Power plant accident information quality assessment method and device

Abstract

The invention provides a power plant accident information quality evaluation method and device, and relates to the technical field of power systems. The method comprises the steps of obtaining alarm event data of a current to-be-displayed target alarm event; acquiring at least one accident alarm record of the target power plant, wherein the historical processing feedback data comprises a processing operation type and an influence level for responding to the accident alarm; determining an accident type coding vector based on the accident type, the processing operation type and the influence level of the accident alarm record, and determining a position coding vector based on the target power plant, the position coding vector being used for indicating the difference between the target power plant and other power plants; according to the accident type coding vector and the position coding vector, determining historical event characteristics used for describing the behavior pattern of the target power plant; and associating the historical event characteristics with the alarm event data, generating an alarm importance score of the target alarm event, and displaying the target alarm event. The overall operation response efficiency can be effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and in particular to a power plant accident information quality evaluation method and device. BACKGROUND

[0002] In the prior art, the power system control platform mainly receives accident alarm records from various power plants. These records are usually in a structured or semi-structured form and contain brief information such as power plant, time, type, and basic parameter changes, which are used to trigger alarm prompts of the monitoring system and notify the operation personnel to intervene in processing. Specifically, the current processing mechanism relies on preset alarm thresholds and simple classification rules to judge the importance and urgency of the alarm in combination with artificial experience. However, this kind of method generally lacks the ability to deeply evaluate the actual impact of the event, and it is difficult to distinguish between low-impact events that do not need urgent intervention and serious abnormalities that really need to be focused on, although they trigger alarms. Therefore, the prior art cannot automatically identify and highlight high-impact events that really need attention, which makes it difficult for operation and maintenance personnel to focus on key issues in a large number of low-impact alarms, reducing the overall operation response efficiency. SUMMARY

[0003] To solve the above technical problems of the prior art, the present application provides a power plant accident information quality evaluation method and device, which solves the technical problem that it is difficult to focus on key issues in a large number of low-impact alarms due to the inability to automatically identify and highlight high-impact events that really need attention.

[0004] In a first aspect, the present application provides a power plant accident information quality evaluation method, comprising: obtaining alarm event data of a target alarm event to be displayed, wherein the alarm event data includes an alarm event type and a target power plant to which the target alarm event belongs; obtaining at least one accident alarm record of the target power plant, wherein the accident alarm record contains an accident type of an occurred accident alarm and historical processing feedback data, and the historical processing feedback data includes a processing operation type and an impact level in response to the accident alarm; determining an accident type code vector based on the accident type, the processing operation type, and the impact level of the accident alarm record, and determining a location code vector based on the target power plant, wherein the location code vector is used to indicate the difference between the target power plant and other power plants; determining a historical event feature for describing a behavior pattern of the target power plant according to the accident type code vector and the location code vector of the accident alarm record; associating the historical event feature with the alarm event data to generate an alarm importance score of the target alarm event; The target alarm event is displayed according to a priority mapped with the alarm importance score.

[0005] In some embodiments, the accident type code vector is determined based on the accident type, the handling operation type and the impact level of the accident alarm record, and a location code vector is determined based on the target power plant, including: The accident type, the handling operation type and the impact level of the accident alarm record are encoded to obtain an accident alarm fusion feature; The accident alarm fusion feature is converted into an accident type code vector through a preset first embedding model, and the accident type code vector is used to indicate the correlation between the accident type, the handling operation type and the impact level; The power plant identifier of the target power plant is encoded to obtain a power plant feature; The power plant feature is converted into a location code vector through a preset second embedding model.

[0006] In some embodiments, the accident alarm record further includes an alarm timestamp of the accident alarm; and the historical event feature used to describe the behavior pattern of the target power plant is determined according to the accident type code vector and the location code vector of the accident alarm record, including: The accident alarm records occurring in the target power plant are arranged in ascending order of the alarm timestamp to obtain an accident type code sequence of the target power plant; The accident type code sequence is subjected to time feature extraction by using a pre-constructed feature extraction model to obtain a forward hidden state and a backward hidden state, and the forward hidden state and the backward hidden state are spliced to obtain a context-aware feature, which is used for the forward and backward dependency relationship between the accident alarm records in the accident type code sequence; The location code vector and the context-aware feature are subjected to feature fusion to obtain a historical fusion feature of the accident alarm record; The time decay weight of the accident alarm record is determined based on the alarm timestamp of the accident alarm record, and the historical fusion feature and the time decay weight of each accident alarm record occurring in the target power plant are subjected to weighted calculation to obtain the historical event feature of the target power plant.

[0007] In some embodiments, the location code vector and the context-aware feature are subjected to feature fusion to obtain a historical fusion feature of the accident alarm record, including: The location importance bias vector is obtained by using a preset first multi-layer perception to strengthen the information of the location code vector; The position importance bias vector and the context-aware feature are spliced to obtain a spliced feature, and the spliced feature is processed by using a preset second multi-layer perception machine to obtain a history fusion feature; The time decay weight of the accident alarm record is determined based on the alarm timestamp of the accident alarm record, and the history fusion feature and the time decay weight of each accident alarm record occurring in the target power plant are calculated by weighting to obtain a history event feature of the target power plant, including: The time decay weight of the accident alarm record is determined based on the alarm timestamp of the accident alarm record, the occurrence timestamp of the target alarm event, and a preset time decay rate, the time decay weight is used to indicate the influence degree of the accident alarm record with a time difference value less than a preset time difference threshold on the target alarm event, and the time difference value is determined by the alarm timestamp of the accident alarm record and the occurrence timestamp; The time decay weight and the history fusion feature of any accident alarm record occurring in the target power plant are calculated to obtain a history event feature of the target power plant.

[0008] In some embodiments, the history event feature is associated with the alarm event data to generate an alarm importance score of the target alarm event, including: Based on the alarm event type of the target alarm event, an event type feature is determined, and a target power plant feature is determined based on the target power plant, and the event type feature and the target power plant feature are spliced into an event feature; The semantic similarity between the event feature and the history event feature is calculated, and the semantic similarity is used to indicate the relevance between the target alarm event and any accident alarm record; The semantic similarity associated with each accident alarm record is superimposed to obtain a target similarity, and the target similarity is processed by using a preset third multi-layer perception machine to obtain a context modulation factor; Based on the context modulation factor, the event feature, and the history event feature, an alarm importance feature of the target alarm event is determined; Based on a preset alarm importance weight matrix and a preset alarm importance bias vector, the alarm importance feature is mapped to an alarm importance score.

