Fault diagnosis method, device, equipment, medium and product

Abstract

The invention relates to a fault diagnosis method and device, equipment, a medium and a product, and the method comprises the steps: obtaining carrier temperature data and node temperature data in a preset time interval, and enabling the carrier temperature data to be data obtained through the temperature estimation of a carrier in a post-processing system, the node temperature data is data obtained by performing temperature detection on each node in the post-processing system; obtaining carrier average temperature and node average temperature according to the carrier temperature data and the node temperature data; obtaining a theoretical temperature interval according to the carrier average temperature and the node average temperature; and based on the theoretical temperature interval, performing comparison processing on the node average temperature corresponding to the target node temperature sensor to obtain a diagnosis result. By adopting the method, the reliability of the diagnosis result and the accuracy of the fault diagnosis method are improved.

Description

Technical Field

[0001] The present application relates to the technical field of fault diagnosis, and in particular to a fault diagnosis method, device, equipment, medium and product. Background Art

[0002] The exhaust gas treatment process in the after-treatment system has strict requirements on temperature. In order to avoid inaccurate temperature data measurement caused by temperature sensor failure in the after-treatment system, which will affect the exhaust gas treatment effect, it is necessary to monitor the temperature sensor in the after-treatment system in real time and perform fault diagnosis.

[0003] In the related art, the fault diagnosis results of the temperature sensor involved in different working conditions of the engine are obtained by comparing the temperature change of the diesel engine under different working conditions with the estimated reasonable temperature range.

[0004] However, current temperature sensor fault diagnosis methods have low accuracy. Summary of the invention

[0005] Based on this, it is necessary to provide a fault diagnosis method, device, equipment, medium and product that can improve the accuracy of fault diagnosis in response to the above technical problems.

[0006] In a first aspect, the present application provides a fault diagnosis method, which is applied to a target node temperature sensor in a post-processing system, and the method comprises:

[0007] Acquire carrier temperature data and node temperature data within a preset time interval, wherein the carrier temperature data is data obtained by estimating the temperature of the carrier in the post-processing system, and the node temperature data is data obtained by detecting the temperature of each node in the post-processing system;

[0008] Obtaining a carrier average temperature and a node average temperature according to the carrier temperature data and the node temperature data;

[0009] Obtaining a theoretical temperature range according to the average temperature of the carrier and the average temperature of the node;

[0010] Based on the theoretical temperature range, the node average temperature corresponding to the target node temperature sensor is compared and processed to obtain a diagnosis result.

[0011] In one embodiment, the provided fault diagnosis method further includes:

[0012] Acquiring status data corresponding to the post-processing system;

[0013] Determining a catalytic reduction efficiency corresponding to the post-treatment system according to the state data;

[0014] When the catalytic reduction efficiency meets the preset enabling condition, the process of acquiring carrier temperature data and node temperature data is performed until the diagnosis result is obtained.

[0015] In one embodiment, the process of obtaining the carrier average temperature and the node average temperature according to the carrier temperature data and the node temperature data in the provided fault diagnosis method includes:

[0016] Performing mean processing on the temperature data of each carrier within the time interval to obtain the average temperature of the carrier;

[0017] The temperature data of each node within the time interval are averaged to obtain the average temperature of the node.

[0018] In one embodiment, the node temperature data in the provided fault diagnosis method includes: a first node temperature and a second node temperature, the first node temperature is data obtained by temperature detection of a node upstream of a diesel particulate filter, and the second node temperature is data obtained by temperature detection of a node upstream of a catalytic reducer; the node average temperature includes: a first node average temperature and a second node average temperature;

[0019] The process of obtaining the theoretical temperature range according to the average temperature of the carrier and the average temperature of the node in the provided fault diagnosis method includes:

[0020] Performing linear operation processing on the average temperature of the carrier, the average temperature of the second node, and a preset upper limit correction coefficient to obtain interval upper limit data;

[0021] Performing linear operation processing on the average temperature of the carrier, the average temperature of the second node, and a preset lower limit correction coefficient to obtain interval lower limit data;

[0022] The theoretical temperature interval is obtained according to the interval upper limit data and the interval lower limit data.

