object of the present disclosure is to provide a computer program product.

[0009] To achieve the above-mentioned purpose, the first aspect of the present disclosure proposes an early warning processing method, including: after receiving early warning diagnosis information, obtaining key fields of the early warning diagnosis information; matching in a fault knowledge base based on the key fields to obtain at least one target knowledge that matches successfully, and the target knowledge includes at least one disposal measure; generating a disposal work order based on the early warning diagnosis information and the target knowledge, and issuing it to the corresponding disposal object; obtaining feedback information of the disposal object based on the disposal work order, and establishing new candidate knowledge based on the feedback information, and updating it to the fault knowledge base.

[0010] According to an embodiment of the present disclosure, generating a disposal work order based on the early warning diagnosis information and the target knowledge includes: generating a disposal suggestion based on the disposal measures of the target knowledge; and generating the disposal work order based on the disposal suggestion and the early warning diagnosis information.

[0011] According to one embodiment of the present disclosure, establishing new candidate knowledge based on the feedback information and updating it to the fault knowledge base includes: matching the candidate knowledge with all knowledge in the fault knowledge base to obtain a first matching value; in response to all of the first matching values ​​being less than a matching threshold, updating the candidate knowledge to the fault knowledge base.

[0012] According to an embodiment of the present disclosure, the method further includes: the method further includes: in response to the first matching value being greater than or equal to the matching threshold, no updating is performed.

[0013] According to one embodiment of the present disclosure, the key fields include the power plant name, the logical device identifier, and the early warning diagnosis information device, and the matching in the fault knowledge base based on the key fields to obtain at least one target knowledge that matches successfully includes: obtaining a preset matching pattern; matching the power plant name, the logical device identifier and the early warning diagnosis information device with the knowledge in the fault knowledge base according to the matching pattern to obtain a second matching value between the early warning diagnosis information and the knowledge in the fault knowledge base; and determining at least one target knowledge based on the second matching value.

[0014] According to one embodiment of the present disclosure, the matching is performed based on the power plant name, the logical device identifier and the early warning diagnosis information device with the knowledge in the fault knowledge base according to the matching pattern, including: in response to the matching pattern being an exact match, obtaining the early warning content in the early warning diagnosis information; matching is performed based on the early warning content, the power plant name, the logical device identifier, the early warning content and the early warning diagnosis information device with the knowledge in the fault knowledge base.

[0015] According to an embodiment of the present disclosure, the method further includes: in response to the matching mode being a full match, matching the knowledge in the fault knowledge base based on the power plant name, the logical device identifier and the early warning diagnosis information device.

[0016] To achieve the above-mentioned purpose, the second aspect embodiment of the present disclosure proposes an early warning processing device, including: an acquisition module, used to acquire the key fields of the early warning diagnosis information after receiving the early warning diagnosis information; a matching module, used to match in the fault knowledge base based on the key fields to obtain at least one target knowledge that matches successfully, and the target knowledge includes at least one disposal measure; a sending module, used to generate a disposal work order based on the early warning diagnosis information and the target knowledge, and send it to the corresponding disposal object; an updating module, used to acquire the feedback information of the disposal object based on the disposal work order, and establish new candidate knowledge based on the feedback information, and update it to the fault knowledge base.

[0017] To achieve the above-mentioned purpose, the third aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the early warning processing method as described in the first aspect embodiment of the present disclosure.

[0018] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the early warning processing method as described in the first aspect embodiment of the present disclosure.

[0019] To achieve the above-mentioned purpose, the fifth aspect of the present disclosure proposes a computer program product, including a computer program, which, when executed by a processor, is used to implement the early warning processing method as described in the first aspect of the present disclosure.

[0020] Therefore, through automated means, potential risk points can be discovered at the first time, and corresponding emergency measures can be initiated immediately to prevent the situation from worsening. At the same time, expert experience and industry standards can be integrated into the fault knowledge base to form a complete solution set for reference by operation and maintenance personnel to reduce their burden, regularly sort out and summarize common fault phenomena and treatment methods, continuously improve the content of the fault knowledge base, make preventive preparations in advance, reduce the probability of accidents, and provide an accurate data basis for subsequent matter handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic diagram of an early warning processing method according to an embodiment of the present disclosure;

[0022] Figure 2 is a schematic diagram of another early warning processing method according to an embodiment of the present disclosure;

[0023] Figure 3 is a schematic diagram of a work order according to one embodiment of the present disclosure;

[0024] Figure 4 is a schematic diagram of another early warning processing method according to an embodiment of the present disclosure;

[0025] Figure 5 is a schematic diagram of another early warning processing method according to an embodiment of the present disclosure;

[0026] Figure 6 is a schematic diagram of an early warning processing device according to one embodiment of the present disclosure;

[0027] Figure 7 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0029] The acquisition, storage, use, and processing of data in the technical solution disclosed in this disclosure are in compliance with the relevant provisions of relevant laws and regulations.

