Methods, devices and electronic equipment for diagnosing abnormal coolant in construction machinery

Abstract

This application provides a method, device, and electronic device for diagnosing coolant anomalies in construction machinery. The method includes: acquiring real-time operating data of the construction machinery; inputting the real-time operating data into a coolant anomaly identification model, and determining the coolant status based on the identification results of the coolant anomaly identification model; wherein the coolant anomaly identification model is trained based on historical operating data of the construction machinery; the operating data includes any one or more of engine speed, lever pilot valve data, instantaneous fuel efficiency, and torque, as well as coolant temperature; the coolant status includes at least abnormal coolant temperature rise and normal coolant temperature; when the coolant status is determined to be abnormal coolant temperature rise, a coolant temperature rise anomaly warning message is generated. This application can identify abnormal coolant temperature rise before the coolant level in the construction machinery exceeds a threshold and generate a coolant temperature rise anomaly warning message to instruct relevant personnel to intervene in a timely manner to avoid damage caused by the operation of the construction machinery.

Description

Technical Field

[0001] This application relates to the field of engineering machinery control technology, and in particular to a method, device and electronic equipment for diagnosing abnormal coolant in engineering machinery. Background Technology

[0002] Construction machinery is one of the important pillar industries of national economic development. As an important part of the equipment manufacturing industry, it is a general term for construction machinery used in national basic construction projects, mainly applied in urban and rural roads, urban infrastructure construction, national defense, water conservancy, power, transportation, and energy industries. With the development of construction machinery equipment technology, Internet of Things technology, and artificial intelligence technology, real-time monitoring of the status of construction machinery can be provided by sending real-time operating data of construction machinery to a remote server. Existing methods for diagnosing coolant abnormalities in construction machinery mainly rely on the coolant temperature and coolant threshold value for judgment. In the process of implementing the embodiments of this application, it was found that the technology has at least the following problems: the existing coolant abnormality judgment is lagging behind. Before the coolant temperature becomes abnormal, the construction machinery has already been operating abnormally for a period of time. The lag in coolant abnormality judgment can easily aggravate the wear and tear of construction machinery. Summary of the Invention

[0003] This application provides a method, device, and electronic device for diagnosing coolant abnormalities in construction machinery, in order to solve the problem that delayed judgment of coolant abnormalities can easily aggravate the wear and tear on construction machinery.

[0004] In a first aspect, embodiments of this application provide a method for diagnosing abnormal coolant in engineering machinery, including:

[0005] Obtain real-time operating data of construction machinery;

[0006] Real-time operating data is input into the coolant anomaly identification model, and the coolant status is determined based on the identification results of the coolant anomaly identification model; wherein, the coolant anomaly identification model is trained based on the historical operating data of the construction machinery; the operating data includes: engine speed, lever pilot valve data, instantaneous fuel efficiency and torque, or any one or more of these, and coolant temperature; the coolant status includes at least abnormal coolant temperature rise and normal coolant temperature;

[0007] When the coolant condition is determined to be abnormal, an abnormal coolant temperature rise warning message is generated.

[0008] In one possible implementation, the coolant condition also includes an abnormally high temperature of the coolant;

[0009] Accordingly, the method further includes:

[0010] When the coolant condition is determined to be abnormally high, the thermostat status is obtained;

[0011] When the thermostat is open, a high coolant temperature abnormality warning message is generated; when the thermostat is closed, a thermostat abnormality warning message is generated.

[0012] In one possible implementation, before inputting the real-time operating data into the coolant anomaly identification model, the method further includes:

[0013] Align the various parameters in the real-time operation data according to time sequence;

[0014] The missing values ​​of the aligned parameter data are interpolated.

[0015] Normalize the interpolated parameters.

[0016] In one possible implementation, the handle pilot valve data is the handle pilot valve pressure or handle operation indication.

[0017] In one possible implementation, the method further includes:

[0018] When the coolant condition is determined to be abnormal, an anomaly record is generated.

[0019] The abnormal records include: the construction machinery ID, the abnormal date, and the operating data within a set time period.

[0020] In one possible implementation, the method further includes updating the coolant anomaly identification model based on the anomaly record.

[0021] In one possible implementation, updating the coolant anomaly identification model based on the anomaly record includes:

[0022] The abnormal records are divided into a training set and a validation set;

[0023] The training set is input into the coolant anomaly identification model for training;

[0024] Input the validation set into the trained coolant anomaly identification model;

[0025] The validation set is input into the validated coolant anomaly identification model, and the coolant anomaly identification model is used as the updated model when the accuracy is greater than the set value.

