Vehicle emission anomaly detection and traceability method and device and electronic equipment

Abstract

The invention relates to the technical field of transportation, in particular to a vehicle emission anomaly detection and tracing method and device and electronic equipment, and the method comprises the steps: inputting a current operation condition and current operation data into a dynamic threshold model to obtain a first detection result, and inputting the first detection result into an emission prediction model to obtain a second detection result; fusing the first detection result and the second detection result to obtain a final detection result when the first detection result and the second detection result do not meet a preset vehicle emission abnormal judgment condition, judging that the vehicle emission is abnormal when the final detection result is greater than or equal to a preset threshold value, and judging that the vehicle emission is abnormal based on a vehicle emission abnormal knowledge graph database. And a Cosine similarity calculation strategy is utilized to obtain reasons and probabilities of abnormal emission. The problems that a traditional vehicle emission anomaly detection and tracing method is low in emission anomaly detection efficiency, high in misjudgment rate and difficult in tracing are solved, accurate detection of emission anomaly is achieved, closed-loop management from anomaly triggering to root cause tracing is completed, and operation and maintenance intelligence is improved.

Description

Technical Field

[0001] The present invention relates to the field of transportation technology, and in particular to a method, device and electronic equipment for detecting and tracing vehicle emission anomalies. Background Art

[0002] Traditional vehicle emission anomaly detection methods mostly rely on single threshold detection (such as fixed nitrogen oxide NOx limit) or manual experience judgment, which poses the following technical challenges: (1) Data fragmentation and inefficient analysis: Emission anomalies involve the coupling of multiple components such as the engine and post-treatment system (such as the selective catalytic reduction system (SCR) and diesel particulate filter (DPF). Traditional methods rely on scattered sensor data or manual experience, and it is difficult to integrate multi-source heterogeneous information such as maintenance cases, real-time data from vehicle terminals, and literature knowledge, resulting in low efficiency in locating the root cause of the fault.

[0003] (2) Poor adaptability of static thresholds: Existing threshold models often use fixed limits, ignoring the dynamic differences in the vehicle's actual operating conditions (such as urban congestion, high-speed cruising, and low-temperature cold starts), which can easily lead to false alarms or missed detections due to fluctuations in operating conditions.

[0004] (3) Insufficient causal reasoning capabilities: The root cause of emission abnormalities and their manifestations are complex (e.g., “urea pump blockage → NOx exceeding the standard” requires multi-level transmission). Traditional rule engines or statistical methods are difficult to model deep causal relationships, and the accuracy of tracing is low.

[0005] (4) Delayed knowledge updating: Maintenance experience and new fault cases rely on manual summarization, and the knowledge base update cycle is long, which cannot adapt to the rapid iteration of emission control technology. Summary of the invention

[0006] The present invention provides a vehicle emission anomaly detection and tracing method, device and electronic equipment to solve the problems of traditional vehicle emission anomaly detection and tracing methods, such as low emission anomaly detection efficiency, high misjudgment rate, and difficult tracing due to data fragmentation and complex causal relationships.

[0007] The first aspect of the present invention provides a method for detecting and tracing vehicle emission anomalies, comprising the following steps: obtaining the current operating condition and current operating data of the vehicle; inputting the current operating condition and the current operating data into a preset dynamic threshold model to obtain a first detection result, and inputting the current operating condition and the current operating data into a preset emission prediction model to obtain a second detection result, wherein the preset dynamic threshold model is constructed by a plurality of preset operating conditions and a preset emission abnormality threshold corresponding to each preset operating condition, and the preset emission prediction model is obtained by training a long short-term memory network (LSTM) neural network with the historical operating data of the vehicle; when the first detection result and the second detection result do not meet the preset conditions for determining vehicle emission anomalies, the first detection result and the second detection result are fused to obtain a final detection result, and when the final detection result is greater than or equal to the preset threshold, the vehicle emission is determined to be abnormal, and based on a preset vehicle emission anomaly knowledge graph library, the cause and probability of the vehicle emission anomaly are obtained by using a Cosine similarity calculation strategy.

[0008] Optionally, the first detection result includes the actual emissions of the vehicle. When judging whether the first detection result meets the preset conditions for determining vehicle emissions abnormality, it also includes: judging whether the actual emissions of the vehicle are greater than a preset emissions abnormality threshold, and whether the first duration of the actual emissions of the vehicle being greater than the preset emissions abnormality threshold is greater than a first preset duration, wherein the preset emissions abnormality threshold is obtained by the current operating conditions; if the actual emissions of the vehicle are greater than the preset emissions abnormality threshold and the first duration is greater than the first preset duration, then it is determined that the first detection result meets the preset conditions for determining vehicle emissions abnormality, and the vehicle emissions are determined to be abnormal, and based on the preset vehicle emissions abnormality knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of the vehicle emissions abnormality, otherwise, it is determined that the first detection result does not meet the preset conditions for determining vehicle emissions abnormality.

[0009] Optionally, before determining whether the first detection result is greater than the preset emission abnormality threshold, it also includes: acquiring historical operating data of the vehicle; constructing the multiple preset operating conditions based on data features in the historical operating data; establishing a mapping relationship between the historical operating data of each preset operating condition and the emission abnormality threshold, so as to obtain the preset emission abnormality threshold according to the current operating condition based on the mapping relationship.

[0010] Optionally, the second detection result includes the actual emissions of the vehicle. After the current operating conditions and the current operating data are input into the preset emission prediction model to obtain the second detection result, it also includes: judging whether the ratio of the actual emissions of the vehicle to the emission prediction value is greater than the preset ratio, and whether the second duration of the actual emissions of the vehicle is greater than the emission prediction value is greater than the second preset duration; if the ratio of the actual emissions of the vehicle to the emission prediction value is greater than the preset ratio, and the second duration is greater than the second preset duration, then it is determined that the second detection result meets the preset vehicle emission abnormality determination condition, and the vehicle emissions are determined to be abnormal, and based on the preset vehicle emission abnormality knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of the vehicle emission abnormality, otherwise, it is determined that the second detection result does not meet the preset vehicle emission abnormality determination condition.

[0011] Optionally, the method of obtaining the cause and probability of vehicle emission abnormality based on a preset vehicle emission abnormality knowledge graph library using a Cosine similarity calculation strategy includes: obtaining historical operation data of the vehicle; based on the historical operation data, using the Cosine similarity calculation strategy to calculate the data features of each relationship chain in the preset vehicle emission abnormality knowledge graph library respectively to obtain multiple Cosine similarity calculation results; based on a preset sorting strategy, sorting the multiple Cosine similarity calculation results, and using the Cosine similarity calculation results that meet the preset ranking conditions in the sorting results as the cause and probability of the vehicle emission abnormality.

[0012] Optionally, before obtaining the cause and probability of vehicle emission anomaly based on a preset vehicle emission anomaly knowledge graph library and using a Cosine similarity calculation strategy, the method includes: obtaining vehicle emission anomaly cause data; forming a vehicle emission anomaly cause data relationship chain based on the vehicle emission anomaly cause data; using a preset knowledge graph theory to convert the vehicle emission anomaly cause data relationship chain into entities and relationships in a knowledge graph; and using an entity alignment strategy to perform entity fusion on relatively conflicting entities to obtain the preset vehicle emission anomaly knowledge graph library.

[0013] Optionally, the fusing the first detection result and the second detection result to obtain the final detection result includes: using a preset fusion formula to fuse the first detection result and the second detection result to obtain the final detection result, wherein the preset fusion formula is: ; in, For the final test results, E caris the actual emission of the vehicle, E Th is the preset emission abnormality threshold, E Pv is the emission prediction value, a is the weight of the first detection result, b is the weight of the second detection result.