[0009] In a second aspect, the present application provides a power plant accident information quality evaluation device, including: An event data acquisition module is configured to acquire alarm event data of a target alarm event to be displayed, the alarm event data including an alarm event type and a target power plant to which the target alarm event belongs; a historical data acquisition module configured to acquire at least one accident alarm record of the target power plant, the accident alarm record comprising an accident type of an occurred accident alarm and historical processing feedback data, the historical processing feedback data including a processing operation type and an impact level in response to the accident alarm; a coding module configured to determine an accident type coding vector based on the accident type, the processing operation type and the impact level of the accident alarm record, and determine a location coding vector based on the target power plant, the location coding vector being used to indicate a difference between the target power plant and other power plants; a historical event feature extraction module configured to determine a historical event feature used to describe a behavior pattern of the target power plant according to the accident type coding vector and the location coding vector of the accident alarm record; an alarm importance evaluation module configured to associate the historical event feature with the alarm event data to generate an alarm importance score of the target alarm event; a display module configured to display the target alarm event according to a priority mapped with the alarm importance score.

[0010] The power plant accident information quality evaluation method and device provided by the application determine an accident type coding vector based on an accident type, a processing operation type and an impact level in an accident alarm record, fuse the accident type and historical processing feedback, determine a location coding vector based on a target power plant to which a target alarm event belongs, consider the difference between the target power plant and other power plants, then determine a historical event feature of the target power plant based on the accident type coding vector and the location coding vector, use the historical event feature as a historical experience benchmark to evaluate the target alarm event, improve the reliability of evaluating the target alarm event, associate the target alarm event with the historical event feature to generate an alarm importance score of the target alarm event, and then display the target alarm event according to the alarm importance score, improve the identification ability of a real high-impact event, and improve the operation response efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 A flowchart of a power plant accident information quality evaluation method provided by the application; Figure 2 A structural diagram of a power plant accident information quality evaluation device provided by the application. DETAILED DESCRIPTION

[0012] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work belong to the protection scope of the present application.

[0013] As shown in Figure 1 The present application provides a power plant accident information quality evaluation method, which comprises steps S101 to S106.

[0014] S101, obtaining alarm event data of a target alarm event to be displayed, the alarm event data comprising an alarm event type and a target power plant to which the target alarm event belongs.

[0015] In the present application, the target alarm event refers to an alarm to be disposed triggered by real-time monitoring data of a power plant, which reflects abnormality of equipment of the power plant or existing security risks, for example, abnormal boiler water level, abnormal transformer temperature, etc. The alarm event type includes but is not limited to equipment failure type, parameter out-of-limit type, environmental abnormality type, etc. The target power plant represents a specific power plant where the target alarm event occurs.

[0016] S102, obtaining at least one accident alarm record of the target power plant, the accident alarm record containing an accident type of an occurred accident alarm and historical processing feedback data, the historical processing feedback data comprising a processing operation type and an impact level in response to the accident alarm.

[0017] In the present application, the accident alarm record represents archived data of an accident event occurred in the target power plant and completed disposal. The accident type represents a category of the accident alarm occurred in the power plant in history, such as equipment short circuit, bearing temperature exceeding standard, valve sticking, power grid frequency fluctuation, etc., to distinguish different nature faults or alarms. It should be noted that the accident type of the accident alarm record extracted in the present application corresponds to the alarm event type of the target alarm event. For example, the target alarm event is transformer temperature out-of-limit, then the record of "transformer temperature related accident" occurred in the power plant needs to be collected. Specifically, the historical processing feedback data represents records of the disposal process and disposal result of the historical accident. The processing operation type represents an operation type of the disposal action actually performed for the accident alarm, for example, emergency shutdown for maintenance, remote parameter adjustment and no abnormality after on-site troubleshooting. The impact level represents the impact degree actually caused by the historical accident alarm, i.e. the actual consequence of the historical accident alarm.

[0018] Therefore, the application introduces the accident alarm record of the target power plant, combines the historical disposal feedback of the power plant itself, makes the evaluation of the target alarm event consistent with the actual operation of the power plant, improves the accuracy of the event evaluation, realizes the differentiation of the alarm event with high actual influence degree, and can effectively improve the operation efficiency.

[0019] In S103, an accident type code vector is determined based on the accident type, the processing operation type and the influence level of the accident alarm record, and a location code vector is determined based on the target power plant. The location code vector is used to indicate the difference between the target power plant and other power plants.

[0020] In the application, the accident type, the processing operation type and the influence level are encoded and then converted into vectors to obtain an accident type code vector. The accident type code vector reflects the quantitative characteristics (i.e., data fusion characteristics) of the accident type, the disposal method and the influence degree of the accident alarm, that is, reflects the properties of the accident itself. In addition, the difference characteristics (such as the unit type, the installed capacity, the geographical location and the historical accident frequency) of the power plant are converted into vectors based on the power plant identifier of the target power plant to obtain a location code vector. The location code vector represents the difference between the target power plant and other power plants, that is, the difference between different power plants in physical characteristics, operation scale and region. The location code vector reflects the characteristics of the power plant scene properties.

[0021] In some embodiments, determining the accident type code vector based on the accident type, the processing operation type and the influence level of the accident alarm record and determining the location code vector based on the target power plant comprises: encoding the accident type, the processing operation type and the influence level of the accident alarm record to obtain an accident alarm fusion feature; converting the accident alarm fusion feature into an accident type code vector through a preset first embedding model. The accident type code vector is used to indicate the correlation between the accident type, the processing operation type and the influence level; encoding the power plant identifier of the target power plant to obtain a power plant feature; converting the power plant feature into a location code vector through a preset second embedding model.

[0022] In this embodiment, the accident type, the processing operation type and the influence level of the accident alarm record are converted into numerical form to obtain an accident alarm fusion feature. The encoding method can be one-hot encoding. Then, the first embedding model (Embedding) is used to convert the accident alarm fusion feature into an accident type code vector representing a continuous vector, which integrates the accident itself and the historical disposal feedback and improves the evaluation accuracy.

[0023] In addition, a power plant identifier of the target power plant is encoded to obtain a power plant feature, and the encoding manner can be one-hot encoding, and the power plant feature is used to identify identity information of the power plant. The power plant identifier is used to uniquely distinguish symbols or strings of different power plants, for example, "power plant A01", "hydropower plant 05", and the like. Then, the one-hot encoding of the power plant identifier is converted into a continuous vector by a second embedding model (Embedding) to obtain a location encoding vector, which reflects physical characteristics, operation scale, regional differences and the like of the power plant, and is used to distinguish characteristics of different power plants.

[0024] For example, the accident type encoding vector is calculated by the following formula: ; Wherein, represents an accident type encoding vector of the i th accident alarm record, represents an accident type of the i th accident alarm record, represents a processing operation type of the i th accident alarm record, represents an impact level of the i th accident alarm record.

[0025] The location encoding vector is calculated by the following formula: ; Wherein, represents a location encoding vector of the i th accident alarm record, represents a power plant to which the i th accident alarm record belongs.

[0026] Therefore, the embodiment realizes the conversion of the power plant difference into a calculable vector difference. In the power plant accident information quality evaluation, the importance of the same accident type alarm in different power plants may be different (for example, "fan failure" is more important in a wind power-based power plant than in a thermal power-based power plant), and the difference between different power plants (such as equipment characteristics and operating environment difference) is reflected by the location encoding vector, thereby improving the accuracy of the current alarm event evaluation.