[0030] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0031] Figure 1 is a schematic diagram of an early warning processing method according to an embodiment of the present disclosure, such as Figure 1 As shown, the early warning processing method includes the following steps:

[0032] S101, after receiving the early warning diagnosis information, obtain the key fields of the early warning diagnosis information.

[0033] The early warning processing method of the embodiment of the present application can be applied to the scenario of intelligent operation and maintenance of power equipment. The executor of the early warning processing of the embodiment of the present application can be the early warning processing device of the embodiment of the present application, and the early warning processing device can be set on an electronic device.

[0034] It should be noted that the early warning diagnosis information in the embodiments of the present disclosure may be automatically generated by the early warning system.

[0035] In the disclosed embodiment, the early warning system may utilize intelligent sensors, online monitoring systems and other technical means to collect operating parameters of power generation equipment in real time, such as temperature, pressure, vibration, current, voltage, etc.

[0036] Analyze and process the collected data, evaluate the operating status of the equipment, and determine whether the equipment has potential failure risks.

[0037] Then, based on historical data and equipment operating status, data analysis and machine learning algorithms are used to predict the type and time of possible equipment failures.

[0038] Finally, early warning diagnostic information is generated based on the fault type, occurrence time, and faulty equipment.

[0039] It should be noted that the key fields in the embodiments of the present disclosure may be multiple, and no limitation is made here. For example, the key fields may be fault type, occurrence time, device identification, etc. For example, the key fields may be as shown in the following table:

[0040]

[0041]

[0042] S102, matching is performed in a fault knowledge base based on the key fields to obtain at least one successfully matched target knowledge, where the target knowledge includes at least one disposal measure.

[0043] It should be noted that the fault knowledge base in the embodiment of the present disclosure stores a database of knowledge points. Knowledge points are summaries and compilations of lessons learned and knowledge gained during equipment operation and maintenance, including different faults or maintenance items, and their corresponding treatments or implementation measures. For example, it can be shown in the following table:

[0044]

[0045] S103: Generate a disposal work order based on the early warning diagnosis information and target knowledge, and issue it to the corresponding disposal object.

[0046] It should be noted that there can be many types of disposal work orders, and no limitation is made here. It should be noted that the disposal work order can be equipment inspection and maintenance or fault diagnosis and repair, etc. Different types of disposal work orders may correspond to different disposal objects, which can be determined according to actual conditions.

[0047] The object of disposal can conduct regular inspections on the power generation equipment in accordance with the operation and maintenance plan and inspection standards to check whether the equipment's operating status, appearance, connection parts, etc. are normal.

[0048] Perform daily maintenance on the equipment, such as cleaning, lubrication, tightening, etc., to ensure that the equipment is in good operating condition.

[0049] Timely discover and handle problems that arise during equipment operation, such as abnormal vibration, temperature increase, leakage, etc.

[0050] When equipment fails, the person in charge can also use the intelligent diagnosis system and expert experience to quickly and accurately diagnose the type and cause of the failure based on the handling work order.

[0051] Develop reasonable fault repair plans, organize maintenance personnel to carry out repair work, and ensure that the equipment resumes normal operation as soon as possible.

[0052] S104, obtaining feedback information of the treatment object based on the completion of the treatment work order, and establishing new candidate knowledge based on the feedback information, and updating it into the fault knowledge base.

[0053] In the embodiments of the present disclosure, there are many methods for establishing new candidate knowledge based on feedback information, which are not limited here. For example, feedback information can be judged and established by experts to generate candidate knowledge. Alternatively, feedback information can also be processed by a generation model or a generation algorithm to establish new candidate knowledge.