[0014] Optionally, after determining that the vehicle emission is abnormal and obtaining the cause and probability of the vehicle emission abnormality based on a preset vehicle emission abnormality knowledge graph library and using a Cosine similarity calculation strategy, it includes: sending vehicle emission abnormality warning information to a preset terminal; based on the vehicle emission abnormality warning information, the cause and probability of the vehicle emission abnormality, using an AI model to generate vehicle maintenance suggestions.

[0015] The second aspect of the present invention provides a vehicle emission anomaly detection and tracing device, including: an acquisition module, used to acquire the current operating condition and current operating data of the vehicle; a detection module, used to input the current operating condition and the current operating data into a preset dynamic threshold model to obtain a first detection result, and input the current operating condition and the current operating data into a preset emission prediction model to obtain a second detection result, wherein the preset dynamic threshold model is constructed by multiple preset operating conditions and a preset emission abnormality threshold corresponding to each preset operating condition, and the preset emission prediction model is obtained by training an LSTM neural network with the historical operating data of the vehicle; a tracing module, used to fuse the first detection result and the second detection result to obtain a final detection result when the first detection result and the second detection result do not meet the preset vehicle emission abnormality judgment condition, and when the final detection result is greater than or equal to the preset threshold, judge the vehicle emission abnormality, and based on the preset vehicle emission abnormality knowledge graph library, use the Cosine similarity calculation strategy to obtain the cause and probability of the vehicle emission abnormality.

[0016] Optionally, the first detection result includes the actual emissions of the vehicle. When judging whether the first detection result meets the preset conditions for determining vehicle emissions abnormality, the detection module is further used to: judge whether the actual emissions of the vehicle are greater than a preset emissions abnormality threshold, and whether the first duration of the actual emissions of the vehicle being greater than the preset emissions abnormality threshold is greater than a first preset duration, wherein the preset emissions abnormality threshold is obtained by the current operating conditions; if the actual emissions of the vehicle are greater than the preset emissions abnormality threshold and the first duration is greater than the first preset duration, then it is determined that the first detection result meets the preset conditions for determining vehicle emissions abnormality, and the vehicle emissions are determined to be abnormal, and based on the preset vehicle emissions abnormality knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of the vehicle emissions abnormality; otherwise, it is determined that the first detection result does not meet the preset conditions for determining vehicle emissions abnormality.

[0017] Optionally, before determining whether the first detection result is greater than the preset emission abnormality threshold, the detection module is also used to: obtain historical operating data of the vehicle; construct the multiple preset operating conditions based on data features in the historical operating data; establish a mapping relationship between the historical operating data of each preset operating condition and the emission abnormality threshold, so as to obtain the preset emission abnormality threshold according to the current operating condition based on the mapping relationship.

[0018] Optionally, the second detection result includes the actual emissions of the vehicle. When judging whether the second detection result meets the preset conditions for determining vehicle emissions abnormality, the detection module is further used to: judge whether the ratio of the actual emissions of the vehicle to the predicted emissions is greater than the preset ratio, and whether the second duration of the actual emissions of the vehicle is greater than the predicted emissions value is greater than the second preset duration; if the ratio of the actual emissions of the vehicle to the predicted emissions is greater than the preset ratio, and the second duration is greater than the second preset duration, then it is judged that the second detection result meets the preset conditions for determining vehicle emissions abnormality, and the vehicle emissions are judged to be abnormal, and based on the preset vehicle emissions abnormality knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of the vehicle emissions abnormality; otherwise, it is judged that the second detection result does not meet the preset conditions for determining vehicle emissions abnormality.

[0019] Optionally, the traceability module is also used to: obtain historical operating data of the vehicle; based on the historical operating data, use the Cosine similarity calculation strategy to calculate the data features of each relationship chain in the preset vehicle emission abnormality knowledge graph library, and obtain multiple Cosine similarity calculation results; based on a preset sorting strategy, sort the multiple Cosine similarity calculation results, and use the Cosine similarity calculation results that meet the preset ranking conditions in the sorting results as the cause and probability of the vehicle's emission abnormality.

[0020] Optionally, before obtaining the cause and probability of vehicle emission anomaly based on a preset vehicle emission anomaly knowledge graph library and using a Cosine similarity calculation strategy, the tracing module is also used to: obtain vehicle emission anomaly cause data; form a vehicle emission anomaly cause data relationship chain based on the vehicle emission anomaly cause data; use a preset knowledge graph theory to convert the vehicle emission anomaly cause data relationship chain into entities and relationships in the knowledge graph; use an entity alignment strategy to perform entity fusion on relatively conflicting entities to obtain the preset vehicle emission anomaly knowledge graph library.

[0021] Optionally, the traceability module is further used to: use a preset fusion formula to fuse the first detection result and the second detection result to obtain the final detection result, wherein the preset fusion formula is: ; in, For the final test results, E car is the actual emission of the vehicle, E Th is the preset emission abnormality threshold, E Pv is the emission prediction value, a is the weight of the first detection result, b is the weight of the second detection result.

[0022] Optionally, after determining that the vehicle emissions are abnormal and obtaining the cause and probability of the vehicle emissions abnormality based on a preset vehicle emissions abnormality knowledge graph library using a Cosine similarity calculation strategy, the traceability module is also used to: send vehicle emissions abnormality warning information to a preset terminal; and generate vehicle maintenance suggestions based on the vehicle emissions abnormality warning information, the cause and probability of the vehicle emissions abnormality using an AI model.

[0023] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle emission abnormality detection and tracing method as described in the above embodiment.

[0024] A fourth aspect of the present invention provides a computer program product having a computer program stored thereon, which is executed by a processor to implement the vehicle emission anomaly detection and tracing method as described in the above embodiments.

[0025] In the above implementation, the current operating condition and the current operating data are input into a preset dynamic threshold model to obtain a first detection result, and the current operating condition and the current operating data are input into a preset emission prediction model to obtain a second detection result, wherein the preset dynamic threshold model is constructed by a plurality of preset operating conditions and a preset emission abnormality threshold corresponding to each preset operating condition, and the preset emission prediction model is obtained by training the LSTM neural network with the historical operating data of the vehicle; when the first detection result and the second detection result do not meet the preset conditions for determining vehicle emission abnormality, the first detection result and the second detection result are integrated to obtain the final detection result, and when the final detection result is greater than or equal to the preset threshold, the vehicle emission is determined to be abnormal, and based on the vehicle emission abnormality knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of vehicle emission abnormality. Thus, the traditional vehicle emission abnormality detection and tracing method is solved, and the problems of low efficiency, high misjudgment rate and difficult tracing of emission abnormality due to data fragmentation and complex causal relationships are solved, and accurate detection of emission abnormalities is achieved, and closed-loop management from abnormal triggering to root cause tracing is completed, so as to achieve the goals of reducing vehicle emission pollution, improving the level of intelligent operation and maintenance, and ensuring environmental compliance.

[0026] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of a method for detecting and tracing vehicle emission anomalies according to an embodiment of the present invention; Figure 2 (a) is a flow chart of a detection mechanism of a dynamic threshold model and an LSTM machine learning model according to an embodiment of the present invention. Figure 2 (b) is a flow chart of establishing a collaborative detection mechanism integrating a dynamic threshold model and an LSTM machine learning model according to an embodiment of the present invention; Figure 3 A flowchart of a method for detecting and tracing vehicle emission anomalies according to another embodiment of the present invention; Figure 4 A schematic diagram of a knowledge graph of causes of abnormal vehicle emissions according to an embodiment of the present invention; Figure 5 An exemplary diagram of a vehicle emission abnormality detection and source tracing device according to an embodiment of the present invention; Figure 6 FIG. 4 is a schematic diagram of a structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] Embodiments of the present invention 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 invention, and should not be construed as limiting the present invention.