[0027] S104, according to the accident type encoding vector and the location encoding vector of the accident alarm record, determining a historical event feature used to describe a behavior mode of the target power plant.

[0028] In the present application, the accident type encoding vector and the location encoding vector are subjected to feature extraction to obtain a historical event feature of the target power plant, which reflects the experience law of the accident occurrence frequency, the disposal habit and the impact consequence of the power plant, that is, the behavior mode of the power plant. The behavior mode represents the event-response-consequence correlation mode formed by the power plant in the accident disposal, and embodies the equipment characteristics and the operation and maintenance habit of the power plant.

[0029] Therefore, the embodiment converts the accident alarm record into a behavior mode by determining the historical event feature for describing the behavior mode of the target power plant, uses the behavior mode and rule of the power plant to evaluate the importance of the current alarm event, and improves the reliability of the importance evaluation.

[0030] S105, associate the historical event feature with the alarm event data to generate an alarm importance score of the target alarm event.

[0031] In the present application, the historical event feature is matched with the alarm event data, and the corresponding alarm importance score is generated based on the matching result, so as to improve the objectivity and operability of the evaluation by associating the historical rule.

[0032] S106, display the target alarm event according to the priority mapped with the alarm importance score.

[0033] It should be noted that the corresponding rule of the alarm importance score and the priority is configured, so that the target alarm event is displayed differently according to the mapped priority. The priority represents the classification of the emergency order of the current alarm based on the alarm importance score, and directly guides the response priority. Therefore, the present application differentiates the display of the alarm event through the priority, identifies the alarm event with high emergency importance (i.e. high impact), and improves the disposal efficiency.

[0034] In some embodiments, the accident alarm record further includes an alarm timestamp of the accident alarm; and the historical event feature for describing the behavior mode of the target power plant is determined according to the accident type coding vector and the position coding vector of the accident alarm record, including: arranging the accident alarm records occurring in the target power plant in ascending order of the alarm timestamp to obtain an accident type coding sequence of the target power plant; using a pre-constructed feature extraction model to perform time feature extraction on the accident type coding sequence to obtain a forward hidden state and a backward hidden state, and splicing the forward hidden state and the backward hidden state to obtain a context-aware feature, the context-aware feature being used for the forward and backward dependency relationship between the accident alarm records in the accident type coding sequence; performing feature fusion on the position coding vector and the context-aware feature to obtain a historical fusion feature of the accident alarm record; determining a time decay weight of the accident alarm record based on the alarm timestamp of the accident alarm record, and performing weighted calculation on the historical fusion feature and the time decay weight of each accident alarm record occurring in the target power plant to obtain the historical event feature of the target power plant.

[0035] In the embodiment, the alarm timestamp represents the time record when the accident alarm occurs, and then the accident alarm records are sorted in the order of time from the past to the present. The accident type code sequence represents a one-dimensional ordered array composed of the accident type code vectors of the target power plant in chronological order. Then, the feature extraction model refers to a bidirectional recurrent neural network (BiRNN) and its variants (such as BiLSTM, BiGRU) for processing time series data, aiming to capture the forward and backward dependencies of the elements in the accident type code sequence. Then, the forward hidden state is obtained by using the feature extraction model, specifically, the feature vector output by the feature extraction model at each time step when traversing the sequence in the direction of “time from early to late”, which contains the information of all accidents before this time step. At the same time, the backward hidden state is obtained by using the feature extraction model, specifically, the feature vector output by the feature extraction model at each time step when traversing the sequence in the direction of “time from late to early”, which contains the information of all accidents after this time step. Thus, by concatenating the forward hidden state and the backward hidden state, the context-aware feature is obtained, which reflects the bidirectional dependency between accident alarms, for example, after the event of high boiler pressure alarm, the event of safety valve action alarm often occurs, and at the same time, the event of safety valve action alarm also confirms the authenticity of the event of high boiler pressure alarm.

[0036] Further, by feature fusion of the position encoding vector and the context-aware feature, the position-enhanced context-aware feature is obtained, which realizes the fusion of the individualized attributes of the target power plant (i.e. power plant difference) and the time correlation logic of the target power plant accident (i.e. accident time correlation), and improves the adaptability of the feature to the power plant scene.

[0037] Secondly, according to the difference between the alarm timestamp of each accident alarm record and the occurrence time of the current target alarm event, the time decay weight of the accident alarm record is calculated by a preset decay function (such as exponential decay, linear decay). Exemplarily, the time decay weight indicates that the closer the accident, the higher the weight; the farther the accident, the lower the weight.

[0038] Further, the historical fusion features (i.e. position-enhanced context-aware features) of all accident alarm records of the target power plant are summed with their respective time decay weights to obtain the historical event features representing the overall accident behavior pattern of the target power plant.

[0039] Therefore, the embodiment constructs an accident type coding sequence, extracts context-aware features from the accident type coding sequence, fuses the location coding vector and the context-aware features to obtain historical fusion features of the accident alarm record, determines a time decay weight according to an alarm timestamp of the accident alarm record, and obtains historical event features of the target power plant by the historical fusion features and the time decay weight, thereby strengthening the timeliness of the features, making the historical fusion features fit the current state of the target power plant, improving the accuracy of the historical event features, and providing the accuracy of the alarm event evaluation.

[0040] In some embodiments, the fusing the location coding vector and the context-aware features to obtain the historical fusion features of the accident alarm record comprises: performing information strengthening on the location coding vector by using a preset first multi-layer perception machine to obtain a location importance bias vector; splicing the location importance bias vector and the context-aware features to obtain spliced features, and processing the spliced features by using a preset second multi-layer perception machine to obtain the historical fusion features.

[0041] In the embodiment, the first multi-layer perception machine is used to strengthen key information related to accident evaluation in the location coding to obtain the location importance bias vector. The location importance bias vector can highlight power plant difference information that has an impact on accident importance score, and weaken irrelevant differences (such as non-functional differences of power plant names and geographical positions). Then, the location importance bias vector and the context-aware features are spliced to obtain spliced features, and integration between power plant differences and time-dependent features is realized. The second multi-layer perception machine is used to process the spliced features to extract the correlation between the features, and the historical fusion features reflect the fusion of power plant differentiated information and historical accident time-dependent information.

[0042] For example, the location importance bias vector is determined by the following formula: ; wherein, represents the location importance bias vector of the i th accident alarm record.

[0043] The historical fusion features are determined by the following formula: ; wherein, represents the historical fusion features of the i th accident alarm record, represents the context-aware features, and k represents the number of accident alarm records. represents a splicing operation.