[0026] Secondly, embodiments of this application provide a diagnostic device for abnormal coolant in engineering machinery, comprising:

[0027] The acquisition module is used to acquire real-time operating data of the construction machinery;

[0028] An anomaly detection module is used to input real-time operating data into a coolant anomaly detection model and determine the coolant status based on the detection results of the coolant anomaly detection model. The coolant anomaly detection model is trained based on historical operating data of the construction machinery. The operating data includes engine speed, lever pilot valve data, instantaneous fuel efficiency, torque, and coolant temperature. The coolant status includes at least abnormal coolant temperature rise and normal coolant temperature.

[0029] The control module is used to generate an abnormal prompt message when it determines that the coolant temperature is abnormal.

[0030] In one possible implementation, the coolant condition also includes an abnormally high temperature of the coolant;

[0031] Accordingly, the control module is also used to obtain the thermostat status when it is determined that the coolant status is abnormally high temperature; generate a coolant high temperature abnormality prompt message when the thermostat is open; and generate a thermostat abnormality prompt message when the thermostat is closed.

[0032] In one possible implementation, it further includes: a preprocessing module, used to align the parameter data in the real-time operating data according to time sequence before inputting the real-time operating data into the coolant anomaly identification model;

[0033] The missing values ​​of the aligned parameter data are interpolated.

[0034] Normalize the interpolated parameters.

[0035] In one possible implementation, the handle pilot valve data is the handle pilot valve pressure or handle operation indication.

[0036] In one possible implementation, the control module is further configured to generate an anomaly record when it is determined that the coolant status is abnormally high.

[0037] The abnormal records include: the construction machinery ID, the abnormal date, and the operating data within a set time period.

[0038] In one possible implementation, the device further includes an update module for updating the coolant anomaly identification model based on the anomaly records.

[0039] In one possible implementation, the update module is specifically used for:

[0040] The abnormal records are divided into a training set and a validation set;

[0041] The training set is input into the coolant anomaly identification model for training;

[0042] Input the validation set into the trained coolant anomaly identification model;

[0043] The validation set is input into the validated coolant anomaly identification model, and the coolant anomaly identification model is used as the updated model when the accuracy is greater than the set value.

[0044] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect or any possible implementation of the first aspect above.

[0045] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation of the first aspect.

[0046] This application provides a method, device, and electronic device for diagnosing coolant anomalies in construction machinery. Before diagnosing coolant anomalies, the method trains a model based on coolant temperature, engine speed, pilot valve data, instantaneous fuel efficiency, and torque (or one or more of these data). The real-time operating data of the construction machinery is then input into the coolant anomaly identification model. This allows the model to identify coolant temperature anomalies before they exceed a threshold and generate a coolant temperature anomaly warning message. This instructs relevant personnel to continue maintenance or adjust the machinery's operating plan, preventing coolant temperature from exceeding the threshold and causing downtime. This reduces wear and tear on components caused by abnormal operation and extends the overall service life of the construction machinery. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating the implementation of an embodiment of the engineering machinery coolant abnormality diagnosis method provided in this application;

[0049] Figure 2a and Figure 2b This is a schematic diagram of normal and abnormal coolant temperature rise data provided in an embodiment of this application;

[0050] Figure 3 This is a schematic diagram of the structure of an engineering machinery coolant abnormality diagnosis device provided in one embodiment of this application;

[0051] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0052] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0053] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0054] Unless otherwise stated, the term "multiple" means two or more. The character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B. The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0055] The terms used in this application are for describing embodiments only and are not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term “and / or” as used herein means including one or more of the associated listed elements and all possible combinations thereof. Additionally, when used in this application, the terms “comprise” and its variations “comprises” and / or “comprising” refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitation, an element defined by the phrase “comprising an…” does not exclude the presence of additional identical elements in the process, method, or apparatus that includes said element.

[0056] In this application, each embodiment focuses on describing the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, then the relevant parts can be referred to the description of the method section.

[0057] In this application, the types of construction machinery include, but are not limited to, excavators, heavy transport vehicles, large cranes, bulldozers, road rollers, and loaders. With the advancement of construction machinery equipment technology and Internet of Things (IoT) technology, real-time operating data of construction machinery can be sent to a cloud server, where the server collects real-time operating data to monitor the temperature and temperature changes of the construction machinery's coolant.

[0058] In practice, the controller of the construction machinery is connected to each component via a CAN bus, and the operating data of each component is collected in real time and sent to the server.