[0029] The following describes the vehicle emission anomaly detection and tracing method, device and electronic device of the embodiment of the present invention with reference to the accompanying drawings. In view of the problems of low efficiency, high misjudgment rate and difficult tracing caused by data fragmentation and complex causal relationship in the traditional vehicle emission anomaly detection and tracing method mentioned in the above background technology, the present invention provides a vehicle emission anomaly detection and tracing method, in which the current operating condition and the current operating data are input into a preset dynamic threshold model to obtain a first detection result, and the current operating condition and the current operating data are input into a preset emission prediction model to obtain a second detection result, wherein the preset dynamic threshold model is constructed by a plurality of preset operating conditions and a preset emission amount abnormality threshold corresponding to each preset operating condition, and the preset emission prediction model is obtained by training the LSTM neural network with the historical operating data of the vehicle; when the first detection result and the second detection result do not meet the preset vehicle emission abnormality determination condition, the first detection result and the second detection result are fused to obtain the final detection result, and when the final detection result is greater than or equal to the preset threshold, the vehicle emission is determined to be abnormal, and based on the vehicle emission abnormality knowledge graph library, the cause and probability of the vehicle emission abnormality are obtained by using the Cosine similarity calculation strategy. As a result, the problems of traditional vehicle emission anomaly detection and tracing methods, such as low efficiency, high misjudgment rate and difficult tracing due to data fragmentation and complex causal relationships, are solved. Accurate detection of emission anomalies is achieved, and closed-loop management from anomaly triggering to root cause tracing is completed, achieving the goals of reducing vehicle emission pollution, improving the level of intelligent operation and maintenance, and ensuring environmental compliance.

[0030] Specifically, Figure 1 A schematic flow chart of a method for detecting and tracing vehicle emission anomalies provided in an embodiment of the present invention.

[0031] like Figure 1 As shown, the vehicle emission abnormality detection and tracing method includes the following steps: In step S101, the current operating condition and current operating data of the vehicle are obtained. Among them, the current operating data includes vehicle speed, acceleration, engine net output torque, engine speed, engine fuel flow, engine coolant temperature, DPF pressure difference, DPF regeneration frequency, ambient temperature, intake pressure, exhaust temperature, exhaust back pressure, air-fuel ratio, exhaust gas recirculation EGR valve opening, urea injection amount, particulate matter concentration and SCR downstream NOx transmitter output value data. The current operating condition is one of urban and suburban conditions, high-speed cruising conditions, low-temperature cold start conditions, and climbing conditions.

[0032] In step S102, the current operating condition and the current operating data are input into a preset dynamic threshold model to obtain a first detection result, and the current operating condition and the current operating data are input into a preset emission prediction model to obtain a second detection result, wherein the preset dynamic threshold model is constructed by multiple preset operating conditions and a preset emission abnormality threshold corresponding to each preset operating condition, and the preset emission prediction model is obtained by training an LSTM neural network using historical operating data of the vehicle.

[0033] Among them, the preset emission prediction model is also an LSTM model.

[0034] Optionally, in some embodiments, before determining whether the first detection result is greater than a preset emission abnormality threshold, it also includes: obtaining historical operating data of the vehicle; constructing multiple preset operating conditions based on data features in the historical operating data; establishing a mapping relationship between the historical operating data of each preset operating condition and the emission abnormality threshold, so as to obtain the preset emission abnormality threshold according to the current operating condition based on the mapping relationship.

[0035] Specifically, the steps of constructing a preset dynamic threshold model are: The first step is to establish four types of preset operating conditions: urban and suburban conditions (vehicle speed less than 50 km / h, idle ratio higher than 18%), high-speed cruising conditions (vehicle speed higher than 70 km / h, accelerator pedal opening fluctuation rate lower than 10%), climbing conditions (engine torque higher than 70%, slope greater than 6%), and low-temperature cold start conditions (coolant temperature lower than 40°C, ambient temperature lower than 10°C).

[0036] In the second step, the historical operating data is processed using the 30-s moving window averaging method, and divided into urban and suburban conditions, high-speed cruising conditions, climbing conditions, and low-temperature cold start conditions according to the data characteristics. The 95% quantile of NOx emissions under each condition is calculated as the preset emission abnormality threshold (such as the NOx emission abnormality threshold for urban and suburban conditions is 500 ppm), thereby establishing a mapping relationship between the historical operating data of each preset operating condition and the emission abnormality threshold.

[0037] Optionally, in some embodiments, the first detection result includes the actual emissions of the vehicle. When judging whether the first detection result meets the preset conditions for determining vehicle emissions abnormality, it also includes: judging whether the actual emissions of the vehicle are greater than a preset emissions abnormality threshold, and whether the first duration of the actual emissions of the vehicle being greater than the preset emissions abnormality threshold is greater than a first preset duration, wherein the preset emissions abnormality threshold is obtained by the current operating conditions; if the actual emissions of the vehicle are greater than the preset emissions abnormality threshold and the first duration is greater than the first preset duration, then it is determined that the first detection result meets the preset conditions for determining vehicle emissions abnormality, and the vehicle emissions are determined to be abnormal, and based on the preset vehicle emissions abnormality knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of the vehicle emissions abnormality, otherwise, it is determined that the first detection result does not meet the preset conditions for determining vehicle emissions abnormality.

[0038] In the embodiment of the present invention, the first preset duration is set to 2 hours.

[0039] It should be understood that the current operating condition of the vehicle is determined based on the current operating data, and the preset emission abnormality threshold corresponding to the current operating condition is determined based on the mapping relationship. If the actual emissions of the vehicle are greater than the preset emission abnormality threshold corresponding to the current operating condition, and the first duration is greater than 2 hours, then it is determined that the first detection result meets the preset vehicle emission abnormality determination condition, and the vehicle emission abnormality is directly determined. Further, based on the preset vehicle emission abnormality knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of the vehicle emission abnormality. Otherwise, it is determined that the first detection result does not meet the preset vehicle emission abnormality determination condition, that is, the first detection result cannot directly determine the vehicle emission abnormality. Among them, based on the preset vehicle emission abnormality knowledge graph library, the steps of obtaining the cause and probability of the vehicle emission abnormality using the Cosine similarity calculation strategy are specifically described below.

[0040] For example, if the actual emissions of the vehicle exceed the preset emission abnormality threshold corresponding to the current operating conditions for two consecutive hours during operation, there is no need to merge the second detection result and directly determine that the vehicle emissions are abnormal.

[0041] Optionally, in some embodiments, the second detection result includes the actual emissions of the vehicle, and determining whether the second detection result meets the preset conditions for determining vehicle emissions abnormality includes: determining whether the ratio of the vehicle's actual emissions to the predicted emissions is greater than the preset ratio, and whether the second duration for which the vehicle's actual emissions are greater than the predicted emissions is greater than the second preset duration; if the ratio of the vehicle's actual emissions to the predicted emissions is greater than the preset ratio, and the second duration is greater than the second preset duration, then determining that the second detection result meets the preset conditions for determining vehicle emissions abnormality, and determining that the vehicle emissions are abnormal, and based on the preset vehicle emissions abnormality knowledge graph library, using the Cosine similarity calculation strategy, obtaining the cause and probability of the vehicle emissions abnormality, otherwise, determining that the second detection result does not meet the preset conditions for determining vehicle emissions abnormality.