[0054] In the disclosed embodiment, after receiving the early warning diagnosis information, the key fields of the early warning diagnosis information are obtained, and then the key fields are matched in the fault knowledge base to obtain at least one target knowledge that is successfully matched, and the target knowledge includes at least one disposal measure. Finally, a disposal work order is generated based on the early warning diagnosis information and the target knowledge, and issued to the corresponding disposal object. Thus, through automated means, potential risk points can be discovered at the first time, and corresponding emergency measures can be initiated immediately to avoid the deterioration of the situation. At the same time, expert experience and industry norms are integrated into the fault knowledge base to form a complete set of solutions for operation and maintenance personnel to refer to and reduce their burden. Common fault phenomena and treatment methods are regularly sorted out and summarized, and the content of the fault knowledge base is continuously improved. Precautionary preparations are made in advance to reduce the probability of accidents, and provide an accurate data basis for subsequent matters.

[0055] In the above embodiment, a disposal work order is generated based on the early warning diagnosis information and the target knowledge, and Figure 2 Explaining further, the method includes:

[0056] S201, generating treatment suggestions based on treatment measures of target knowledge.

[0057] It should be noted that one target knowledge may include multiple candidate treatment measures. In a possible implementation, multiple candidate treatment measures may be simultaneously written into the treatment suggestion.

[0058] In another possible implementation, the most appropriate one or more candidate disposal measures may be selected from multiple candidate disposal measures based on the current early warning diagnosis information and written into the disposal suggestion. There may be many methods for selecting the most appropriate one or more candidate disposal measures from multiple candidate disposal measures, and no limitation is made here. For example, the early warning diagnosis information and multiple candidate disposal measures may be processed by a selection model to select the most appropriate one or more candidate disposal measures from multiple candidate disposal measures. The selection model is trained in advance and can be stored in the storage space of the electronic device for easy retrieval when needed.

[0059] S202: Generate a disposal work order based on the disposal suggestion and the early warning diagnosis information.

[0060] In one possible implementation, the generated processing work order can be as follows: Figure 3 shown.

[0061] In the disclosed embodiment, a disposal suggestion is first generated based on the disposal measures of the target knowledge, and then a disposal work order is generated based on the disposal suggestion and the early warning diagnosis information. In this way, it is possible to utilize existing professional knowledge and technical experience to improve the quality and efficiency of decision-making, and at the same time establish a standardized workflow to facilitate subsequent tracking and review analysis.

[0062] In the above embodiment, new candidate knowledge is established based on the feedback information and updated to the fault knowledge base. Figure 4 Explaining further, the method includes:

[0063] S401, matching candidate knowledge with all knowledge in a fault knowledge base to obtain a first matching value.

[0064] In the embodiment of the present disclosure, there are many methods for obtaining the first matching value, which are not limited here. For example, the candidate knowledge and all the knowledge in the fault knowledge base can be calculated by a matching algorithm to calculate and obtain the first matching value. The matching algorithm is designed in advance and can be changed according to actual design needs, which is not limited here.

[0065] In another possible implementation, the candidate knowledge and all the knowledge in the fault knowledge base may be processed based on the matching model to obtain a first matching value of the candidate knowledge and all the knowledge in the fault knowledge base. The matching model is trained in advance and can be stored in the storage space of the electronic device to facilitate retrieval and use when needed.

[0066] S402: In response to all first matching values ​​being smaller than a matching threshold, updating the candidate knowledge into a fault knowledge base.

[0067] It should be noted that the matching threshold is a critical value for determining whether the candidate knowledge already exists in the fault knowledge base. The matching threshold is designed in advance and can be changed according to actual design needs. No limitation is made here.

[0068] When all the first matching values ​​are smaller than the matching threshold, it indicates that the current judgment candidate knowledge does not exist in the fault knowledge base.

[0069] S403: In response to the first matching value being greater than or equal to the matching threshold, no updating is performed.

[0070] In the disclosed embodiment, the candidate knowledge is first matched with all the knowledge in the fault knowledge base to obtain a first matching value, and in response to all the first matching values ​​being less than a matching threshold, the candidate knowledge is updated to the fault knowledge base, and in response to a first matching value being greater than or equal to the matching threshold, no update is performed. Thus, low-quality information sources can be effectively filtered out, and only high-quality content is retained, thereby improving the quality level of the fault knowledge base.

[0071] In the above embodiment, the key fields include the power plant name, the logical device identifier, and the early warning diagnosis information device. Based on the key fields, matching is performed in the fault knowledge base to obtain at least one target knowledge that matches successfully. Figure 5 Explaining further, the method includes:

[0072] S501, obtaining a preset matching mode.