[0023] In one embodiment, the fault diagnosis method provided herein comprises the following steps: comparing the average node temperature corresponding to the target node temperature sensor based on the theoretical temperature range to obtain a diagnosis result, including:

[0024] When the average temperature of the first node exceeds the theoretical temperature range, taking the state abnormality as a diagnosis result;

[0025] When the average temperature of the first node meets the theoretical temperature range, the state is taken as a normal diagnosis result.

[0026] In one embodiment, the provided fault diagnosis method further includes:

[0027] When the diagnosis result indicates that the state is abnormal, a fault alarm signal is generated.

[0028] In a second aspect, the present application also provides a fault diagnosis device, which is applied to a target node temperature sensor in a post-processing system, and the device comprises:

[0029] A data acquisition module, used to acquire carrier temperature data and node temperature data within a preset time interval, wherein the carrier temperature data is data obtained by estimating the temperature of the carrier in the post-processing system, and the node temperature data is data obtained by detecting the temperature of each node in the post-processing system;

[0030] A mean value calculation module, used for obtaining a carrier average temperature and a node average temperature according to the carrier temperature data and the node temperature data;

[0031] An interval calculation module, used for obtaining a theoretical temperature interval according to the average temperature of the carrier and the average temperature of the node;

[0032] The fault diagnosis module is used to compare and process the node average temperature corresponding to the target node temperature sensor based on the theoretical temperature range to obtain a diagnosis result.

[0033] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in the first aspect are implemented.

[0034] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0035] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0036] The above-mentioned fault diagnosis method, device, equipment, medium and product, one aspect of which provides a fault diagnosis method comprising: obtaining carrier temperature data and node temperature data within a preset time interval, the carrier temperature data is data obtained by estimating the temperature of the carrier in the post-processing system, and the node temperature data is data obtained by detecting the temperature of each node in the post-processing system; obtaining the carrier average temperature and the node average temperature according to the carrier temperature data and the node temperature data; obtaining a theoretical temperature interval according to the carrier average temperature and the node average temperature; based on the theoretical temperature interval, comparing and processing the node average temperature corresponding to the target node temperature sensor to obtain a diagnosis result. In the present application, by using the carrier temperature data and the node temperature data to calculate the theoretical temperature interval of the target node temperature sensor, the temperature data range of the target node temperature sensor can be estimated more accurately, avoiding the problem that a single data affects the estimation result and causes deviation in the overall diagnosis result, thereby improving the reliability of the diagnosis result and the accuracy of the fault diagnosis method. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] FIG1 is an application environment diagram of a fault diagnosis method in one embodiment;

[0039] FIG2 is a schematic diagram of a flow chart of a fault diagnosis method in one embodiment;

[0040] FIG3 is a schematic flow chart of a fault diagnosis method in another embodiment;

[0041] FIG4 is a block diagram of a fault diagnosis device in one embodiment;

[0042] FIG5 is a diagram showing the internal structure of a computer device in one embodiment;

[0043] Description of reference numerals:

[0044] 1. Exhaust system; 2. Aftertreatment system; 21. Diesel oxidation catalyst; 211. DOC upstream node temperature sensor; 22. Diesel particulate filter; 221. DPF upstream node temperature sensor; 23. Urea injection device; 24. Mixer; 25. Selective catalytic reduction catalyst; 251. SCR upstream node temperature sensor; 252. SCR downstream node temperature sensor; 26. Ammonia slip catalyst; 27. Upstream NO xSensor; 28, downstream NO x Sensor; 3. Intake system; 4. Supercharger. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] The fault diagnosis method provided in the embodiment of the present application can be applied to the target node temperature sensor in the aftertreatment system 2 as shown in FIG1. ​​As shown in FIG1, the aftertreatment system 2 is connected to the output end of the exhaust system 1 of the diesel engine through the supercharger 4. The exhaust gas generated when the engine is working enters the aftertreatment system 2 through the exhaust system 1. The exhaust gas contains nitrogen oxides (hereinafter referred to as NO x ), hydrocarbons (hereinafter referred to as HC), carbon monoxide (hereinafter referred to as CO) and particulate matter (hereinafter referred to as PM). The exhaust gas first enters the diesel oxidation catalyst 21 (Diesel Oxidation Catalyst, DOC), where the CO and HC in the exhaust gas are oxidized into carbon dioxide and water through oxidation reactions. At the same time, part of the NO is oxidized into NO2. Next, the exhaust gas passes through the diesel particulate filter 22 (Diesel Particulate Filter, DPF) to capture and filter the PM in the exhaust gas. When the PM captured by the DPF reaches a certain amount, the PM is burned through the regeneration process to remove the carbon deposits in the DPF and maintain the filtering effect. After passing through the DPF, the exhaust gas enters the urea injection device 23, which injects urea solution into the exhaust gas to fully mix it with the exhaust gas and generate ammonia (hereinafter referred to as NH3) at high temperature. After passing through the mixer 24, the exhaust gas enters the selective catalytic reduction (Selective Catalytic Reduction, SCR) catalyst 25. Under the action of the SCR catalyst, NH3 and NO in the exhaust gas x A reduction reaction occurs, reducing harmful NOx to harmless nitrogen and water, significantly reducing NOx emissions in the exhaust gas. Finally, the exhaust gas enters the ammonia slip catalyst 26 (Ammonia Slip Catalyst, ASC), which converts the remaining NH3 into nitrogen and water through a catalytic reaction, preventing NH3 from escaping into the atmosphere and ensuring that the NH3 content in the exhaust gas meets the emission standards.

[0047] The fault diagnosis method provided in the present application can be applied to a terminal or a server. The terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices, and portable wearable devices, and the server can be implemented as an independent server or a server cluster consisting of multiple servers.

[0048] In an exemplary embodiment, as shown in FIG2 , a fault diagnosis method is provided, which is applied to a target node temperature sensor in a post-processing system 2 . The method is applied to a server as an example for explanation, and includes the following steps 202 to 206 . Among them:

[0049] Step 202: Obtain the carrier temperature data T within a preset time interval. C and node temperature data T N .

[0050] The carrier temperature data TC is data obtained by estimating the temperature of the carrier in the post-processing system 2, and the node temperature data TN is data obtained by detecting the temperature of each node in the post-processing system 2. For example, as shown in FIG. 1, the node temperature data T N The data may be obtained by performing temperature detection on the DOC upstream node temperature sensor 211 , the DPF upstream node temperature sensor 221 , the DPF downstream node temperature sensor, the SCR upstream node temperature sensor 251 , and the SCR downstream node temperature sensor 252 in the post-processing system 2 .

[0051] The carrier in the post-treatment system 2 is NH3 generated by the reaction of urea solution and exhaust gas. When the urea supply is normal and the NH3 storage is sufficient, the SCR conversion efficiency mainly depends on the temperature of the carrier. The carrier temperature T C When the temperature is greater than 280°C, the average conversion efficiency of SCR can reach more than 95%. The temperature estimation of the carrier can be based on a preset carrier temperature estimation model, such as a mathematical model based on heat transfer and reaction kinetics, taking into account parameters such as the flow rate of the exhaust gas and the injection amount of urea to estimate the carrier temperature data.

[0052] Step 204: according to the carrier temperature data T C and node temperature data T N , get the average carrier temperature T CA and the average node temperature T NA .

[0053] In a possible implementation, step 204 further includes: performing mean processing on each carrier temperature data TC within the time interval to obtain the carrier average temperature T CA ; For each node temperature data in the time interval T N Perform mean processing to obtain the average node temperature TNA .

[0054] The mean value processing may specifically include processing the carrier temperature data T corresponding to each moment in the time interval. C Perform summation to obtain the carrier temperature accumulation value, perform ratio calculation on the carrier temperature accumulation value and the duration corresponding to the time interval to obtain the carrier average temperature T CA ; For each node temperature data T N Perform summation to obtain the node temperature accumulation value, perform ratio calculation on the node temperature accumulation value and the duration to obtain the node average temperature T NA .