[0044] Therefore, the embodiment realizes highlighting the power plant difference information that has an impact on the accident importance score, while weakening irrelevant differences, avoids irrelevant information in the power plant identification interfering with accident assessment, and focuses on the difference characteristics (such as device configuration and operation and maintenance capability) related to accident handling and impact level. Meanwhile, it is ensured that the historical accident characteristics of different power plants can reflect their own accident occurrence rules when being fused. By determining the historical fusion characteristics and highlighting the power plant differentiation and time-dependent characteristics, the accident recurrence risk of the power plant can be accurately reflected, the effective correlation is strengthened, and the accuracy of the alarm event assessment is improved.

[0045] In some embodiments, the time decay weight of the accident alarm record is determined based on the alarm timestamp of the accident alarm record, and the historical fusion characteristics of each accident alarm record occurring in the target power plant and the time decay weight are weighted and calculated to obtain the historical event characteristics of the target power plant, including: The time decay weight of the accident alarm record is determined based on the alarm timestamp of the accident alarm record, the occurrence timestamp of the target alarm event, and a preset time decay rate. The time decay weight is used to indicate the influence degree of the accident alarm record with a time difference value less than a preset time difference threshold on the target alarm event, and the time difference value is determined by the alarm timestamp of the accident alarm record and the occurrence timestamp. The time decay weight and the historical fusion characteristics of any accident alarm record occurring in the target power plant are calculated to obtain the historical event characteristics of the target power plant.

[0046] In the embodiment, the time decay weight of the accident alarm record is determined based on the alarm timestamp of the accident alarm record, the occurrence timestamp of the target alarm event, and the time decay rate, and the reference value of the historical accident to the current alarm event is considered.

[0047] For example, the time decay weight is determined by the following formula: ; wherein, the time decay weight of the i th accident alarm record is represented by w i, the time decay rate is represented by r, the alarm timestamp of the i th accident alarm record is represented by t i, the occurrence timestamp of the target alarm event is represented by t, the serial number of the target alarm event is represented by n.

[0048] The historical event characteristics are determined by the following formula: ; wherein, represents the historical event feature of the target power plant, k represents the number of accident alarm records.

[0049] Therefore, the embodiment obtains the historical event feature of the target power plant through weighted calculation of the historical fusion feature and the time decay weight, ensures that the recent and highly correlated historical accident information accounts for a higher proportion in the final feature, and can better reflect the potential risk of the current alarm. At the same time, the embodiment quantifies the accident occurrence regularity of the target power plant, provides historical experience and behavior regularity for the evaluation of the current alarm event, and improves the accuracy of the score.

[0050] Therefore, in the present application, the accident type coding sequence of the target power plant is constructed, the accident alarm records of the power plant are arranged in ascending order of timestamp, the context-aware feature is generated by using the bidirectional long short-term memory network to deeply mine the context dependence relationship of the accident type coding sequence, the position-enhanced context feature is formed by using the position importance bias vector to perform nonlinear mapping on the position coding vector to fuse the spatial context information. At the same time, the embodiment dynamically weights the historical accident alarms by using the time decay weight, so that the recent events obtain a higher weight, and finally obtains the historical event feature through weighted aggregation. In this way, the embodiment can accurately evaluate the alarm quality because the disposal value of the power plant alarm is highly dependent on time sensitivity, that is, for the same power plant, the same type of event occurring recently is more likely to reflect the current operation hidden danger. The embodiment automatically suppresses the interference of obsolete events by using the time decay weight, strengthens the contribution of key historical segments, so that the historical event feature can not only reflect the cumulative effect of accidents at the same position, but also distinguish the operation characteristics of different power plants, thereby filtering out inert alarms that trigger alarms but actually have decreasing impact, and significantly improving the recognition ability of real high-impact events.

[0051] In some embodiments, the association of the historical event feature with the alarm event data generates an alarm importance score of the target alarm event, including: determining an event type feature based on the alarm event type of the target alarm event, and determining a target power plant feature based on the target power plant, and splicing the event type feature and the target power plant feature into an event feature; calculating a semantic similarity between the event feature and the historical event feature, the semantic similarity being used to indicate the relevance between the target alarm event and any accident alarm record; superimposing the semantic similarity associated with each accident alarm record to obtain a target similarity, and processing the target similarity by using a preset third multilayer perception to obtain a context modulation factor; determining an alarm importance feature of the target alarm event based on the context modulation factor, the event feature and the historical event feature; Based on a preset alarm importance weight matrix and a preset alarm importance bias vector, the alarm importance features are mapped to alarm importance scores.

[0052] For example, event characteristics can be determined using the following formula: ; in, Indicates the first The event characteristics of an alarm event (i.e., the target alarm event). Indicates the first Event type characteristics of each alarm event Indicates the first The characteristics of the target power plant for each alarm event.

[0053] The importance characteristics of alarms are determined using the following formula: ; in, Indicates the first The alarm importance characteristics of each alarm event. This represents the context modulation factor.

[0054] The alarm importance score is determined using the following formula: ; in, Indicates the first Alarm importance score for each alarm event This represents the alarm importance weight matrix. This represents the alarm importance bias vector.

[0055] In this embodiment, by constructing alarm importance features, the alarm event type and location encoding vector of the current alarm event are fused into event features, and semantic similarity is calculated with historical event features to capture the degree of matching between the event and the power plant's historical behavior patterns. Then, through context modulation gain, the accumulated similarity result is non-linearly mapped to generate a context modulation factor. This context modulation factor is used to fuse historical event features with current event features, improving the accuracy of feature fusion. Finally, the context modulation factor is multiplied by the event features and then superimposed with historical event features to form the alarm importance features.

[0056] It should be noted that, since the actual influence of the power plant alarm is often implied in the historical experience of historical handling feedback, when a certain type of alarm frequently occurs but the historical handling is marked as low influence, the semantic similarity will be significantly high, and the context modulation factor will automatically suppress the feature weight of such event, avoiding misjudgment of inertial alarm as high risk; on the contrary, if the event is highly deviated from the historical high-influence mode, the similarity is reduced, and the context modulation factor strengthens the feature expression, ensuring that the real anomaly obtains a higher score. Therefore, by using the context modulation factor, the event feature and the historical event feature, the alarm importance feature of the target alarm event is determined, which can accurately distinguish the alarm value. Secondly, the semantic similarity is used to quantify the historical experience, and the feature is adaptively modulated by dynamic gain, which not only avoids isolated judgment of a single event, but also overcomes the noise interference of historical data, so that the alarm importance feature can truly reflect the actual value of the event in the power plant operation process, thereby filtering low-influence alarms and significantly improving the discrimination accuracy and decision reliability of the evaluation result.