[0042] In the embodiment of the present invention, the second preset duration is set to 2 hours, and the preset ratio is set to 1.5.

[0043] Specifically, the steps of building a preset emission prediction model and detecting whether vehicle emissions are abnormal are as follows: The first step is to use the historical operating data obtained as training data, the vehicle speed, acceleration, torque, speed, and fuel consumption rate in the historical operating data as input, and the NOx emission rate as output. The LSTM neural network is used to train and build an LSTM machine learning model to predict vehicle emissions.

[0044] In the second step, during the operation of the vehicle, the preset emission prediction model continuously predicts the emission prediction value. If the ratio of the vehicle's actual emissions to the emission prediction value is greater than 1.5 for two consecutive hours, the second detection result is determined to meet the preset vehicle emission abnormality determination conditions, and there is no need to merge the second detection result with the first detection result. The heavy-duty vehicle emission abnormality is directly determined, and based on the preset vehicle emission abnormality knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of vehicle emission abnormality. Otherwise, it is determined that the second detection result does not meet the preset vehicle emission abnormality determination conditions, that is, the second detection result cannot directly determine vehicle emission abnormality.

[0045] In summary, the preset emission prediction model and the preset dynamic threshold model can independently determine whether vehicle emissions are abnormal.

[0046] In step S103, when the first detection result and the second detection result do not meet the preset conditions for determining vehicle emission abnormality, the first detection result and the second detection result are fused to obtain a final detection result. When the final detection result is greater than or equal to a preset threshold, the vehicle emission is determined to be abnormal, and based on the preset vehicle emission abnormality knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of the vehicle emission abnormality.

[0047] It should be noted that the fact that the first detection result and the second detection result do not meet the preset conditions for determining vehicle emission abnormalities can be understood as the first detection result cannot directly determine vehicle emission abnormalities and the second detection result cannot directly determine vehicle emission abnormalities. At this time, the first detection result and the second detection result need to be fused.

[0048] Optionally, in some embodiments, fusing the first detection result and the second detection result to obtain the final detection result includes: using a preset fusion formula to fuse the first detection result and the second detection result to obtain the final detection result, wherein the preset fusion formula is: ; (1) in, For the final test results, E car is the actual emission of the vehicle, E Th is the preset emission abnormality threshold, E Pv is the emission prediction value, a is the weight of the first detection result, b is the weight of the second detection result.

[0049] In formula (1), parameter a defaults to 0.6, parameter b defaults to 0.4, and if the final detection result is greater than or equal to the preset threshold c, it is considered that the vehicle has abnormal emissions, triggering the abnormal emission source analysis. The preset threshold c needs to balance the false positives or missed negatives, so the preset threshold c is set to 1.2 by default, which can cover slight fluctuations while avoiding oversensitivity, thereby effectively avoiding false positives or missed negatives.

[0050] Specifically, the embodiment of the present invention establishes a collaborative detection mechanism of a preset dynamic threshold model and a preset emission prediction model to achieve accurate triggering of emission anomaly tracing analysis. The specific steps are: In the first step, the preset dynamic threshold model and the preset emission prediction model are used to detect whether there are vehicle emission abnormalities. If the vehicle emissions are determined to be abnormal, the emission abnormality tracing analysis is directly triggered.

[0051] When the first detection result and the second detection result do not meet the preset conditions for determining abnormal vehicle emissions, it is necessary to fuse the first detection result output by the preset dynamic threshold model and the second detection result output by the preset emission prediction model.

[0052] In the second step, a preset fusion formula is used to fuse the first detection result output by the preset dynamic threshold model and the second detection result output by the preset emission prediction model to obtain a final detection result.

[0053] For example, Figure 2As shown in (a), if the actual emission of the vehicle E car >Preset emission abnormality threshold E Th If the first duration of the vehicle emission is greater than the first preset duration, the vehicle emission is directly determined to be abnormal, and the abnormal emission source analysis is directly triggered. Otherwise, the vehicle emission is not directly determined to be abnormal. If the actual emission of the vehicle E car >Emission forecast E Pv If the second duration of the vehicle emission is greater than the second preset duration, the vehicle emission is directly determined to be abnormal, and the abnormal emission source tracing analysis is directly triggered; otherwise, the vehicle emission is not directly determined to be abnormal; If the actual emission is not one of the above two situations, that is, the first test result and the second test result do not meet the preset emission abnormality determination condition, that is, the vehicle emission abnormality is not determined by the first test result and the second test result, then it is necessary to merge the first test result and the second test result to obtain the final test result, such as Figure 2 As shown in (b), if the final test result If the value is greater than or equal to the preset threshold value c, the vehicle’s emissions are judged to be abnormal, directly triggering the emission abnormality tracing analysis; if the final test result is less than the preset threshold value c, the vehicle’s emissions are judged to be normal.

[0054] It can be seen that the preset dynamic threshold model and the preset emission prediction model can independently detect whether the vehicle has abnormal emissions, and can also integrate the results of separate detections to determine whether there are abnormal emissions. A collaborative detection mechanism of the preset dynamic threshold model and the preset emission prediction model is established, which effectively and reliably supports the detection of abnormal emissions of heavy-duty vehicles and avoids false alarms and omissions of a single model.

[0055] Optionally, in some embodiments, before obtaining the cause and probability of vehicle emission anomaly based on a preset vehicle emission anomaly knowledge graph library and using a Cosine similarity calculation strategy, the method includes: obtaining vehicle emission anomaly cause data; forming a vehicle emission anomaly cause data relationship chain based on the vehicle emission anomaly cause data; using a preset knowledge graph theory to convert the vehicle emission anomaly cause data relationship chain into entities and relationships in the knowledge graph; and using an entity alignment strategy to perform entity fusion on relatively conflicting entities to obtain a preset vehicle emission anomaly knowledge graph library.

[0056] The historical operation data of the vehicle is obtained through remote monitoring, and cluster analysis is performed using the K-Means clustering method to obtain multi-dimensional cluster centers. The cluster centers are used to represent each relationship chain in the preset vehicle emission anomaly knowledge graph library.

[0057] First, historical operating data is obtained, which includes vehicle speed, acceleration, engine net output torque, engine speed, engine fuel flow, engine coolant temperature, DPF pressure difference, DPF regeneration frequency, ambient temperature, intake pressure, exhaust temperature, exhaust back pressure, air-fuel ratio, EGR valve opening, urea injection amount, particulate matter concentration and SCR downstream NOx transmitter output value parameters.

[0058] Then, taking the historical operation data as input, the K-Means clustering method is used to perform cluster analysis, obtain multi-dimensional cluster centers, and extract the multi-dimensional features of each cluster center.

[0059] Finally, based on the multi-dimensional characteristics of each cluster center, it is linked to each relationship chain in the preset vehicle emission anomaly knowledge graph library to support the tracing of the cause of heavy-duty vehicle emission anomalies.

[0060] Among them, the steps of using knowledge graph theory to construct a preset vehicle emission anomaly knowledge graph library are: First, data on the causes of abnormal vehicle emissions are obtained through multiple channels and dimensions. The data sources include: automobile maintenance manuals, heavy-duty vehicle maintenance case data, operation and emission data collected by heavy-duty vehicle onboard terminals, and existing technical literature.

[0061] Then, in the vehicle emission abnormality cause data, the frequent correlation patterns between the root causes of the emission abnormality and the emission abnormality manifestations are extracted to form a relationship chain of vehicle emission abnormality causes (for example: this relationship chain can be expressed as: "urea pump blockage" → "reduced urea injection amount" → "reduced SCR catalytic efficiency" → "NOx exceeds the standard").