[0055] Step 206, according to the average temperature T of the carrier CA and the average node temperature T NA , and obtain the theoretical temperature range.

[0056] Step 208 : Based on the theoretical temperature range, the average temperature of the node corresponding to the target node temperature sensor is compared to obtain a diagnosis result.

[0057] Exemplarily, the target node temperature sensor may be a temperature sensor located at a DOC upstream node, a DPF upstream node, a DPF downstream node, an SCR upstream node, or an SCR downstream node in the post-treatment system 2 .

[0058] The above fault diagnosis method is applied to the target node temperature sensor in the post-processing system 2, and the method includes: obtaining carrier temperature data and node temperature data within a preset time interval, the carrier temperature data is data obtained by estimating the temperature of the carrier in the post-processing system 2, and the node temperature data is data obtained by detecting the temperature of each node in the post-processing system 2; according to the carrier temperature data and the node temperature data, obtaining the carrier average temperature and the node average temperature; according to the carrier average temperature and the node average temperature, obtaining the theoretical temperature interval; based on the theoretical temperature interval, comparing and processing the node average temperature corresponding to the target node temperature sensor to obtain a diagnosis result. In this embodiment, the theoretical temperature interval of the target node temperature sensor is calculated using the carrier temperature data and the node temperature data, so that the temperature data range of the target node temperature sensor can be estimated more accurately, avoiding the problem that a single data affects the estimation result and causes deviation in the overall diagnosis result, thereby improving the reliability of the diagnosis result and the accuracy of the fault diagnosis method.

[0059] In an exemplary embodiment, based on the embodiment shown in FIG. 2 , the provided method also includes: acquiring status data corresponding to the after-treatment system 2; determining the catalytic reduction efficiency corresponding to the after-treatment system 2 based on the status data; and when the catalytic reduction efficiency meets the preset enabling conditions, executing the process of acquiring the carrier temperature data and the node temperature data until a diagnostic result is obtained.

[0060] In a possible implementation, the state data includes: environmental state data and system state data, wherein the environmental state data includes state data corresponding to the external environment in which the aftertreatment system 2 is located, such as atmospheric temperature and pressure; the system state data includes: engine state conditions, such as operating time, operating conditions, combustion mode, coolant temperature; actuator state conditions, such as boost pressure conditions, EGR valve conditions, throttle valve conditions, urea nozzle conditions; related sensor working state conditions; upstream NO x Status, such as upstream NO x concentration, upstream NO x Mass flow, filtered upstream NO x The mass flow rate and SCR system status, such as SCR carrier temperature, NH3 storage, SCR downstream temperature gradient, urea injection amount and SCR system operating mode. x Sensor 27 detects the upstream NO x Status and downstream NO x The downstream NO detected by sensor 28 x The status is calculated.

[0061] Among them, the preset enabling conditions may include that the catalytic reduction efficiency is greater than or equal to a high efficiency threshold within an enabling interval of a preset time length. Exemplarily, the high efficiency threshold may be 95%. When the catalytic reduction efficiency meets the enabling conditions, the process of obtaining the diagnostic result as in the above embodiment is performed; when the catalytic reduction efficiency does not meet the enabling conditions, the status data is continuously acquired, and it is determined whether the catalytic reduction efficiency meets the enabling conditions according to a preset time interval. Optionally, in the above embodiment, the time interval may be calculated after the catalytic reduction efficiency meets the enabling conditions, and the carrier temperature data and node data within the preset time interval may be acquired. Optionally, in the above embodiment, after the catalytic reduction efficiency meets the enabling conditions, a time interval of a preset length may be intercepted from the enabling interval, and the corresponding carrier temperature data and node temperature data may be acquired.

[0062] In this embodiment, when the catalytic reduction efficiency meets the preset enabling conditions, the target node temperature sensor is diagnosed for faults to obtain accurate and reliable diagnostic results, thereby avoiding the influence of the target node temperature sensor's temperature rise response lag and the external environment on the diagnostic results, thereby improving the accuracy of the fault diagnosis method.