[0057] In some embodiments, the method further comprises: obtaining an operation condition and a topology structure of the target power plant, wherein the operation condition comprises a power plant overall operation state and a device operation state and a device association state of a plurality of devices in the target power plant, the association state is used to indicate a concurrent alarm event and association device data of an associated device to which the concurrent alarm event belongs, which occurs simultaneously with the target alarm event, and the power plant overall operation state comprises a regional power grid load level and a scheduling requirement of a region where the target power plant is located, and the topology structure is used to indicate a connection relationship between the devices; determining a first influence degree based on the operation condition and the topology structure, the first influence degree being used to indicate a correlation between the target alarm event, a processing device and the associated device; matching an accident alarm record and an influence level related to an alarm event type of the target alarm event; determining a second influence degree based on the device operation state, the device association state and the power plant overall operation state, the second influence degree being used to indicate an influence degree of the operation condition on the target alarm event; determining a real-time influence factor based on the first influence degree and the second influence degree, and determining a historical correlation coefficient according to the real-time influence factor and the matched influence level; correcting a priority corresponding to the alarm importance score according to the first influence degree, the second influence degree, the influence level and the historical correlation coefficient, to obtain an execution priority, and indicating processing of the target alarm event according to the execution priority.

[0058] In the embodiment, the operating condition represents a set of current dynamic operating states of the power plant and the equipment, and the embodiment reflects the current risk environment through the real-time condition. The topology represents the physical connection and functional dependence relationship between the equipment, which is used to determine whether the failure of a certain equipment will affect other equipment or the entire system. For example, the overall operating state of the power plant can be the load rate of the power plant (full load / half load / low valley), whether it is in the start-stop stage, whether it is executing a power protection task, etc. The regional power grid load level represents the overall electricity demand of the region where the target power plant is located (such as summer peak load and deep night valley load), reflecting the power supply pressure of the power plant, that is, the alarm processing needs to be more urgent during the peak period to avoid affecting the regional power supply. The equipment operating state can be the real-time parameters of the equipment and associated equipment involved in the target alarm event (such as temperature, pressure, current, vibration value, and whether it is supercritical threshold). The equipment association state can be a concurrent alarm event (indicating a possible chain failure) that occurs simultaneously with the target alarm event, for example, the "boiler water level abnormality" event is accompanied by the "low pressure of feed water pump" event, and the real-time data of the associated equipment (such as the flow and speed of the feed water pump).

[0059] Then, by the association relationship between the equipment, the concurrent alarm relationship in the operating condition, and the physical connection relationship in the topology, the correlation between the target alarm event, the processing equipment, and the associated equipment is calculated, and the first influence degree is determined, that is, the equipment importance, the connection tightness, and the number of concurrent alarms. Further, by the equipment operating state in the operating condition, the overall state of the power plant, and the power grid state, the risk degree of the operating condition to the target alarm event is calculated, that is, the direct influence of the real-time operating condition on the target alarm risk, and the risk change caused by the difference in the operating condition is realized.

[0060] Further, the real-time influence factor represents the quantification of the equipment association risk and the operating condition risk of the current scene. By quantifying the association strength between the real-time influence factor and the influence level, the historical correlation coefficient is determined, which reflects the reference value of the influence level in the history to the current real-time scene. Therefore, by combining the first influence degree, the second influence degree, the influence level, and the historical correlation coefficient, the priority is corrected, and the execution priority is obtained to process the current alarm event.

[0061] Therefore, the embodiment combines historical alarm experience with real-time operating environment, avoids the situation that only relying on historical experience cannot cope with real-time dynamic scene, adapts to the execution scene, improves the alarm processing efficiency, and meets the actual needs of power plant operation and maintenance.

[0062] In some embodiments, the first influence degree is determined based on the operating condition and the topology, including: determine an event influence degree of the target alarm event based on the topology structure and the operation condition, wherein the event influence degree comprises an event influence range and an event influence time, the event influence range is used to indicate at least one level of equipment, the number of equipment and the first equipment correlation between the equipment affected by the target alarm event, and the event influence time is used to indicate the time length between the occurrence time stamp of the target alarm event and the time when the target alarm event is completed; determine a device influence degree of the processing equipment based on the topology structure, the device operation state and the device correlation state, wherein the device influence degree comprises a device influence time of the processing equipment, a device processing operation difficulty, a device importance degree and a second device correlation between the processing equipment, and the device influence time is used to indicate the time length between the time when the processing equipment is abnormal due to the influence of the target alarm event and the time when the processing equipment recovers to normal; determine a concurrent influence degree of the concurrent alarm events based on the topology structure and the device correlation state, wherein the concurrent influence degree comprises the number of the concurrent alarm events, a concurrent event processing difficulty, a concurrent event importance degree, an event correlation degree between the concurrent alarm events and the correlation device data of the corresponding correlation equipment, the concurrent event processing difficulty is used to indicate the correlation equipment operation difficulty of the correlation equipment involved in processing the concurrent alarm events, and the concurrent event importance degree is used to indicate the importance of the concurrent alarm events compared with the target power plant and the target alarm event; obtain a first influence degree based on the event influence degree, the device influence degree and the concurrent influence degree.

[0063] In the embodiment, the event influence degree represents the quantitative description of the overall influence scale of the target alarm event, reflects how large the influence range is and how long the influence lasts, and is used to judge whether the alarm needs emergency response. Wherein, the equipment of the target alarm event is located based on the topology structure, the upstream and downstream equipment levels are traced along the topology link, and the affected equipment level and the number of equipment are determined. The equipment level represents the equipment grade divided according to the topology importance (such as the first level equipment is the core production equipment, the second level equipment is the auxiliary equipment, and the third level equipment is the auxiliary facility), or the equipment grade according to the functional causal relationship of the equipment. The first equipment correlation represents the functional dependence strength between the affected processing equipment, and identifies the global influence of the event level. The event influence time represents the total time length from the occurrence of the target alarm event to the completion of the processing of the target alarm event, and directly reflects the interference period of the alarm to the continuous operation of the power plant. Therefore, by determining the event influence degree, the influence quantification of the equipment structure, the equipment level, the number of equipment and the equipment correlation is considered.

[0064] Then, the device influence time indicates the time length from the abnormality (triggered by the alarm) to the recovery of the processing device; the device processing operation difficulty indicates the technical complexity and operation risk required for processing the device, which can be determined by the complexity of the processing device, the operation process and the historical operation success rate; the device importance is determined by judging the irreplaceability of the processing device in the production link of the power plant based on the topology structure; the second device correlation indicates the cooperative dependence strength between multiple processing devices, and high correlation means that the operation needs to be strictly synchronized, otherwise it is easy to cause new risks. Therefore, the embodiment determines the device influence degree, determines the importance of the processing device, identifies the potential problems of multi-device cooperation, so as to reduce the risk in the disposal process.