[0073] In the embodiment of the present disclosure, the matching mode can be divided into exact matching and full matching.

[0074] Among them, exact match is more accurate than full match. It is suitable for matters with high matching requirements. Full match can obtain more search results and has a wider selection space.

[0075] S502, matching the knowledge in the fault knowledge base with the power plant name, the logical device identifier and the early warning diagnosis information device according to the matching mode to obtain a second matching value between the early warning diagnosis information and the knowledge in the fault knowledge base.

[0076] It should be noted that the matching data corresponding to different matching modes may be different, and no limitation is made here.

[0077] In response to the matching mode being an exact match, the warning content in the warning diagnosis information is obtained, and matching is performed based on the warning content, the power plant name, the logical device identifier, the warning content and the knowledge in the warning diagnosis information device and the fault knowledge base.

[0078] In response to the matching mode being a full match, a match is performed based on the power plant name, the logical device identifier, and the early warning diagnosis information device and the knowledge in the fault knowledge base.

[0079] S503: Determine at least one target knowledge based on the second matching value.

[0080] In the embodiment of the present disclosure, after obtaining the second matching value, the second matching value can be compared with the configuration threshold. When the second matching value is greater than the matching threshold, it means that the knowledge adaptation warning diagnosis information corresponding to the second matching value can be used as the target knowledge to generate corresponding disposal measures.

[0081] Corresponding to the warning processing methods provided in the above-mentioned embodiments, an embodiment of the present disclosure also provides a warning processing device. Since the warning processing device provided in the embodiment of the present disclosure corresponds to the warning processing methods provided in the above-mentioned embodiments, the implementation methods of the above-mentioned warning processing methods are also applicable to the warning processing device provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0082] Figure 6 is a schematic diagram of an early warning processing device according to an embodiment of the present disclosure. Figure 6 As shown, the warning processing device 600 includes: an acquisition module 610, a matching module 620, a sending module 630 and an updating module 640.

[0083] The acquisition module 610 is used to acquire key fields of the early warning diagnosis information after receiving the early warning diagnosis information.

[0084] The matching module 620 is used to perform matching in the fault knowledge base based on the key fields to obtain at least one successfully matched target knowledge, where the target knowledge includes at least one disposal measure.

[0085] The issuing module 630 is used to generate a treatment work order based on the early warning diagnosis information and the target knowledge, and issue it to the corresponding treatment object.

[0086] The updating module 640 is used to obtain feedback information of the treatment object based on the completion of the treatment work order, and to establish new candidate knowledge based on the feedback information and update it into the fault knowledge base.

[0087] In one embodiment of the present disclosure, the issuing module 630 is further used to: generate a disposal suggestion based on the disposal measures of the target knowledge; and generate a disposal work order based on the disposal suggestion and the early warning diagnosis information.

[0088] In one embodiment of the present disclosure, the updating module 640 is further used to: obtain feedback information of the treatment object based on the completion of the treatment work order; establish new candidate knowledge based on the feedback information, and update it into the fault knowledge base.

[0089] In one embodiment of the present disclosure, the update module 640 is also used to: match the candidate knowledge with all the knowledge in the fault knowledge base to obtain a first matching value; in response to all the first matching values ​​being less than a matching threshold, update the candidate knowledge to the fault knowledge base; or, in response to the existence of a first matching value greater than or equal to the matching threshold, not update.

[0090] In one embodiment of the present disclosure, the matching module 620 is also used to: obtain a preset matching pattern; match the knowledge in the fault knowledge base based on the power plant name, logical device identification and early warning diagnosis information equipment according to the matching pattern to obtain a second matching value of the early warning diagnosis information and the knowledge in the fault knowledge base; determine at least one target knowledge based on the second matching value.

[0091] In one embodiment of the present disclosure, the matching module 620 is also used to: in response to the matching mode being an exact match, obtain the warning content in the warning diagnosis information; and match the knowledge in the warning diagnosis information device and the fault knowledge base based on the warning content, power plant name, logical device identifier, warning content and warning diagnosis information.

[0092] In one embodiment of the present disclosure, the matching module 620 is further used to: in response to the matching mode being a full match, match the knowledge in the equipment and fault knowledge base based on the power plant name, logical device identification and early warning diagnosis information.