[0063] In an exemplary embodiment, the target node temperature sensor is set at a node upstream of the DPF. Based on the above embodiment, the node temperature data in the provided method includes: the first node temperature T N1 and the second node temperature T N2 , the first node temperature T N1 The second node temperature T is the data obtained by performing temperature detection on the upstream node of the diesel particulate filter 22, that is, the upstream node of the DPF. N2 The data is obtained by temperature detection of the upstream node of the catalytic reducer, that is, the upstream node of the SCR; the node average temperature includes: the first node average temperature T NA1 and the second node average temperature T NA2 .

[0064] Among them, the average temperature of the first node is T NA1 is the temperature of the first node T N1 The temperature obtained by averaging; the average temperature of the second node T NA2 is the temperature of the second node T N2 The temperature obtained by averaging.

[0065] The method provided is based on the average carrier temperature T CA and the average node temperature T NA , get the theoretical temperature range, including: the average carrier temperature T CA , the average temperature of the second node T NA2 The preset upper limit correction coefficient M1 is processed by linear operation to obtain the upper limit data of the interval; the average temperature of the carrier T CA , the average temperature of the second node T NA2 And the preset lower limit correction coefficient M2 is used for linear operation processing to obtain the interval lower limit data; according to the interval upper limit data and the interval lower limit data, the theoretical temperature range is obtained.

[0066] Among them, the upper limit correction coefficient M1 and the lower limit correction coefficient M2 have a mapping relationship with the state data of the post-processing system 2. By acquiring the state data corresponding to the post-processing system 2 in the time interval, such as environmental state data such as atmospheric temperature and atmospheric pressure, the upper limit correction coefficient M1 and the lower limit correction coefficient M2 are obtained according to the state data and the mapping relationship. Exemplarily, the mapping relationship can be a two-dimensional MAP.

[0067] In a possible implementation manner, the interval upper limit data U can be expressed as: (2*T CA -T NA2 )*M1; the interval lower limit data F can be expressed as: (2*T CA -T NA2)*M2; The theoretical temperature range can be expressed as: [F, U].

[0068] In an exemplary embodiment, based on the above embodiment, the method provided in the present invention compares and processes the node average temperature corresponding to the target node temperature sensor based on the theoretical temperature range to obtain the diagnosis result, including: at the first node average temperature T NA1 If the temperature exceeds the theoretical temperature range, the abnormal state is used as the diagnosis result; at the first node average temperature T NA1 If the temperature is within the theoretical temperature range, the status is considered normal as the diagnosis result.

[0069] Among them, if the diagnosis result is an abnormal state, it means that the node temperature data collected by the target node temperature sensor exceeds the expected value range of the sensor, cannot reflect the actual working condition, and there is a credibility failure; if the diagnosis result is a normal state, it means that the node temperature data collected by the target node temperature sensor is reliable data.

[0070] In a possible implementation manner, when the diagnosis result is that the state is abnormal, a fault alarm signal is generated.

[0071] Among them, the fault alarm signal can be used to be transmitted to an alarm device, such as a fault light or an audible and visual alarm, to control the alarm device to alarm so that the staff can confirm the abnormal situation according to the alarm.

[0072] In this embodiment, the first node temperature T N1 and the second node temperature T N2 The average temperature is obtained by performing mean processing respectively, and then the accurate theoretical temperature range is obtained according to the average temperature and the correction coefficient, which reduces the risk of misdiagnosis and improves the accuracy of the diagnosis result.

[0073] In an exemplary embodiment, as shown in FIG3 , a fault diagnosis method is provided, which is applied to a target node temperature sensor in a post-processing system 2 . The method is applied to a server as an example for explanation, and includes the following steps 301 to 311 . Among them:

[0074] Step 301 , obtaining status data corresponding to the post-processing system 2 .

[0075] Step 302, determining the catalytic reduction efficiency corresponding to the post-treatment system 2 according to the state data;

[0076] Step 303 , determining whether the catalytic reduction efficiency meets the preset enabling condition, if so, proceeding to step 304 , if not, returning to step 301 .

[0077] Step 304: Acquire carrier temperature data and node temperature data within a preset time interval.