[0059] During the operation of construction machinery, some components may already be in an abnormal operating state for a period of time before the coolant temperature reaches an abnormally high level. During this abnormal operation, the components of the construction machinery suffer wear and tear. Traditional solutions that issue warnings when the coolant temperature exceeds a threshold will affect the lifespan of the construction machinery, and the need to shut down the machine for inspection when the coolant temperature is high will affect the progress of the project.

[0060] Therefore, the present application aims to provide a solution that can promptly detect abnormally high temperatures in the coolant during the operation of construction machinery and provide an abnormality warning, so as to instruct relevant personnel to promptly inspect or adjust the control scheme of the construction machinery to reduce component wear and avoid affecting the progress of the project during operation.

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0062] Figure 1 This is a flowchart illustrating the implementation of a method for diagnosing coolant abnormalities in engineering machinery according to an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0063] S101 acquires real-time operating data of construction machinery.

[0064] The coolant anomaly diagnosis method for construction machinery provided in this application is executed by a remote server. The server is connected to the construction machinery, and the construction machinery uploads real-time operating data according to a set schedule. The server performs the coolant anomaly diagnosis process, thereby reducing the local data processing load on the construction machinery.

[0065] Optionally, the time period for uploading real-time operating data from the construction machinery can be set to 3 to 5 seconds. The set time can be 3, 4, or 5 seconds, but should not be too long to ensure the data accurately reflects the state of the construction machinery's coolant.

[0066] The server can manage one or more types of construction machinery. The server is responsible for collecting and storing historical operating data of the construction machinery, and for training and updating the coolant anomaly identification model. The training of the model is completed without affecting the operation of the construction machinery.

[0067] S102, input the real-time operating data into the coolant anomaly identification model, and determine the coolant status based on the identification results of the coolant anomaly identification model; wherein, the coolant anomaly identification model is trained based on the historical operating data of the construction machinery; the operating data includes: engine speed, lever pilot valve data, instantaneous fuel efficiency and torque, or any one or more of them, and coolant temperature; the coolant status includes at least abnormal coolant temperature rise and normal coolant temperature.

[0068] In this embodiment, based on the different compositions and coolant circulation loops of various construction machinery, there are corresponding coolant anomaly identification models for different types of construction machinery. Therefore, in one possible implementation, before inputting real-time operating data into the coolant anomaly identification model, the method further includes: selecting the corresponding coolant anomaly identification model according to the type of construction machinery.

[0069] In ordinary vehicles and construction machinery, coolant is typically used to protect the engine and ensure its proper operation. It circulates within the engine's radiator, providing protection against freezing, boiling, rust, and corrosion. Therefore, coolant anomaly detection models are usually trained based on historical operating data of the construction machinery, specifically engine speeds, or engine speeds and torques.

[0070] In other possible implementations, construction machinery differs from ordinary vehicles in that it has components related to construction operations. These components generate significant heat or experience wear during construction. Therefore, in addition to protecting the engine for proper operation, the coolant is also essential for the normal operation of other components within the construction machinery. Similarly, the coolant temperature is also affected by the operating data of other components, such as the data from the control lever pilot valve and instantaneous fuel efficiency.

[0071] This application focuses on experimentally verifying the efficiency of the coolant anomaly identification model using an excavator as an example. The operational data includes engine speed, lever pilot valve data, instantaneous fuel efficiency, torque, and coolant temperature. By constructing a coolant anomaly identification model based on multiple operational data points, the accuracy of overall coolant anomaly assessment can be improved.

[0072] In practice, the rate of coolant warming varies depending on the state of the coolant during the warming process. Broadly speaking, the states of coolant warming during the warming process can be categorized as: cold start idling without operation, cold start acceleration without operation, and cold start acceleration with operation.

[0073] Under normal operation, construction machinery is started from a cold, idling state without any user intervention (e.g., by controlling the oil temperature in the pilot system to steadily rise and preheat the pilot hydraulic system). A cold, idling state is defined as follows: engine speed below the set speed, pilot valve not activated or pressure below the set pressure, instantaneous fuel efficiency below the set efficiency, and torque below the set torque. Specifically, engine speed below the set speed and instantaneous fuel efficiency below the set efficiency indicate the engine is idling, with a relatively small fuel injection volume to ensure a steady rise in oil temperature; pilot valve not activated or pressure below the set pressure indicates the operator is not controlling the excavator's bucket movement; and torque below the set torque indicates the bucket is not digging or loading (i.e., the load is zero). The greater the load when the bucket is digging or carrying, the greater the torque.