[0062] Subsequently, the knowledge graph theory was used to transform the cause chain of vehicle emission anomalies into two types of entities and relationships in the knowledge graph. Based on expert knowledge, entity alignment technology was used to solve the problem of identical but different names in existing entities (for example, "abnormal SCR catalytic efficiency" and "decreased SCR system catalytic rate" both represent the entity "decreased SCR catalytic efficiency"). Entity alignment technology was used to merge relatively conflicting entities to achieve the unification of knowledge representation in the knowledge graph.

[0063] Finally, the above knowledge graph is stored in a graph database (such as Neo4j) to build a preset vehicle emission anomaly knowledge graph library to support efficient path query and reasoning.

[0064] In addition, the preset vehicle emission anomaly knowledge graph library is dynamic and will be continuously optimized with the accumulation of new cases and maintenance experience. The knowledge base can be continuously improved based on maintenance records, emission test results, etc. to improve the accuracy of the knowledge base.

[0065] Optionally, in some embodiments, based on a preset vehicle emission anomaly knowledge graph library, a Cosine similarity calculation strategy is used to obtain the cause and probability of vehicle emission anomaly, including: obtaining historical operation data of the vehicle; based on the historical operation data, using the Cosine similarity calculation strategy to calculate the data features of each relationship chain in the preset vehicle emission anomaly knowledge graph library respectively to obtain multiple Cosine similarity calculation results; based on a preset sorting strategy, sorting the multiple Cosine similarity calculation results, and using the Cosine similarity calculation results that meet the preset ranking conditions in the sorting results as the cause and probability of vehicle emission anomaly.

[0066] Specifically, the present invention is based on a preset vehicle emission anomaly knowledge graph library and uses a Cosine similarity calculation strategy to calculate the potential causes of vehicle emission anomalies and their probability of occurrence, thereby achieving multi-dimensional tracing and source analysis of vehicle emission anomalies.

[0067] First, remotely extract historical operating data where emission anomalies occurred; Then, the Cosine similarity calculation strategy is used to calculate the data features of each relationship chain in the historical operation data of emission anomalies and the preset vehicle emission anomaly knowledge graph library respectively to obtain multiple Cosine similarity calculation results.

[0068] Based on a preset sorting strategy, multiple Cosine similarity calculation results are sorted, and the Cosine similarity calculation results that meet the preset ranking conditions in the sorting results are used as the causes and probabilities of vehicle emission abnormalities. Specifically, in an embodiment of the present invention, each high emission instance and each cause in a preset vehicle emission abnormality knowledge graph library are represented as vectors, and the Cosine similarity between them is calculated to measure the similarity between the instance and each cause.

[0069] A similarity threshold (0.8) is set, and the causes with a similarity higher than 0.8 are regarded as specific causes that may lead to high emissions. Then, for each identified cause, the probability of each cause leading to high emissions is calculated using the weighted average method according to its similarity. The higher the similarity, the greater the corresponding weight, indicating that the cause contributes more to high emissions.

[0070] Thus, the relationship chains in the multiple Cosine similarity calculation results are sorted according to their similarity, and the top three relationship chains in the results are taken as the potential causes of vehicle emission abnormality, and the occurrence probability of the corresponding potential causes is obtained. Here, the top three can also be understood as the three most similar relationship chains in the multiple Cosine similarity calculation results.

[0071] Among them, the Cosine similarity calculation strategy specifically evaluates the similarity between two vectors by calculating the cosine value of the angle between them. Given two non-zero vectors a and b, the Cosine similarity is defined as: .

[0072] in, For vector a and b The dot product of The vectors are a and b The mold length.

[0073] Optionally, in some embodiments, after determining that the vehicle emissions are abnormal and obtaining the cause and probability of the vehicle emissions abnormality based on a preset vehicle emissions abnormality knowledge graph library using a Cosine similarity calculation strategy, it includes: sending vehicle emissions abnormality warning information to a preset terminal; based on the vehicle emissions abnormality warning information, the cause and probability of the vehicle emissions abnormality, using an AI model to generate vehicle maintenance suggestions.

[0074] Specifically, vehicle emission abnormality warning information is sent to the preset terminal to remind users or vehicle monitoring personnel of vehicle emission abnormalities, and the potential causes of vehicle emission abnormalities and their probability of occurrence are visualized. In addition, vehicle maintenance suggestions are synchronously generated in combination with large AI models such as Deepseek or ChatGPT, which supports viewing on PC and mobile terminals.

[0075] In summary, multi-dimensional tracing and source analysis of abnormal dynamic emissions of heavy-duty vehicles can be achieved.

[0076] In order to enable those skilled in the art to further understand the vehicle emission abnormality detection and tracing method according to the embodiment of the present invention, the following is a detailed description in conjunction with a specific embodiment. Figure 3 shown.

[0077] 1. Collect multi-source data and extract association patterns to form relationship chains.

[0078] First, data on the causes of abnormal vehicle emissions are obtained through multiple channels and dimensions. The data sources include: automobile maintenance manuals, heavy-duty vehicle maintenance case data, operation and emission data collected by heavy-duty vehicle onboard terminals, and existing technical literature.

[0079] Among them, in terms of automobile maintenance manuals, all information on the causes of abnormal vehicle emissions in the "Heavy Truck Maintenance Technical Manual" was obtained; in terms of heavy-duty vehicle maintenance case data, heavy-duty vehicle maintenance case data in a certain area in the past three years were investigated; in terms of operation and emission data collected by heavy-duty vehicle on-board terminals, 500 heavy-duty vehicles with a cumulative mileage of more than 50,000 kilometers were selected, and all OBD detection fault information generated by the above heavy-duty vehicles since they left the factory was downloaded. In terms of existing technical literature, a number of academic papers studying abnormal heavy-duty vehicle emissions were queried and downloaded.

[0080] In the data on the causes of abnormal vehicle emissions, the frequent correlation patterns between the root causes of abnormal emissions and the manifestations of abnormal emissions are extracted to form a relationship chain of the causes of abnormal vehicle emissions (for example, this relationship chain can be expressed as: "urea pump blockage" → "reduced urea injection volume" → "reduced SCR catalytic efficiency" → "NOx exceeds the standard").

[0081] 2. Build and optimize the knowledge graph and store it in the graph database to form a dynamic knowledge graph library.

[0082] The knowledge graph theory method is used to transform the cause chain of vehicle emission anomalies into two types of entities and relationships in the knowledge graph. Based on expert knowledge, entity alignment technology is used to solve the problem of identical but different names in existing entities (for example, "SCR catalytic efficiency anomaly" and "SCR system catalytic rate reduction" both represent the entity "SCR catalytic efficiency reduction"). Entity alignment technology is used to merge relatively conflicting entities to achieve the unification of knowledge representation in the knowledge graph.

[0083] Finally, the above knowledge graph is stored in a graph database (such as Neo4j) to build a preset vehicle emission anomaly knowledge graph library, support efficient path query and reasoning, and build a complete vehicle emission anomaly cause knowledge graph. The vehicle emission anomaly knowledge graph library is as follows: Figure 4 shown.

[0084] 3. Obtain remote monitoring data for cluster analysis and establish connections between cluster center features and knowledge graph relationships.

[0085] For example, the historical operating data of 500 heavy-duty vehicles with abnormal emissions for one month are remotely obtained. The cumulative mileage of all vehicles exceeds 50,000 kilometers, and the K-Means clustering method is used for cluster analysis to obtain multi-dimensional cluster centers. The cluster centers are used to represent each relationship chain in the preset vehicle emission abnormality knowledge graph library.