[0078] Among them, the carrier temperature data is the data obtained by estimating the temperature of the carrier in the after-treatment system 2, and the node temperature data is the data obtained by detecting the temperature of each node in the after-treatment system 2; the node temperature data includes: a first node temperature and a second node temperature, the first node temperature is the data obtained by detecting the temperature of the node upstream of the diesel particulate filter 22, and the second node temperature is the data obtained by detecting the temperature of the node upstream of the catalytic reducer.

[0079] Step 305, performing mean processing on the temperature data of each carrier within the time interval to obtain the average temperature of the carrier; performing mean processing on the temperature data of each node within the time interval to obtain the average temperature of the node.

[0080] The node average temperature includes: a first node average temperature and a second node average temperature.

[0081] Step 306, performing linear operation processing on the average temperature of the carrier, the average temperature of the second node, and the preset upper limit correction coefficient to obtain the upper limit data of the interval; performing linear operation processing on the average temperature of the carrier, the average temperature of the second node, and the preset lower limit correction coefficient to obtain the lower limit data of the interval;

[0082] Step 307, obtaining a theoretical temperature interval according to the interval upper limit data and the interval lower limit data.

[0083] Step 308: determine whether the average temperature of the first node exceeds the theoretical temperature range.

[0084] Step 309 , when the average temperature of the first node meets the theoretical temperature range, the state is considered normal as the diagnosis result.

[0085] Step 310 , when the average temperature of the first node exceeds the theoretical temperature range, taking the state abnormality as the diagnosis result.

[0086] Step 311: When the diagnosis result is that the state is abnormal, a fault alarm signal is generated.

[0087] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0088] Based on the same inventive concept, the embodiment of the present application also provides a fault diagnosis device for implementing the fault diagnosis method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more fault diagnosis device embodiments provided below can refer to the limitations on the fault diagnosis method above, and will not be repeated here.

[0089] In an exemplary embodiment, as shown in FIG4 , a fault diagnosis device is provided, including: a data acquisition module 402 , a mean value calculation module 404 , an interval calculation module 406 and a fault diagnosis module 408 , wherein:

[0090] The data acquisition module 402 is used to acquire carrier temperature data and node temperature data within a preset time interval. The carrier temperature data is data obtained by estimating the temperature of the carrier in the post-processing system 2 , and the node temperature data is data obtained by detecting the temperature of each node in the post-processing system 2 .

[0091] The mean value calculation module 404 is used to obtain the carrier average temperature and the node average temperature according to the carrier temperature data and the node temperature data.

[0092] The interval calculation module 406 is used to obtain a theoretical temperature interval according to the average temperature of the carrier and the average temperature of the node.

[0093] The fault diagnosis module 408 is used to compare and process the node average temperature corresponding to the target node temperature sensor based on the theoretical temperature range to obtain a diagnosis result.

[0094] In one of the embodiments, the device also includes: a diagnostic starting module, which is used to obtain status data corresponding to the after-treatment system 2; determine the catalytic reduction efficiency corresponding to the after-treatment system 2 based on the status data; and when the catalytic reduction efficiency meets the preset enabling conditions, execute the process of obtaining temperature data until a diagnostic result is obtained.

[0095] In one embodiment, the mean calculation module 404 is further used to perform mean processing on each carrier temperature data within the time interval to obtain the carrier average temperature; and perform mean processing on each node temperature data within the time interval to obtain the node average temperature.

[0096] In one embodiment, the node temperature data includes: a first node temperature and a second node temperature, the first node temperature is data obtained by temperature detection of a node upstream of the diesel particulate filter 22, and the second node temperature is data obtained by temperature detection of a node upstream of the catalytic reducer; the node average temperature includes: a first node average temperature and a second node average temperature; the interval calculation module 406 is also used to perform linear operation processing on the carrier average temperature, the second node average temperature and a preset upper limit correction coefficient to obtain interval upper limit data; perform linear operation processing on the carrier average temperature, the second node average temperature and a preset lower limit correction coefficient to obtain interval lower limit data; obtain a theoretical temperature interval based on the interval upper limit data and the interval lower limit data.