[0074] In actual operation, construction machinery operators often rev the engine at idle to increase the engine speed and shorten the start-up time, which also improves instantaneous fuel efficiency. This corresponds to the construction machinery's coolant warming up in a cold, idle, and inactive state.

[0075] In addition, there are instances of controlling the bucket movement during the startup of construction machinery, which further affects the rate at which the coolant warms up. In this case, the coolant temperature of the construction machinery corresponds to a cold start idling with operation.

[0076] Therefore, under normal operation, the coolant temperature rise rate is relatively stable. However, when the radiator needs cleaning or the water pump needs replacement, abnormal coolant temperature rise may occur. In such cases, a coolant temperature abnormality warning should be issued promptly to alert relevant personnel to inspect and clean the water pump or radiator. However, when construction operators increase the engine speed at idle or control the bucket movement to shorten the start-up time of the construction machinery, the coolant temperature rise rate fluctuates significantly and rises rapidly. This is not due to the radiator needing cleaning or the water pump needing replacement, but rather due to the operator's personal actions.

[0077] To facilitate the identification and timely detection of coolant temperature anomalies, the coolant temperature must be input into the coolant anomaly identification model when constructing the model and making anomaly judgments based on the model. In addition, one or more of the following data must also be input into the coolant anomaly identification model: engine speed, lever pilot valve data, instantaneous fuel efficiency, and torque. This allows for the detection of abnormal temperature rises during the coolant warming process, identifying operational abnormalities when the temperature rises rapidly.

[0078] In different embodiments, the data of the handle pilot valve is represented in different ways.

[0079] In one possible implementation, the data for the handle pilot valve is the handle pilot valve pressure. Specifically, when the operator is not operating the handle, the handle pilot valve pressure is zero or a small, fixed value; when the operator operates the handle, the handle pilot valve pressure increases.

[0080] In one possible implementation, the data of the handle pilot valve is the handle operation indication. Specifically, the handle operation indication is 1 when the operator operates the handle, and 0 when the operator does not operate the handle.

[0081] Furthermore, given that the temperature rise of coolant in construction machinery is a continuous process, the coolant anomaly identification model needs to be trained based on time-series data. Specifically, a Long Short-Term Memory (LSTM) recurrent neural network is used for model training. Alternatively, models such as Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA) can be used for training.

[0082] S103, when the coolant condition is determined to be abnormal, a coolant temperature rise abnormality prompt message is generated.

[0083] The server can send abnormal coolant temperature information to the construction machinery or the operator's mobile terminal, so that the operator can be aware of the abnormal coolant temperature during the operation of the construction machinery and adjust the control scheme or shut down for maintenance based on the operator's experience. This can prevent the construction machinery from being shut down due to the coolant temperature exceeding the threshold or reduce the abnormal operation time of the construction machinery, reduce the wear and tear of components, and thus extend the overall life of the construction machinery.

[0084] Optionally, the coolant temperature rise anomaly alert message includes the coolant temperature rise anomaly identification result and operating data for a corresponding set time period, facilitating the operator to determine further operating procedures based on the operating data. The set time period is the time corresponding to the coolant temperature rise anomaly; optionally, the set time period is 1 minute to 3 minutes. Alternatively, the set time period can be 1 minute, 2 minutes, or 3 minutes.

[0085] In this embodiment, before diagnosing coolant malfunctions in construction machinery, a model is trained based on coolant temperature, engine speed, pilot valve data, instantaneous fuel efficiency, and torque, among other data. The real-time operating data of the construction machinery is then input into the coolant malfunction identification model. This enables the model to identify coolant temperature malfunctions before the coolant temperature exceeds a threshold and generate a coolant temperature malfunction warning message. This message instructs relevant personnel to continue the maintenance of the construction machinery or adjust the operation plan, preventing the coolant temperature from exceeding the threshold and causing the construction machinery to shut down. This reduces wear and tear on various components caused by abnormal operation of the construction machinery and extends the overall service life of the construction machinery.

[0086] The solutions provided in the above embodiments mainly introduce methods for detecting abnormal coolant temperature rise, mentioning that abnormal temperature rise can be caused by water pump damage or the need for cleaning the water tank. In practice, there is also the possibility of an abnormal situation where a damaged thermostat prevents coolant circulation.

[0087] In one possible implementation, the coolant condition also includes an abnormally high coolant temperature;

[0088] Accordingly, the method also includes:

[0089] When the coolant condition is determined to be abnormally high, the thermostat status is obtained;

[0090] When the thermostat is open, a high coolant temperature warning message is generated; when the thermostat is closed, a thermostat abnormality warning message is generated.