[0065] Further, the concurrent influence degree represents the chain failure risk caused by the concurrent alarms occurring at the same time as the target alarm. The concurrent alarm event represents other alarms overlapping in time with the target alarm event, which may be the result of the target alarm event (such as abnormal boiler water level causing low feed pump pressure) or the same source (such as power grid fluctuation causing voltage abnormality and current fluctuation). The concurrent event processing difficulty represents the overall operation complexity required to dispose all concurrent alarms. The concurrent event importance can be determined by combining the topology structure to determine the device level to which the concurrent alarm belongs, and compared with the importance of the current alarm event. The event correlation degree represents the system belonging consistency and causal correlation between concurrent alarms and between concurrent alarms and the current target alarm event, to determine whether it is a chain failure. Therefore, the embodiment determines the concurrent influence degree to realize the quantification of the pressure of multi-alarm cooperative disposal and improve the identification of the chain failure risk caused by the current alarm event.

[0066] Therefore, the embodiment obtains the first influence degree through the event influence degree, the device influence degree and the concurrent influence degree, which reflects the correlation and influence degree between the global influence of the event, the processing device risk and the concurrent chain risk, ensures that the priority correction is more consistent with the actual device operation and related logic of the power plant, and improves the accuracy of the execution priority of the subsequent current alarm event.

[0067] In some embodiments, the device operating state includes a working condition state and a load rate; and the second influence degree is determined based on the device operating state, the device correlation state and the overall operating state of the power plant, including: determining a device risk coefficient based on the device running state when the working condition state is detected to be not in a specified critical working condition and the load rate is in a preset normal load rate range, wherein the critical working condition is a peak load stage or a start-stop stage; or, increasing the device risk coefficient based on the device running state when the working condition state is detected to be in the critical working condition and the load rate exceeds the normal load rate range; or, decreasing the device risk coefficient based on the device running state when the working condition state is detected to be in a shutdown state or a standby state; determining a concurrent correlation coefficient based on the device correlation state when the number of concurrent alarm events is detected to be less than a preset first number threshold and the event correlation degree is detected to be less than a preset correlation degree threshold; or, increasing the concurrent correlation coefficient based on the device correlation state when the number of concurrent alarm events is detected to be greater than the first number threshold and the event correlation degree is detected to be greater than the correlation degree threshold; or, increasing the increased concurrent correlation coefficient based on the device correlation state when the number of concurrent alarm events is detected to be greater than a preset second number threshold or the event correlation degree reaches a preset cross-system alarm condition, wherein the second number threshold is greater than the first number threshold; determining a power grid constraint coefficient based on the overall power plant running state when the regional power grid load level is detected to be in a flat load level or a valley load level, wherein the power grid constraint coefficient is used to indicate an emergency degree of processing the target alarm event; or, increasing the power grid constraint coefficient based on the overall power plant running state when the regional power grid load level is detected to be in a peak load level; determining a second influence degree based on the device risk coefficient, the concurrent correlation coefficient and the power grid constraint coefficient.

[0068] In the embodiment, when in a non-critical working condition and a normal load, a device risk coefficient can be calculated based on a current parameter of the device, such as a temperature or a vibration value deviating from a normal threshold. Then, when in a critical working condition (i.e., the device is in a peak load stage or a start-stop stage (the device parameter fluctuates greatly), such as a summer peak load stage in which the device runs at full load), the device risk coefficient is increased, because the fault tolerance of the device is low in the critical working condition, and an abnormal load easily causes a fault. In addition, when in a shutdown state (the device has exited running) or a standby state (the device does not bear an actual load), the device risk coefficient is decreased, because the device has little impact on production at this time and the risk is controllable. Therefore, by increasing / decreasing the device risk coefficient, real-time risk is reflected and consumption of invalid disposal resources is reduced.

[0069] Further, if the number of concurrent alarms is less than a first number threshold (e.g., less than 3), and the event correlation degree is less than a correlation degree threshold (i.e., the lower the value, the weaker the correlation with the target alarm or other concurrent alarms), it is considered a low chain risk scenario, and the basic concurrent correlation coefficient can be calculated according to the average importance of the concurrent alarms. Then, if the number of concurrent alarms is greater than the first number threshold (e.g., greater than 3), and the event correlation degree is greater than the correlation degree threshold, it is considered a medium chain risk scenario, and the correlation coefficient is increased based on the basic coefficient, because the concurrent alarms that are many and correlated may indicate a potential failure of the same system. Further, if the number of concurrent alarms is greater than a second number threshold (e.g., greater than 5, and the second threshold is greater than the first threshold), or the event correlation degree meets the cross-system alarm condition (e.g., the concurrent alarms involve the water supply system and the electrical system, breaking the single system boundary), it is considered a high chain risk scenario, and the adjustment amplitude is further increased based on the medium risk adjustment. Therefore, by determining the concurrent correlation coefficient, the embodiment raises the overall risk assessment, ensures that resources are focused on real chain risks, and reduces the interference of invalid concurrent alarms.

[0070] Further, if the regional power grid load level is in a flat section (i.e., stable electricity demand, such as 10 am to 3 pm) or a valley (i.e., low electricity demand, such as 0 am to 5 am), the basic power grid constraint coefficient is calculated according to whether the power plant is currently undertaking the power protection task. If the regional power grid load level is at a peak (i.e., a surge in electricity demand, such as 18:00 to 22:00 in the summer), the power grid constraint coefficient is increased based on the basic coefficient (e.g., from 0.3 to 0.5 after adjustment), because the power plant needs to prioritize power supply during peak hours, and any alarm that may cause output to drop needs to be urgently disposed of (to avoid regional power outages). Therefore, by determining the power grid constraint coefficient, the embodiment ensures that the alarm priority is consistent with the power grid supply target, and avoids scheduling errors caused by a disconnect between internal plant evaluation and grid demand.

[0071] Therefore, by determining the second influence degree based on the device risk coefficient, the concurrent correlation coefficient, and the power grid constraint coefficient, the embodiment quantifies the correlation between the real-time operating condition and the current target alarm event. The second influence degree changes in real time with the operating condition, allowing the alarm priority to respond in real time to changes in the operating condition, avoiding the problem of the priority corresponding to the alarm importance score not being able to adapt to dynamic scenarios, and enabling the influence of the real-time operating condition on the alarm to be converted from an empirical judgment to a result driven by the fusion of real-time operating conditions and historical experience. This can accurately reflect the true risk level under the current operating condition, and can also cooperate with the first influence degree (device correlation risk) to provide a complete real-time risk basis for priority correction based on historical experience and real-time scenarios, ensuring that alarm disposal decisions are more in line with the dynamic reality of power plant operation.

[0072] In some embodiments, the target alarm event is processed according to the execution priority, including: acquire personnel scheduling data, the personnel scheduling data including personnel working states and processing capabilities for the alarm event types; acquire historical processing data of processing operations related to the alarm event type in the accident alarm record, the historical processing data including a historical success rate and a historical processing efficiency; determine an event predicted processing time length based on the running conditions, the topology structure and the historical processing data, and determine an efficiency difference value between the event predicted processing time length and the historical processing efficiency; perform personnel scheduling based on the personnel scheduling data, the historical processing data, the efficiency difference value and the execution priority to process the target alarm event.