[0093] Therefore, through automated means, potential risk points can be discovered at the first time, and corresponding emergency measures can be initiated immediately to prevent the situation from worsening. At the same time, expert experience and industry standards can be integrated into the fault knowledge base to form a complete solution set for reference by operation and maintenance personnel to reduce their burden, regularly sort out and summarize common fault phenomena and treatment methods, continuously improve the content of the fault knowledge base, make preventive preparations in advance, reduce the probability of accidents, and provide an accurate data basis for subsequent matter handling.

[0094] In order to implement the above embodiment, the present disclosure further provides an electronic device 700, Figure 7 is a schematic diagram of an electronic device according to an embodiment of the present disclosure, such as Figure 7 As shown, the electronic device 700 includes: a processor 701 and a memory 702 that is communicatively connected to the processor, the memory 702 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 701 to implement the present disclosure. Figure 1-Figure 5 The early warning processing method of the embodiment.

[0095] In order to implement the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the above embodiments. Figure 1-Figure 5 The early warning processing method of the embodiment.

[0096] In order to implement the above embodiments, the present disclosure also provides a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the above embodiments. Figure 1-Figure 5 The early warning processing method of the embodiment.

[0097] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0098] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0099] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0100] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0101] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0102] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that contains, stores, communicates, propagates or transmits a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0103] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0104] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0105] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0106] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A warning processing method, characterized in that: include: After receiving the early warning diagnosis information, obtaining the key fields of the early warning diagnosis information; Matching in a fault knowledge base based on the key field to obtain at least one target knowledge that matches successfully, wherein the target knowledge includes at least one disposal measure; Generate a disposal work order based on the early warning diagnosis information and the target knowledge, and issue it to the corresponding disposal object; Acquire feedback information of the treatment object based on the completion of the treatment work order, establish new candidate knowledge based on the feedback information, and update it into the fault knowledge base.

2. The method according to claim 1, characterized in that The generating of a disposal work order based on the early warning diagnosis information and the target knowledge includes: generating a treatment suggestion based on the treatment measures of the target knowledge; The handling work order is generated based on the handling suggestion and the early warning diagnosis information.

3. The method according to claim 1, characterized in that The establishing of new candidate knowledge based on the feedback information and updating the new candidate knowledge to the fault knowledge base includes: Matching the candidate knowledge with all the knowledge in the fault knowledge base to obtain a first matching value; In response to all of the first matching values ​​being smaller than a matching threshold, the candidate knowledge is updated into the fault knowledge base.

4. The method according to claim 3, characterized in that The method further comprises: In response to there being a first matching value greater than or equal to the matching threshold, no updating is performed.

5. The method according to claim 1, characterized in that The key fields include the power plant name, the logical device identifier, and the early warning diagnosis information device. The matching in the fault knowledge base based on the key fields to obtain at least one target knowledge that matches successfully includes: Get the preset matching mode; Matching the power plant name, the logical device identifier and the early warning diagnosis information device with the knowledge in the fault knowledge base according to the matching mode to obtain a second matching value between the early warning diagnosis information and the knowledge in the fault knowledge base; At least one target knowledge is determined based on the second matching value.

6. The method according to claim 5, characterized in that The matching based on the power plant name, the logical device identifier and the early warning diagnosis information device with the knowledge in the fault knowledge base according to the matching mode includes: In response to the matching pattern being an exact match, obtaining warning content in the warning diagnosis information; Based on the warning content, the power plant name, the logical device identifier, the warning content and the warning diagnosis information device is matched with the knowledge in the fault knowledge base.

7. The method according to claim 1, characterized in that The method further comprises: In response to the matching mode being a full match, matching is performed based on the power plant name, the logical device identifier and the early warning diagnosis information device and the knowledge in the fault knowledge base.

8. An early warning processing device, characterized in that: include: An acquisition module, used for acquiring key fields of the early warning diagnosis information after receiving the early warning diagnosis information; A matching module, used for matching in a fault knowledge base based on the key field to obtain at least one target knowledge that matches successfully, wherein the target knowledge includes at least one disposal measure; A sending module, used for generating a disposal work order based on the early warning diagnosis information and the target knowledge, and sending it to a corresponding disposal object; An updating module is used to obtain feedback information of the treatment object based on the completion of the treatment work order, establish new candidate knowledge based on the feedback information, and update it into the fault knowledge base.

9. An electronic device, characterized in that: Including memory. processor; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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