[0091] In one possible implementation, when identifying high-temperature anomalies in the coolant based on the coolant anomaly identification model, the high-temperature anomaly is determined by comparing a set temperature threshold with the coolant temperature in real-time operating data. The set temperature threshold is greater than the thermostat activation threshold but less than the coolant's rated threshold.

[0092] The thermostat is a valve that controls the flow path of coolant. It is an automatic temperature control device, usually containing a temperature-sensing component. It opens or closes the flow of air, gas, or liquid based on thermal expansion or contraction. When the thermostat is functioning normally, the coolant temperature rises to a certain threshold, at which point the thermostat opens and controls coolant flow to improve cooling efficiency. However, when the thermostat malfunctions, the coolant temperature rises to a certain point but the thermostat does not activate, preventing coolant flow. As the machinery continues to operate, the coolant temperature will continue to rise and, exceeding the set temperature threshold, will be identified as an abnormal high-temperature state. In this situation, if the water pump or radiator is functioning properly, the coolant temperature rise process will proceed normally until it reaches the temperature set value of the thermostat's temperature-sensing component. If the thermostat fails to activate when the coolant reaches the set temperature of the temperature-sensing component, abnormal coolant temperature rise or even an abnormal high-temperature state will occur.

[0093] In this embodiment, the normal operation of the coolant during the temperature rise phase before the coolant temperature abnormality is taken into account, and the situation where the thermostat malfunction leads to the coolant temperature abnormality. The abnormality warning scheme for construction machinery is improved, the accuracy of the coolant temperature abnormality warning is increased, and false high coolant temperature warnings due to thermostat malfunction are avoided.

[0094] Similarly, in another possible implementation, the method also includes:

[0095] When the coolant condition is determined to be abnormally high temperature, the water pump status is obtained;

[0096] When the water pump is running normally, a high temperature warning message for the coolant is generated; when the water pump fails to start, a water pump malfunction warning message is generated.

[0097] In this embodiment, the normal operation of the coolant during the temperature rise stage before the coolant temperature abnormality is taken into account, and the situation where the water pump abnormality leads to the coolant temperature abnormality. The abnormality warning scheme of the construction machinery is improved, the accuracy of the coolant temperature abnormality warning is improved, and false high coolant temperature warnings due to water pump abnormalities are avoided.

[0098] In one possible implementation, before inputting real-time operational data into the coolant anomaly identification model, the following is also included:

[0099] Align the various parameters in the real-time operation data according to time sequence;

[0100] The missing values ​​of the aligned parameter data are interpolated.

[0101] Normalize the interpolated parameters.

[0102] Since the coolant undergoes a continuous temperature rise before reaching an abnormal high temperature, real-time operating data needs to be input into the coolant anomaly identification model in a time sequence to monitor the coolant temperature rise process. Furthermore, various devices or factors can affect coolant temperature rise, and each component uploads data to the controller via the CAN bus. Therefore, the parameters in the real-time operating data are independent of each other and need to be aligned according to the time sequence.

[0103] Secondly, given the collection of multiple parameter data, the raw data inevitably contains incomplete, inconsistent, anomaly-laden, and off-target data. These problematic data can affect the efficiency of data mining and even the identification results of the coolant anomaly detection model. Therefore, further interpolation of missing values ​​during data preprocessing can improve the efficiency and accuracy of coolant anomaly detection.

[0104] In specific embodiments, the interpolation process operates differently under different sampling periods. Optionally, the time axes of data with different operating parameters are aligned to the same second. If the data sampling interval is 1 second, the missing data generated after alignment is interpolated; if the data sampling interval is greater than 1 second, the missing data generated after alignment is deleted.

[0105] Finally, since the parameters have different dimensions and units, and their impact on coolant rise varies, normalization is performed before inputting the difference-processed data into the coolant anomaly identification model. This ensures that the parameters are on the same order of magnitude, making them suitable for comprehensive comparison and evaluation, and improving the efficiency and accuracy of coolant anomaly identification.

[0106] In this embodiment, before inputting real-time operating data into the coolant anomaly identification model, a data preprocessing process including alignment, interpolation, and normalization is performed to improve the efficiency and accuracy of coolant anomaly identification.