[0073] In the embodiment, the personnel working states include basic states, load conditions and physical positions, the basic states are used to determine whether the personnel can immediately respond, i.e. on duty / temporarily off duty / on leave, the load conditions represent the number of tasks being processed and the progress of the tasks, and the physical positions represent the current power plant area where the personnel are located, which is used to determine the response time of the personnel to the alarm device (e.g. the personnel close to the alarm device can arrive at the scene faster). The processing capabilities can be skill matching degrees and qualification certifications, which represent the processing experience of the personnel for the current alarm event type.

[0074] Then, a proportion of the number of processing successes to the total number of processing times in the historical processing feedback data for the same type of the current alarm event is acquired, i.e. a historical success rate. The historical processing efficiency represents the average processing speed of the same type of alarm event in history, including an average processing time length, i.e. the average time from alarm response to device recovery. Further, an efficiency difference value is determined by the event predicted processing time length and the average processing time length of the same type of alarm event in history, so as to adjust the scheduling personnel configuration strength. Still further, the personnel scheduling data, the historical experience, the efficiency difference value and the execution priority (i.e. urgency) are integrated to configure the processing personnel.

[0075] Therefore, the personnel scheduling is performed by the personnel scheduling data, the historical experience, the efficiency difference value and the execution priority in the embodiment, so as to guarantee the timeliness and accuracy of alarm disposal, reduce the operation risk of the power plant and improve the alarm processing efficiency.

[0076] In a second aspect, the application provides a power plant accident information quality evaluation device, which comprises: an event data acquisition module 201, configured to acquire alarm event data of a target alarm event to be displayed, the alarm event data including an alarm event type and a target power plant to which the target alarm event belongs; The historical data acquisition module 202 is configured to acquire at least one accident alarm record of the target power plant, wherein the accident alarm record comprises an accident type of an occurred accident alarm and historical processing feedback data, and the historical processing feedback data comprises a processing operation type and an impact level in response to the accident alarm; The encoding module 203 is configured to determine an accident type encoding vector based on the accident type, the processing operation type and the impact level of the accident alarm record, and determine a location encoding vector based on the target power plant, wherein the location encoding vector is used to indicate the difference between the target power plant and other power plants; The historical event feature extraction module 204 is configured to determine a historical event feature used to describe the behavior mode of the target power plant according to the accident type encoding vector and the location encoding vector of the accident alarm record; The alarm importance evaluation module 205 is configured to associate the historical event feature with the alarm event data to generate an alarm importance score of the target alarm event; The display module 206 is configured to display the target alarm event according to a priority mapped with the alarm importance score.

[0077] The system of the embodiments of the present application can execute the method provided by the embodiments of the present application, and the implementation principles are similar. The actions performed by each module in the system of the embodiments of the present application correspond to the steps in the method of the embodiments of the present application. For the detailed function description of each module of the system, refer to the description of the corresponding method in the foregoing, which will not be repeated here.

[0078] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for assessing the quality of power plant accident information, characterized in that, include: Obtain the alarm event data of the target alarm event to be displayed, the alarm event data including the alarm event type and the target power plant to which the target alarm event belongs; Acquire at least one accident alarm record of the target power plant, the accident alarm record containing the accident type of the accident alarm and historical processing feedback data, the historical processing feedback data including the processing operation type and impact level in response to the accident alarm; Based on the accident type, handling operation type, and impact level of the accident alarm record, an accident type coding vector is determined, and a location coding vector is determined based on the target power plant. The location coding vector is used to indicate the differences between the target power plant and other power plants. Based on the accident type encoding vector and location encoding vector of the accident alarm record, historical event features used to describe the behavior pattern of the target power plant are determined; The historical event characteristics are correlated with the alarm event data to generate an alarm importance score for the target alarm event; The target alarm events are displayed based on the priority mapped to the alarm importance score.

2. The power plant accident information quality assessment method according to claim 1, characterized in that, The process of determining an accident type coding vector based on the accident type, handling operation type, and impact level of the accident alarm record, and determining a location coding vector based on the target power plant, includes: The accident type, processing operation type, and impact level of the accident alarm records are encoded to obtain accident alarm fusion features; The accident alarm fusion features are converted into an accident type encoding vector by a preset first embedding model. The accident type encoding vector is used to indicate the correlation between the accident type, the processing operation type and the impact level. The power plant identifier of the target power plant is encoded to obtain the power plant characteristics; The power plant features are converted into location encoding vectors using a pre-defined second embedding model.

3. The power plant accident information quality assessment method according to claim 2, characterized in that, The accident alarm record also includes an alarm timestamp for the accident alarm; the step of determining historical event features describing the behavioral patterns of the target power plant based on the accident type encoding vector and location encoding vector of the accident alarm record includes: Arrange the accident alarm records that occurred at the target power plant in ascending order of the alarm timestamps to obtain the accident type coding sequence of the target power plant; The accident type coding sequence is subjected to time feature extraction using a pre-built feature extraction model to obtain forward hidden state and backward hidden state. The forward hidden state and the backward hidden state are then concatenated to obtain context-aware features. The context-aware features are used to establish the sequential dependency relationship between each accident alarm record in the accident type coding sequence. The location encoding vector and the context-aware feature are fused to obtain the historical fusion feature of the accident alarm record; Based on the alarm timestamp of the accident alarm record, the time decay weight of the accident alarm record is determined, and the historical fusion features of each accident alarm record occurring in the target power plant and the time decay weight are weighted and calculated to obtain the historical event features of the target power plant.

4. The power plant accident information quality assessment method according to claim 3, characterized in that, The step of fusing the location encoding vector with the context-aware features to obtain the historical fusion features of the accident alarm record includes: The location encoding vector is enhanced using a preset first multilayer perceptron to obtain a location importance bias vector; The position importance bias vector and the context-aware feature are concatenated to obtain the concatenated feature, and the concatenated feature is processed by a preset second multilayer perceptron to obtain the historical fusion feature. Based on the alarm timestamp of the alarm record, the time decay weight of the alarm record is determined, and the historical fusion features of each alarm record occurring at the target power plant are weighted and calculated with the time decay weight to obtain the historical event features of the target power plant, including: Based on the alarm timestamp of the accident alarm record, the occurrence timestamp of the target alarm event, and a preset time decay rate, the time decay weight of the accident alarm record is determined. The time decay weight is used to indicate the degree of influence of accident alarm records with a time difference less than a preset time difference threshold on the target alarm event. The time difference is determined by the alarm timestamp of the accident alarm record and the occurrence timestamp. The time decay weight and historical fusion characteristics of any accident alarm record occurring at the target power plant are calculated to obtain the historical event characteristics of the target power plant.