[0097] In one embodiment, the fault diagnosis module 408 is further configured to use an abnormal state as a diagnosis result when the average temperature of the first node exceeds the theoretical temperature range; and use a normal state as a diagnosis result when the average temperature of the first node meets the theoretical temperature range.

[0098] In one embodiment, the fault diagnosis module 408 is further configured to generate a fault alarm signal when the diagnosis result is that the state is abnormal.

[0099] Each module in the above fault diagnosis device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0100] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG5. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store carrier temperature data and node temperature data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a fault diagnosis method is implemented.

[0101] Those skilled in the art will understand that the structure shown in FIG. 5 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0102] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0103] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0104] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0105] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0106] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0107] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A fault diagnosis method, characterized in that: A target node temperature sensor applied to a post-processing system, the method comprising: Acquire carrier temperature data and node temperature data within a preset time interval, wherein the carrier temperature data is data obtained by estimating the temperature of the carrier in the post-processing system, and the node temperature data is data obtained by detecting the temperature of each node in the post-processing system; Obtaining a carrier average temperature and a node average temperature according to the carrier temperature data and the node temperature data; Obtaining a theoretical temperature range according to the average temperature of the carrier and the average temperature of the node; Based on the theoretical temperature range, the node average temperature corresponding to the target node temperature sensor is compared and processed to obtain a diagnosis result.

2. The method according to claim 1, characterized in that The method further comprises: Acquiring status data corresponding to the post-processing system; Determining a catalytic reduction efficiency corresponding to the post-treatment system according to the state data; When the catalytic reduction efficiency meets the preset enabling condition, the process of acquiring carrier temperature data and node temperature data is performed until the diagnosis result is obtained.

3. The method according to claim 1, characterized in that The step of obtaining the carrier average temperature and the node average temperature according to the carrier temperature data and the node temperature data includes: Performing mean processing on the temperature data of each carrier within the time interval to obtain the average temperature of the carrier; The temperature data of each node within the time interval are averaged to obtain the average temperature of the node.

4. The method according to claim 3, characterized in that The node temperature data includes: a first node temperature and a second node temperature, the first node temperature is data obtained by detecting the temperature of a node upstream of a diesel particulate filter, and the second node temperature is data obtained by detecting the temperature of a node upstream of a catalytic reducer; the node average temperature includes: a first node average temperature and a second node average temperature; The obtaining of a theoretical temperature range according to the average temperature of the carrier and the average temperature of the node includes: Performing linear operation processing on the average temperature of the carrier, the average temperature of the second node, and a preset upper limit correction coefficient to obtain interval upper limit data; Performing linear operation processing on the average temperature of the carrier, the average temperature of the second node, and a preset lower limit correction coefficient to obtain interval lower limit data; The theoretical temperature interval is obtained according to the interval upper limit data and the interval lower limit data.

5. The method according to claim 4, characterized in that The step of comparing and processing the node average temperature corresponding to the target node temperature sensor based on the theoretical temperature range to obtain a diagnosis result includes: When the average temperature of the first node exceeds the theoretical temperature range, taking the state abnormality as a diagnosis result; When the average temperature of the first node meets the theoretical temperature range, the state is taken as a normal diagnosis result.

6. The method according to claim 5, characterized in that The method further comprises: When the diagnosis result indicates that the state is abnormal, a fault alarm signal is generated.

7. A fault diagnosis device, characterized in that: A target node temperature sensor applied to a post-processing system, the device comprising: A data acquisition module, used to acquire carrier temperature data and node temperature data within a preset time interval, wherein the carrier temperature data is data obtained by estimating the temperature of the carrier in the post-processing system, and the node temperature data is data obtained by detecting the temperature of each node in the post-processing system; A mean value calculation module, used for obtaining a carrier average temperature and a node average temperature according to the carrier temperature data and the node temperature data; An interval calculation module, used for obtaining a theoretical temperature interval according to the average temperature of the carrier and the average temperature of the node; The fault diagnosis module is used to compare and process the node average temperature corresponding to the target node temperature sensor based on the theoretical temperature range to obtain a diagnosis result.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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