[0107] like Figure 2a and Figure 2b As shown, Figure 2b An example is shown where, under operator-controlled, non-cold-start idling conditions, a single operating parameter causes a change in the rate of coolant temperature rise. The triangle represents the coolant temperature curve, and the diamond represents any one of the following: engine speed, torque, lever pilot valve data, or instantaneous fuel efficiency. Figure 2bDuring the cooling process, the temperature rises rapidly. Once the temperature stabilizes, the coolant temperature becomes relatively stable. Based on the coolant anomaly identification model, the parameter corresponding to the diamond-shaped curve can be identified as the cause. If the parameter's impact on the temperature rise rate is determined to be within the normal range, no coolant temperature rise anomaly warning message is generated. In other embodiments, there are also cases where changes in two or more operating parameters lead to abnormal coolant temperature rise, which can all be identified by the coolant anomaly identification model.

[0108] If the coolant still overheats abnormally even when everything else is normal, it may be due to blockages in the coolant circulation components. Specifically, the abnormal overheating can be cleared by cleaning the water tank and coolant circulation pipes.

[0109] In this embodiment, when the coolant temperature rise is determined based on the coolant anomaly identification model, it is possible to further determine the anomaly handling plan based on the various parameter data in the reference data, so as to improve the maintenance efficiency of engineering machinery, extend the service life of components, and avoid delays in the construction period caused by the need to inspect multiple components.

[0110] In one possible implementation, when the coolant condition is determined to be an abnormal coolant temperature rise, an anomaly record is generated.

[0111] The abnormal records include: the construction machinery ID, the abnormal date, and the operating data within a set time period.

[0112] During the use of construction machinery, component wear is inevitable. Compared with the heat generated in the same time period when it was new, the rate at which the coolant heats up may change. Therefore, during the use of construction machinery, it is necessary to update the coolant anomaly identification model based on recorded abnormal data in order to improve the efficiency of the coolant anomaly identification model in identifying abnormal coolant temperature rise.

[0113] The anomaly record includes the construction machinery ID, facilitating the server's categorized management of data for multiple construction machines. When a coolant temperature rise anomaly is determined, data over a continuous period is required. Therefore, upon detecting a coolant temperature rise anomaly, operational data within a defined time frame is acquired to provide a reference for subsequent model updates and training. Given that in different embodiments, the coolant status may include not only a coolant temperature rise anomaly but also a coolant high temperature anomaly, the vehicle coolant temperature anomaly identification result is recorded simultaneously when generating the anomaly record.

[0114] In practice, the reliability of anomaly dates is reduced when they are far from the update date. Therefore, recording anomaly dates in the anomaly log helps determine the reference data range during updates. Furthermore, it facilitates the review of anomalies by relevant personnel, providing detailed information about the specific occurrence of the anomaly. For example, construction machinery manufacturers can provide after-sales service quickly based on the anomaly log; improvements can be made to the production line or the selection of components during production based on newly added anomaly records; performance managers can adjust or review the performance records of relevant operators based on the anomaly log; and operators or on-site maintenance personnel can quickly locate the parts requiring repair, improving the safety of equipment use.

[0115] In this embodiment, an anomaly record is generated when the coolant condition is determined to be abnormal, facilitating relevant personnel to review the construction machinery's usage history for better maintenance services or improved operational safety. Furthermore, the coolant anomaly identification model can be updated based on the anomaly record, improving the efficiency of identifying coolant temperature anomalies.

[0116] In other possible implementations, when the coolant condition is determined to be an abnormal high temperature, a high temperature anomaly record is also generated to update the coolant anomaly identification model based on the high temperature anomaly record, thereby improving the accuracy of identifying high temperature anomalies in the coolant.

[0117] Correspondingly, in another possible implementation, when the coolant condition is determined to be either abnormal coolant temperature rise or abnormal coolant temperature, an anomaly record is generated.

[0118] The abnormal records include: construction machinery ID, abnormal date, operating data within a set time period, and vehicle coolant temperature abnormality identification results; the vehicle coolant temperature abnormality identification results include coolant temperature rise abnormality and coolant high temperature abnormality.

[0119] In one possible implementation, the method further includes updating the coolant anomaly identification model based on anomaly records.

[0120] The anomaly records include: the construction machinery ID, the date of the anomaly, and the operating data within a specified time period. Specifically, when updating the coolant anomaly identification model based on the anomaly records, it is necessary to select operating data of the same or similar construction machinery based on the construction machinery ID to update the coolant anomaly identification model; select recent data based on the date of the anomaly to update the coolant anomaly identification model; and simultaneously, determine the data corresponding to the same anomaly identification result based on the vehicle coolant temperature anomaly identification result to update the coolant anomaly identification model.