[0086] First, the remote monitoring data of heavy-duty vehicles with a sampling frequency of 1 Hz is obtained, including vehicle speed, acceleration, engine net output torque, engine speed, engine fuel flow, engine coolant temperature, DPF pressure difference, DPF regeneration frequency, ambient temperature, intake pressure, exhaust temperature, exhaust back pressure, air-fuel ratio, EGR valve opening, urea injection amount, particulate matter concentration and SCR downstream NOx transmitter output value parameters.

[0087] Then, the heavy-duty vehicle remote monitoring data was used as input, and the K-Means clustering method was used for cluster analysis to obtain the multi-dimensional cluster centers, and the multi-dimensional features of each cluster center were extracted (such as "vehicle speed v < 25 km / h; acceleration a ≥ 0.32 m / s 2 ” → “Low-speed acceleration condition” → “NOx exceeds the standard”; “Vehicle speed v ≥ 50 km / h; Acceleration a ≤ -0.68 m / s 2 ” → “High-speed deceleration condition” → “NOx exceeds the standard”).

[0088] Finally, based on the multi-dimensional characteristics of each cluster center, it is linked to each relationship chain in the preset vehicle emission anomaly knowledge graph library to support the tracing of the cause of heavy-duty vehicle emission anomalies.

[0089] 4. Establish a dynamic threshold model and LSTM model, and integrate the detection results to determine whether the emission is abnormal.

[0090] First, a preset dynamic threshold model is built to detect whether heavy-duty vehicle emissions are abnormal, including: The first step is to establish four types of heavy-duty vehicle operating conditions: urban and suburban conditions (vehicle speed less than 50 km / h, idle ratio higher than 18%), high-speed cruising conditions (vehicle speed higher than 70 km / h, accelerator pedal opening fluctuation rate lower than 10%), climbing conditions (engine torque higher than 70%, slope greater than 6%), and low-temperature cold start conditions (coolant temperature lower than 40°C, ambient temperature lower than 10°C).

[0091] In the second step, the remote monitoring data of heavy-duty vehicles was processed using the 30-s moving window averaging method, and divided into urban and suburban conditions, high-speed cruising conditions, climbing conditions, and low-temperature cold start conditions according to the data characteristics. The 95% quantile of NOx emissions under each condition was calculated as the emission abnormality threshold, and a mapping relationship between the preset operating conditions and the preset emission abnormality threshold was established.

[0092] The third step is to determine the corresponding preset emission abnormality threshold according to the current operating conditions. If the emissions of the heavy-duty vehicle exceed the preset emission abnormality threshold corresponding to the current operating conditions for two consecutive hours during operation, the first detection result meets the preset vehicle emission abnormality judgment conditions, directly judges the heavy-duty vehicle emissions as abnormal, and directly triggers the emission abnormality tracing analysis.

[0093] Then, the preset emission prediction model (i.e., LSTM model) is constructed to detect whether the heavy-duty vehicle emissions are abnormal. The steps are as follows: In the first step, the historical data of heavy-duty vehicles obtained remotely is used as training data, the vehicle speed, acceleration, torque, speed, and fuel consumption rate in the data are used as input, and the NOx emission rate is used as output. The LSTM neural network is used to train and build an LSTM machine learning model to predict heavy-duty vehicle emissions.

[0094] In the second step, if the emissions of a heavy-duty vehicle exceed the predicted value output by the preset emission prediction model by 1.5 times for two consecutive hours during its operation, the second detection result meets the preset conditions for determining vehicle emission abnormalities, and the heavy-duty vehicle is directly determined to have abnormal emissions, directly triggering the emission abnormality tracing analysis.

[0095] Finally, a collaborative detection mechanism between the preset dynamic threshold model and the preset emission prediction model is established to accurately trigger the source analysis of emission anomalies. Specifically, it includes: In the first step, a first detection result is obtained by using a preset dynamic threshold model and a second detection result is obtained by using a preset emission prediction model.

[0096] In the second step, if the first detection result and the second detection result do not meet the preset conditions for determining vehicle emission abnormality, the first detection result output by the preset dynamic threshold model is merged with the second detection result output by the preset emission prediction model to obtain a final detection result.

[0097] 5. Calculate and visualize potential causes and probabilities of abnormal data, and generate maintenance recommendations with the help of AI models.

[0098] Based on the preset vehicle emission anomaly knowledge graph library and the Cosine similarity calculation strategy, the potential causes and occurrence probabilities of heavy-duty vehicle emission anomalies are calculated and obtained, thus realizing multi-dimensional tracing and source analysis of heavy-duty vehicle emission anomalies.

[0099] Taking a 25t heavy-duty vehicle of a certain model with known emission anomalies as an example, the present invention is used to conduct multi-dimensional cause tracing analysis. First, the current operating data of the remote monitoring target heavy-duty vehicle is extracted. The data sampling frequency is 1 Hz. The data content includes vehicle VIN, acquisition time, vehicle speed, atmospheric pressure, engine net output torque, engine speed, engine fuel flow, etc., and the data is preprocessed by deleting null values ​​and abnormal values ​​whose characteristic values ​​do not meet the value range.

[0100] Then, the Cosine similarity calculation method is used to calculate the data features of each relationship chain in the remote monitoring data of heavy-duty vehicles with abnormal emissions and the preset vehicle emission abnormality knowledge graph library, and the Cosine similarity calculation results are obtained. The top three relationship chains in the results are taken as the potential causes of abnormal emissions of heavy-duty vehicles, and the occurrence probability of the corresponding potential causes is obtained. Here, the top three can also be understood as the three most similar relationship chains in the results.

[0101] Finally, the potential causes of abnormal emissions of heavy-duty vehicles and their probability of occurrence are visualized, and vehicle maintenance suggestions are generated synchronously in combination with large AI models such as Deepseek or ChatGPT. It supports viewing on PC and mobile terminals, and the output visualization results are shown in Table 1.

[0102] Table 1

[0103] In summary, the beneficial effects of the embodiments of the present invention are as follows: (1) Establish a collaborative detection mechanism that integrates dynamic thresholds and LSTM time series prediction models. Through weight collaboration, the false alarm rate and missed detection rate of heavy-duty vehicle emission anomaly detection are greatly reduced, ensuring the accuracy and stability of anomaly identification. (2) The relationship chain modeling in the emission anomaly knowledge graph library built based on the Neo4j graph database is combined with Cosine similarity to quickly match potential fault paths, shorten the tracing time, improve the accuracy, and support a dynamic update mechanism to continuously absorb maintenance cases and real-time data to enhance the practicality of the knowledge base.

[0104] (3) Remotely monitor multiple parameters of the vehicle. The system can trigger abnormal warnings and traceability analysis in a short period of time and link the AI ​​big model to automatically generate maintenance suggestions, which are pushed in real time through a visual interface.

[0105] According to the vehicle emission anomaly detection and tracing method proposed in the embodiment of the present invention, the current operating condition and the current operating data are input into a preset dynamic threshold model to obtain a first detection result, and the current operating condition and the current operating data are input into a preset emission prediction model to obtain a second detection result, wherein the preset dynamic threshold model is constructed by a plurality of preset operating conditions and a preset emission abnormality threshold corresponding to each preset operating condition, and the preset emission prediction model is obtained by training the LSTM neural network with the historical operating data of the vehicle; when the first detection result and the second detection result do not meet the preset conditions for determining vehicle emission anomaly, the first detection result and the second detection result are fused to obtain the final detection result, and when the final detection result is greater than or equal to the preset threshold, the vehicle emission is determined to be abnormal, and based on the vehicle emission anomaly knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of vehicle emission anomaly. Thus, the traditional vehicle emission anomaly detection and tracing method solves the problems of low emission anomaly detection efficiency, high misjudgment rate, and difficult tracing due to data fragmentation and complex causal relationships, and realizes accurate detection of emission anomalies.