5. The power plant accident information quality assessment method according to claim 4, characterized in that, The step of associating the historical event features with the alarm event data to generate an alarm importance score for the target alarm event includes: Based on the alarm event type of the target alarm event, determine the event type feature, and based on the target power plant, determine the target power plant feature, and then combine the event type feature and the target power plant feature to form the event feature; Calculate the semantic similarity between the event features and the historical event features, whereby the semantic similarity is used to indicate the correlation between the target alarm event and any accident alarm record; The semantic similarity associated with each accident alarm record is superimposed to calculate the target similarity, and the target similarity is processed by a preset third multilayer perceptron to obtain the context modulation factor. Based on the context modulation factor, the event characteristics, and the historical event characteristics, the alarm importance characteristics of the target alarm event are determined; Based on a preset alarm importance weight matrix and a preset alarm importance bias vector, the alarm importance features are mapped to alarm importance scores.

6. The method for assessing the quality of power plant accident information according to any one of claims 1 to 5, characterized in that, The method further includes: The current operating conditions and topology of the target power plant are obtained. The operating conditions include the overall operating status of the power plant and the operating status and associated status of multiple devices in the target power plant. The associated status is used to indicate the associated device data of concurrent alarm events that occur at the same time as the target alarm event and their associated devices. The overall operating status of the power plant includes the regional power grid load level and dispatch requirements of the area where the target power plant is located. The topology is used to indicate the connection relationship between the devices. Based on the operating conditions and the topology, a first degree of influence is determined. The first degree of influence is used to indicate the correlation between the target alarm event, the processing device, and the associated device. The processing device is used to indicate the device involved in processing the target alarm event. Match the incident alarm records and their impact levels related to the alarm event type of the target alarm event; Based on the equipment operating status, the equipment association status, and the overall operating status of the power plant, a second degree of influence is determined, which is used to indicate the degree of influence of the operating condition on the target alarm event. Based on the first degree of influence and the second degree of influence, a real-time influence factor is determined, and based on the real-time influence factor and the matched influence level, a historical correlation coefficient is determined; Based on the first degree of impact, the second degree of impact, the impact level, and the historical correlation coefficient, the priority mapped to the alarm importance score is adjusted to obtain the execution priority, and the target alarm event is processed according to the execution priority indication.

7. The power plant accident information quality assessment method according to claim 6, characterized in that, The determination of the first degree of influence based on the operating conditions and the topology includes: Based on the topology and the operating conditions, the event impact degree of the target alarm event is determined, wherein the event impact degree includes the event impact range and the event impact time. The event impact range is used to indicate at least one level of equipment affected by the target alarm event, the number of equipment, and the first equipment correlation between equipment. The event impact time is used to indicate the duration between the occurrence timestamp of the target alarm event and the completion time of the target alarm event. Based on the topology, the device operating status, and the device association status, the device impact degree of the processing device is determined. The device impact degree includes the device impact time of the processing device, the difficulty of the device processing operation, the importance of the device, and the second device correlation between the processing devices. The device impact time is used to indicate the duration between the moment when the processing device becomes abnormal due to the target alarm event and the moment when it returns to normal. Based on the topology and the device association status, the concurrent impact of the concurrent alarm events is determined. The concurrent impact includes the number of concurrent alarm events, the difficulty of handling concurrent events, the importance of concurrent events, the correlation between concurrent alarm events, and the associated device data of the corresponding associated devices. The difficulty of handling concurrent events is used to indicate the difficulty of operating the associated devices involved in handling the concurrent alarm events. The importance of concurrent events is used to indicate the importance of the concurrent alarm events compared to the target power plant and the target alarm events. Based on the degree of impact of the event, the degree of impact of the device, and the degree of impact of concurrency, a first degree of impact is obtained.

8. The power plant accident information quality assessment method according to claim 7, characterized in that, The equipment operating status includes operating condition and load rate; determining the second degree of influence based on the equipment operating status, the associated equipment status, and the overall operating status of the power plant includes: When the operating condition is detected to be not in the specified critical operating condition and the load rate is within the preset normal load rate range, the equipment risk coefficient is determined based on the equipment operating status, wherein the critical operating condition is a peak load phase or a start-up / shutdown phase; or, when the operating condition is detected to be in the critical operating condition and the load rate exceeds the normal load rate range, the equipment risk coefficient is increased based on the equipment operating status; or, when the operating condition is detected to be in a shutdown or standby state, the equipment risk coefficient is decreased based on the equipment operating status. When the number of concurrent alarm events detected is less than a preset first threshold and the event correlation is less than a preset correlation threshold, a concurrent correlation coefficient is determined based on the device correlation status; or, when the number of concurrent alarm events detected is greater than the first threshold and the event correlation is greater than the correlation threshold, the concurrent correlation coefficient is increased based on the device correlation status; or, when the number of concurrent alarm events detected is greater than a preset second threshold, or the event correlation reaches a preset cross-system alarm condition, the increased concurrent correlation coefficient is added based on the device correlation status, where the second threshold is greater than the first threshold. When the load level of the regional power grid is detected to be at a flat load level or a low load level, a power grid constraint coefficient is determined based on the overall operating status of the power plant. The power grid constraint coefficient is used to indicate the urgency of handling the target alarm event; or, when the load level of the regional power grid is detected to be at a peak load level, the power grid constraint coefficient is increased based on the overall operating status of the power plant. The second degree of influence is determined based on the equipment risk coefficient, the concurrent correlation coefficient, and the power grid constraint coefficient.

9. The power plant accident information quality assessment method according to claim 8, characterized in that, The process of handling the target alarm event according to the execution priority indication includes: Acquire personnel scheduling data, which includes personnel work status and the ability to handle the alarm event types; Obtain historical processing data of the processing operations related to the alarm event type in the accident alarm record, the historical processing data including historical success rate and historical processing efficiency; Based on the operating conditions, the topology, and the historical processing data, the event prediction processing time is determined, and the efficiency difference between the event prediction processing time and the historical processing efficiency is determined. Personnel scheduling is performed based on the personnel scheduling data, the historical processing data, the efficiency difference, and the execution priority to handle the target alarm event.

10. A power plant accident information quality assessment device, characterized in that, include: The event data acquisition module is used to acquire the alarm event data of the target alarm event to be displayed. The alarm event data includes the alarm event type and the target power plant to which the target alarm event belongs. The historical data acquisition module is used to acquire at least one accident alarm record of the target power plant. The accident alarm record includes the accident type of the accident alarm and historical processing feedback data. The historical processing feedback data includes the processing operation type and impact level in response to the accident alarm. The coding module is used to determine an accident type coding vector based on the accident type, handling operation type and impact level of the accident alarm record, and to determine a location coding vector based on the target power plant. The location coding vector is used to indicate the difference between the target power plant and other power plants. The historical event feature extraction module is used to determine historical event features that describe the behavior pattern of the target power plant based on the accident type encoding vector and location encoding vector of the accident alarm record. The alarm importance assessment module is used to associate the historical event characteristics with the alarm event data to generate an alarm importance score for the target alarm event; The display module is used to display the target alarm events according to the priority mapped to the alarm importance score.

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