[0121] In one possible implementation, the coolant anomaly identification model is updated based on anomaly records, including:

[0122] The abnormal records are divided into a training set and a validation set;

[0123] The training set is input into the coolant anomaly detection model for training;

[0124] Input the validation set into the trained coolant anomaly detection model;

[0125] Input the validation set into the validated coolant anomaly identification model, and use the coolant anomaly identification model as the updated model when the accuracy is greater than the set value.

[0126] During model training, the accuracy of the coolant anomaly identification model should reach over 95% to achieve accurate identification of coolant anomalies.

[0127] In the specific implementation process, the abnormal record data serves as a reference for identifying coolant abnormalities. In addition, to reflect coolant abnormalities, it is also necessary to combine normal operation data. To achieve diversified deployment, when updating or initially training the coolant abnormality identification model, historical data is obtained based on different sampling periods. Optionally, the sampling periods include 1s, 2s, and 3s, and three versions of the coolant abnormality identification model are generated accordingly.

[0128] The following are device embodiments of this application. For details not described in detail, please refer to the corresponding method embodiments described above.

[0129] Figure 3 This is a schematic diagram of the structure of an engineering machinery coolant abnormality diagnosis device provided in an embodiment of this application, as shown below. Figure 3 As shown, for ease of explanation, only the parts related to the embodiments of this application are shown, such as... Figure 3 As shown, the device includes:

[0130] The acquisition module 301 is used to acquire real-time operating data of the construction machinery;

[0131] The anomaly identification module 302 is used to input real-time operating data into the coolant anomaly identification model and determine the coolant status based on the identification results of the coolant anomaly identification model. The coolant anomaly identification model is trained based on the historical operating data of the construction machinery. The operating data includes engine speed, lever pilot valve data, instantaneous fuel efficiency, torque, and coolant temperature. The coolant status includes at least abnormal coolant temperature rise and normal coolant temperature.

[0132] The control module 303 is used to generate an abnormal prompt message when it is determined that the coolant status is abnormal and the coolant temperature rise is abnormal.

[0133] In one possible implementation, the coolant condition also includes an abnormally high coolant temperature;

[0134] Correspondingly, the control module 303 is also used to obtain the thermostat status when the coolant status is determined to be abnormally high temperature; generate a high temperature abnormality warning message when the thermostat is open; and generate a thermostat abnormality warning message when the thermostat is closed.

[0135] In one possible implementation, it also includes: a preprocessing module, used to align the various parameter data in the real-time operating data according to time sequence before inputting the real-time operating data into the coolant anomaly identification model;

[0136] The missing values ​​of the aligned parameter data are interpolated.

[0137] Normalize the interpolated parameters.

[0138] In one possible implementation, the handle pilot valve data is the handle pilot valve pressure or the handle operation indication.

[0139] In one possible implementation, the control module 303 is also used to generate an abnormal record when it is determined that the coolant status is abnormal and the coolant temperature rise is abnormal.

[0140] The abnormal records include: the construction machinery ID, the abnormal date, and the operating data within a set time period.

[0141] In one possible implementation, the device also includes an update module for updating the coolant anomaly identification model based on anomaly records.

[0142] In one possible implementation, the update module is specifically used for:

[0143] The abnormal records are divided into a training set and a validation set;

[0144] The training set is input into the coolant anomaly detection model for training;

[0145] Input the validation set into the trained coolant anomaly detection model;

[0146] Input the validation set into the validated coolant anomaly identification model, and use the coolant anomaly identification model as the updated model when the accuracy is greater than the set value.

[0147] In this embodiment, before diagnosing coolant anomalies in construction machinery, a model is trained based on coolant temperature, engine speed, pilot valve data, instantaneous fuel efficiency, and torque, among other data. The acquired real-time operating data of the construction machinery is input into the coolant anomaly identification model. This enables the model to identify coolant temperature anomalies before the coolant temperature exceeds a threshold, generating a coolant temperature anomaly warning message. This instructs relevant personnel to continue with the abnormal maintenance or adjust the machinery's operating plan, preventing coolant temperature rise exceeding the threshold and causing machinery shutdown. This reduces wear and tear on components caused by abnormal machinery operation, ultimately extending the overall service life of the construction machinery. Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For example... Figure 4 As shown, the electronic device 4 in this embodiment includes: a processor 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processor 40. When the processor 40 executes the computer program 42, it implements the steps in the various embodiments of the engineering machinery coolant abnormality diagnosis method described above, for example... Figure 1 Steps S101 to S103 are shown. Alternatively, when the processor 40 executes the computer program 42, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 3 The functions of modules 301 to 303 are shown.