[0106] Next, the vehicle emission abnormality detection and source tracing device proposed in an embodiment of the present invention will be described with reference to the accompanying drawings.

[0107] Figure 5 It is a block diagram of a vehicle emission abnormality detection and tracing device according to an embodiment of the present invention.

[0108] like Figure 5 As shown, the vehicle emission abnormality detection and tracing device 10 includes: an acquisition module 100, a detection module 200 and a tracing module 300.

[0109] Among them, the acquisition module 100 is used to obtain the current operating condition and current operating data of the vehicle; the detection module 200 is used to input the current operating condition and the current operating data into a preset dynamic threshold model to obtain a first detection result, and input the current operating condition and the current operating data into a preset emission prediction model to obtain a second detection result, wherein the preset dynamic threshold model is constructed by multiple preset operating conditions and a preset emission abnormality threshold corresponding to each preset operating condition, and the preset emission prediction model is obtained by training the LSTM neural network with the historical operating data of the vehicle; the tracing module 300 is used to fuse the first detection result and the second detection result to obtain the final detection result when the first detection result and the second detection result do not meet the preset vehicle emission abnormality judgment condition, and when the final detection result is greater than or equal to the preset threshold, judge that the vehicle emission is abnormal, and based on the preset vehicle emission abnormality knowledge graph library, use the Cosine similarity calculation strategy to obtain the cause and probability of the vehicle emission abnormality.

[0110] Optionally, in some embodiments, the first detection result includes the actual emissions of the vehicle. When judging whether the first detection result meets the preset conditions for determining vehicle emissions abnormality, the detection module 200 is further used to: judge whether the actual emissions of the vehicle are greater than a preset emissions abnormality threshold, and whether the first duration of the actual emissions of the vehicle being greater than the preset emissions abnormality threshold is greater than a first preset duration, wherein the preset emissions abnormality threshold is obtained by the current operating conditions; if the actual emissions of the vehicle are greater than the preset emissions abnormality threshold and the first duration is greater than the first preset duration, then it is judged that the first detection result meets the preset conditions for determining vehicle emissions abnormality, and the vehicle emissions are judged to be abnormal, and based on the preset vehicle emissions abnormality knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of the vehicle emissions abnormality, otherwise, it is judged that the first detection result does not meet the preset conditions for determining vehicle emissions abnormality.

[0111] Optionally, in some embodiments, before determining whether the first detection result is greater than a preset emission abnormality threshold, the detection module 200 is further used to: obtain historical operating data of the vehicle; construct multiple preset operating conditions based on data features in the historical operating data; establish a mapping relationship between the historical operating data of each preset operating condition and the emission abnormality threshold, so as to obtain the preset emission abnormality threshold according to the current operating condition based on the mapping relationship.

[0112] Optionally, in some embodiments, the second detection result includes the actual emissions of the vehicle. When judging whether the second detection result meets the preset conditions for determining vehicle emissions abnormality, the detection module 200 is further used to: judge whether the ratio of the actual emissions of the vehicle to the predicted emissions is greater than the preset ratio, and whether the second duration for which the actual emissions of the vehicle are greater than the predicted emissions is greater than the second preset duration; if the ratio of the actual emissions of the vehicle to the predicted emissions is greater than the preset ratio, and the second duration is greater than the second preset duration, then it is judged that the second detection result meets the preset conditions for determining vehicle emissions abnormality, and the vehicle emissions are judged to be abnormal, and based on the preset vehicle emissions abnormality knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of the vehicle emissions abnormality, otherwise, it is judged that the second detection result does not meet the preset conditions for determining vehicle emissions abnormality.

[0113] Optionally, in some embodiments, the traceability module 300 is also used to: obtain historical operating data of the vehicle; based on the historical operating data, use the Cosine similarity calculation strategy to calculate the data features of each relationship chain in the preset vehicle emission anomaly knowledge graph library, and obtain multiple Cosine similarity calculation results; based on a preset sorting strategy, sort the multiple Cosine similarity calculation results, and use the Cosine similarity calculation results that meet the preset ranking conditions in the sorting results as the cause and probability of the vehicle emission anomaly.

[0114] Optionally, in some embodiments, before obtaining the cause and probability of vehicle emission anomaly based on a preset vehicle emission anomaly knowledge graph library and using a Cosine similarity calculation strategy, the traceability module 300 is also used to: obtain vehicle emission anomaly cause data; form a vehicle emission anomaly cause data relationship chain based on the vehicle emission anomaly cause data; use a preset knowledge graph theory to convert the vehicle emission anomaly cause data relationship chain into entities and relationships in the knowledge graph; use an entity alignment strategy to perform entity fusion on relatively conflicting entities to obtain a preset vehicle emission anomaly knowledge graph library.

[0115] Optionally, in some embodiments, the traceability module 300 is further used to: use a preset fusion formula to fuse the first detection result and the second detection result to obtain a final detection result, wherein the preset fusion formula is: ; in, For the final test results, E car is the actual emission of the vehicle, E Th is the preset emission abnormality threshold, E Pv is the emission prediction value, a is the weight of the first detection result, b is the weight of the second detection result.

[0116] Optionally, in some embodiments, after determining that the vehicle emissions are abnormal and obtaining the cause and probability of the vehicle emissions abnormality based on a preset vehicle emissions abnormality knowledge graph library using a Cosine similarity calculation strategy, the traceability module 300 is also used to: send vehicle emissions abnormality warning information to a preset terminal; and generate vehicle maintenance suggestions using an AI model based on the vehicle emissions abnormality warning information, the cause and probability of the vehicle emissions abnormality.

[0117] It should be noted that the aforementioned explanations and descriptions of the embodiment of the vehicle emission abnormality detection and tracing method are also applicable to the vehicle emission abnormality detection and tracing device of this embodiment, and will not be repeated here.

[0118] According to the vehicle emission anomaly detection and tracing device proposed in an embodiment of the present invention, the current operating condition and the current operating data are input into a preset dynamic threshold model to obtain a first detection result, and the current operating condition and the current operating data are input into a preset emission prediction model to obtain a second detection result, wherein the preset dynamic threshold model is constructed by multiple preset operating conditions and a preset emission abnormality threshold corresponding to each preset operating condition, and the preset emission prediction model is obtained by training an LSTM neural network with historical operating data of the vehicle; when the first detection result and the second detection result do not meet the preset conditions for determining vehicle emission abnormality, the first detection result and the second detection result are fused to obtain a final detection result, and when the final detection result is greater than or equal to the preset threshold, the vehicle emission is determined to be abnormal, and based on the vehicle emission anomaly knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of the vehicle emission anomaly. As a result, the problems of traditional vehicle emission anomaly detection and tracing methods, such as low efficiency, high misjudgment rate and difficult tracing due to data fragmentation and complex causal relationships, are solved. Accurate detection of emission anomalies is achieved, and closed-loop management from anomaly triggering to root cause tracing is completed, achieving the goals of reducing vehicle emission pollution, improving the level of intelligent operation and maintenance, and ensuring environmental compliance.

[0119] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include: A memory 601 , a processor 602 , and a computer program stored in the memory 601 and executable on the processor 602 .

[0120] When the processor 602 executes the program, the vehicle emission abnormality detection and tracing method provided in the above embodiment is implemented.

[0121] Furthermore, the electronic device further comprises: The communication interface 603 is used for communication between the memory 601 and the processor 602 .

[0122] The memory 601 is used to store computer programs that can be executed on the processor 602 .

[0123] The memory 601 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0124] If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0125] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can communicate with each other through an internal interface.

[0126] The processor 602 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0127] An embodiment of the present invention further provides a computer program product having a computer program stored thereon, which implements the above-mentioned vehicle emission anomaly detection and tracing method when executed by a processor.

[0128] In the description of this specification, 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 invention. 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 N 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.

[0129] 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 the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0130] 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 invention 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 reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0131] 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 specifically implemented in any computer program product for use with 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 purposes of this specification, a "computer program product" can be any device that can contain, store, communicate, propagate or transmit a program for use with an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer program products (a non-exhaustive list) include the following: an electrical connection with one or N wirings (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), an optical fiber device, and a portable compact disk read-only memory (CDROM). Furthermore, the computer program product 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, if necessary, processing it in another suitable manner, and then storing it in a computer memory.

[0132] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N 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 or combination of the following technologies known in the art: 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.

[0133] 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 program product, which, when executed, includes one or a combination of the steps of the method embodiment.

[0134] In addition, each functional unit in each embodiment of the present invention 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 program product.

[0135] The computer program product mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for detecting and tracing vehicle emission anomalies, characterized in that: The following steps are involved: Obtain the current operating conditions and current operating data of the vehicle; Inputting the current operating condition and the current operating data into a preset dynamic threshold model to obtain a first detection result, and inputting the current operating condition and the current operating data into a preset emission prediction model to obtain a second detection result, wherein the preset dynamic threshold model is constructed by a plurality of preset operating conditions and a preset emission abnormality threshold corresponding to each preset operating condition, and the preset emission prediction model is obtained by training an LSTM neural network with historical operating data of the vehicle; When the first detection result and the second detection result do not meet the preset conditions for determining vehicle emission abnormality, the first detection result and the second detection result are fused to obtain a final detection result. When the final detection result is greater than or equal to a preset threshold, the vehicle emission is determined to be abnormal, and based on a preset vehicle emission abnormality knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of the vehicle emission abnormality.

2. The vehicle emission abnormality detection and tracing method according to claim 1, characterized in that: The first detection result includes the actual emission of the vehicle. When determining whether the first detection result meets the preset condition for determining abnormal vehicle emissions, it includes: Determine whether the actual emission of the vehicle is greater than a preset emission abnormality threshold, and whether a first duration during which the actual emission of the vehicle is greater than the preset emission abnormality threshold is greater than a first preset duration, wherein the preset emission abnormality threshold is obtained from the current operating condition; If the actual emissions of the vehicle are greater than the preset emissions abnormality threshold and the first duration is greater than the first preset duration, it is determined that the first detection result meets the preset vehicle emissions abnormality determination condition, and the vehicle emissions are determined to be abnormal. Based on the preset vehicle emissions abnormality knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of the vehicle emissions abnormality. Otherwise, it is determined that the first detection result does not meet the preset vehicle emissions abnormality determination condition.

3. The vehicle emission abnormality detection and tracing method according to claim 2, characterized in that: Before determining whether the first detection result is greater than the preset emission abnormality threshold, the method further includes: Acquiring historical operation data of the vehicle; Constructing the plurality of preset operating conditions according to data features in the historical operating data; A mapping relationship between the historical operating data of each preset operating condition and the emission abnormality threshold is established, so as to obtain the preset emission abnormality threshold according to the current operating condition based on the mapping relationship.

4. The vehicle emission abnormality detection and tracing method according to claim 1, characterized in that: The second detection result includes the actual emission of the vehicle. When judging whether the second detection result meets the preset condition for determining abnormal vehicle emissions, it also includes: Determine whether a ratio of the actual emission of the vehicle to the emission prediction value is greater than a preset ratio, and whether a second duration during which the actual emission of the vehicle is greater than the emission prediction value is greater than a second preset duration; If the ratio of the actual emissions of the vehicle to the predicted emissions is greater than the preset ratio, and the second duration is greater than the second preset duration, it is determined that the second detection result meets the preset conditions for determining vehicle emissions abnormality, and the vehicle emissions are determined to be abnormal. Based on the preset vehicle emissions abnormality knowledge graph library, the Cosine similarity calculation strategy is used to obtain the cause and probability of the vehicle emissions abnormality. Otherwise, it is determined that the second detection result does not meet the preset conditions for determining vehicle emissions abnormality.

5. The vehicle emission abnormality detection and tracing method according to claim 1, characterized in that: The method based on the preset vehicle emission anomaly knowledge graph library uses the Cosine similarity calculation strategy to obtain the cause and probability of vehicle emission anomaly, including: Acquiring historical operation data of the vehicle; Based on the historical operation data, the Cosine similarity calculation strategy is used to calculate the data features of each relationship chain in the preset vehicle emission anomaly knowledge graph library to obtain multiple Cosine similarity calculation results; Based on a preset sorting strategy, the plurality of Cosine similarity calculation results are sorted, and the Cosine similarity calculation results that meet a preset ranking condition in the sorting results are used as the cause and probability of the abnormal emission of the vehicle.

6. The vehicle emission abnormality detection and tracing method according to claim 5, characterized in that: Before obtaining the cause and probability of vehicle emission anomaly based on the preset vehicle emission anomaly knowledge graph library and using the Cosine similarity calculation strategy, the following steps are included: Obtain data on the causes of abnormal vehicle emissions; Forming a vehicle emission abnormality cause data relationship chain based on the vehicle emission abnormality cause data; Using the preset knowledge graph theory, the data relationship chain of the vehicle emission abnormality cause is converted into entities and relationships in the knowledge graph; The relatively conflicting entities are fused using an entity alignment strategy to obtain the preset vehicle emission anomaly knowledge graph library.

7. The vehicle emission abnormality detection and tracing method according to claim 1, characterized in that: The fusing the first detection result and the second detection result to obtain a final detection result includes: The first detection result and the second detection result are fused using a preset fusion formula to obtain the final detection result, wherein the preset fusion formula is: ； in, For the final test results, E car is the actual emission of the vehicle, E Th is the preset emission abnormality threshold, E Pv is the emission prediction value, a is the weight of the first detection result, b is the weight of the second detection result.

8. The vehicle emission anomaly detection and tracing method according to claim 1, characterized in that: After determining that the vehicle emission is abnormal, and obtaining the cause and probability of the vehicle emission abnormality by using the Cosine similarity calculation strategy based on the preset vehicle emission abnormality knowledge graph library, the following steps are included: Send vehicle emission abnormal warning information to the preset terminal; Based on the vehicle emission abnormality warning information, the cause and probability of the vehicle emission abnormality, an AI model is used to generate vehicle maintenance suggestions.

9. A vehicle emission abnormality detection and tracing device, characterized in that: include: An acquisition module, used to acquire the current operating condition and current operating data of the vehicle; A detection module, used for inputting the current operating condition and the current operating data into a preset dynamic threshold model to obtain a first detection result, and inputting the current operating condition and the current operating data into a preset emission prediction model to obtain a second detection result, wherein the preset dynamic threshold model is constructed by a plurality of preset operating conditions and a preset emission abnormality threshold corresponding to each preset operating condition, and the preset emission prediction model is obtained by training an LSTM neural network with historical operating data of the vehicle; The tracing module is used to fuse the first detection result and the second detection result to obtain a final detection result when the first detection result and the second detection result do not meet the preset conditions for determining vehicle emission abnormality, and to determine that the vehicle emission is abnormal when the final detection result is greater than or equal to a preset threshold, and based on a preset vehicle emission abnormality knowledge graph library, use the Cosine similarity calculation strategy to obtain the cause and probability of the vehicle emission abnormality.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle emission abnormality detection and tracing method as described in any one of claims 1 to 8.

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