[0148] For example, the computer program 42 can be divided into one or more modules / units, which are stored in the memory 41 and executed by the processor 40 to complete this application. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 42 in the electronic device 4. For example, the computer program 42 can be divided into... Figure 3 Modules 301 to 303 are shown.

[0149] The electronic device 4 can be a desktop computer, laptop, handheld computer, or cloud server, etc. The electronic device 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 4 and does not constitute a limitation on electronic device 4. It may include more or fewer components than shown, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0150] The processor 40 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0151] The memory 41 can be an internal storage unit of the electronic device 4, such as a hard disk or memory. The memory 41 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 41 can include both internal and external storage units of the electronic device 4. The memory 41 is used to store the computer program and other programs and data required by the electronic device. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0152] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0153] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0154] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0155] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0156] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0157] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0158] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various embodiments of the engineering machinery coolant abnormality diagnosis method. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0159] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for diagnosing abnormal coolant conditions in engineering machinery, characterized in that, include: Obtain real-time operating data of construction machinery; A corresponding coolant anomaly identification model is selected based on the type of construction machinery. Real-time operating data is input into the coolant anomaly identification model, and the coolant status is determined based on the identification results of the model. The coolant anomaly identification model is trained based on historical operating data of the construction machinery. Operating data includes: engine speed, lever pilot valve data, instantaneous fuel efficiency and torque, and coolant temperature. The coolant status includes at least abnormal coolant temperature rise and normal coolant temperature. The coolant temperature rise rate varies depending on the coolant temperature state of the construction machinery. The coolant temperature rise states include cold start idling without operation, cold start acceleration without operation, and cold start acceleration with operation. When the coolant condition is determined to be abnormal, an abnormal coolant temperature rise warning message is generated.

2. The method for diagnosing abnormal coolant in engineering machinery according to claim 1, characterized in that, The coolant condition also includes abnormal high temperature of the coolant; Accordingly, the method further includes: When the coolant condition is determined to be abnormally high, the thermostat status is obtained; When the thermostat is open, a high coolant temperature abnormality warning message is generated; when the thermostat is closed, a thermostat abnormality warning message is generated.

3. The method for diagnosing abnormal coolant conditions in engineering machinery according to claim 1, characterized in that, Before inputting real-time operating data into the coolant anomaly identification model, the method further includes: Align the various parameters in the real-time operation data according to time sequence; The missing values ​​of the aligned parameter data are interpolated. Normalize the interpolated parameters.

4. The method for diagnosing abnormal coolant in engineering machinery according to claim 1, characterized in that, in, The data for the handle pilot valve is either the handle pilot valve pressure or the handle operation indication.

5. The method for diagnosing abnormal coolant conditions in engineering machinery according to any one of claims 1 to 4, characterized in that, The method further includes: When the coolant condition is determined to be abnormal, an anomaly record is generated. The abnormal records include: the construction machinery ID, the abnormal date, and the operating data within a set time period.

6. The method for diagnosing abnormal coolant in engineering machinery according to claim 5, characterized in that, The method further includes updating the coolant anomaly identification model based on the anomaly records.

7. The method for diagnosing abnormal coolant in engineering machinery according to claim 6, characterized in that, The step of updating the coolant anomaly identification model based on the anomaly records includes: The abnormal records are divided into a training set and a validation set; The training set is input into the coolant anomaly identification model for training; Input the validation set into the trained coolant anomaly identification model; The validation set is input into the validated coolant anomaly identification model, and the coolant anomaly identification model is used as the updated model when the accuracy is greater than the set value.

8. A diagnostic device for abnormal coolant in engineering machinery, characterized in that, include: The acquisition module is used to acquire real-time operating data of the construction machinery; An anomaly identification module is used to select a corresponding coolant anomaly identification model based on the type of construction machinery, input real-time operating data into the coolant anomaly identification model, and determine the coolant status based on the identification results of the coolant anomaly identification model. The coolant anomaly identification model is trained based on historical operating data of the construction machinery. The operating data includes engine speed, lever pilot valve data, instantaneous fuel efficiency, torque, and coolant temperature. The coolant status includes at least abnormal coolant temperature rise and normal coolant temperature. The rate of coolant warming varies depending on the state of the coolant during the warming process. The states of the coolant during the warming process include cold engine idling without operation, cold engine acceleration without operation, and cold engine acceleration with operation. The control module is used to generate an abnormal prompt message when it determines that the coolant temperature is abnormal.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7 above.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7 above.

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