Self-adaptive control method and device for dynamic risk grading of engineering machinery

Abstract

The invention provides a self-adaptive control method and device for dynamic risk grading of engineering machinery. The method comprises the following steps: acquiring initial operation parameters of the engineering machinery, and normalizing the initial operation parameters to obtain target operation parameters; determining the dynamic weight of each target operation parameter based on an entropy weight method; determining the risk level of the engineering machinery according to the dynamic weight and the risk score of each target operation parameter; determining a dynamic safety boundary of the engineering machinery according to the preset reference PID parameters of different risk levels and the risk levels; determining a PID parameter correction value according to the target operation parameter and a preset operation parameter; and when the PID parameter correction value exceeds the dynamic safety boundary, triggering safety interruption. According to the method, real-time risk assessment and safety judgment are carried out on the engineering machinery, so that the working safety of the engineering machinery can be improved.

Description

Technical Field

[0001] This invention relates to the field of engineering machinery technology, and specifically to an adaptive control method and device for dynamic risk classification of engineering machinery. Background Technology

[0002] The operating environment of construction machinery (cranes, excavators, loaders) is complex. Factors such as load fluctuations, attitude deviations, and environmental disturbances can cause various safety risks, including overturning, collisions, mechanical injuries, falls, fires and explosions, and system malfunctions. Traditional control methods often use fixed thresholds for risk classification, failing to consider the different weights of various risk factors under different operating conditions (e.g., the load weight should be higher than the tilt angle under heavy loads, and vice versa under light loads). This results in low accuracy of risk assessment, leading to insufficient safety of construction machinery. Summary of the Invention

[0003] This application aims to provide an adaptive control method and device for dynamic risk classification of construction machinery, so as to improve the safety of construction machinery operation.

[0004] Firstly, an adaptive control method for dynamic risk classification of engineering machinery is provided, the method comprising: The initial operating parameters of the construction machinery are obtained, and the initial operating parameters are normalized to obtain the target operating parameters; The dynamic weights of each target operating parameter are determined based on the entropy weight method; The risk level of the construction machinery is determined based on the dynamic weights and risk scores of each of the target operating parameters. The dynamic safety boundary of the construction machinery is determined based on the preset baseline PID parameters for different risk levels and the risk level. Determine the PID parameter correction value based on the target operating parameters and the preset operating parameters; If the PID parameter correction value exceeds the dynamic safety boundary, a safety interruption is triggered.

[0005] Optionally, the dynamic weights of each of the target operating parameters are determined based on the entropy weight method, including: Calculate the information entropy of each of the target operating parameters based on the target operating parameters; The dynamic weights of each target operating parameter are determined based on the information entropy of each target operating parameter.

[0006] Optionally, the information entropy satisfies:

[0007] in, Let represent the information entropy of the i-th running parameter at time k, m represent the number of sampling windows, and N represent the total number of sampling windows. This represents the normalized probability value of the i-th running parameter at time k in the m-th adoption window;

[0008] in, This represents the normalized target running parameter value of the i-th running parameter in the m-th sampling window.

[0009] Optionally, determining the PID parameter correction value based on the target operating parameters and preset operating parameters includes: The control error, error rate of change, and error variance of each target operating parameter are determined based on the target operating parameters and the preset operating parameters. The control error, the rate of change of error, and the variance of error are fuzzified to obtain fuzzy membership degrees. The fuzzy membership degree is subjected to rule-based reasoning and defuzzification to obtain the PID parameter correction value.

[0010] Optionally, the risk level of the construction machinery is determined based on the dynamic weights and risk scores of each of the target operating parameters, including: The target operating parameters are linearly mapped according to a preset ratio to obtain a risk score for each target operating parameter. The sum of the products of the risk scores of each target operating parameter and the dynamic weights is determined as the risk score of the engineering machinery. The risk level of the construction machinery is determined based on its risk score.

[0011] Optionally, the risk score of the construction machinery meets the following requirements:

[0012] in, This represents the risk score of the construction machinery at time k. This represents the dynamic weight of the i-th running parameter at time k. This represents the risk score of the i-th running parameter at time k.

[0013] Secondly, an adaptive control device for dynamic risk classification of engineering machinery is provided, the device comprising: The acquisition module is used to acquire the initial operating parameters of the construction machinery and normalize the initial operating parameters to obtain the target operating parameters; The first determining module is used to determine the dynamic weights of each of the target operating parameters based on the entropy weight method; The second determining module is used to determine the risk level of the engineering machinery based on the dynamic weights and risk scores of each of the target operating parameters; The third determining module is used to determine the dynamic safety boundary of the construction machinery based on the preset benchmark PID parameters of different risk levels and the risk level. The fourth determining module is used to determine the PID parameter correction value based on the target operating parameters and the preset operating parameters; The control module is used to trigger a safety interrupt when the PID parameter correction value exceeds the dynamic safety boundary.

[0014] Thirdly, an electronic device is provided, comprising: The memory is configured to store instructions; and The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the adaptive control method for dynamic risk classification of engineering machinery provided in the first aspect of the embodiments of this application.

[0015] Fourthly, a machine-readable storage medium is provided, on which instructions are stored, the instructions being used to cause a machine to execute an adaptive control method based on the above-described dynamic risk classification of engineering machinery.

[0016] Fifthly, a computer program product is provided, wherein when the instructions in the computer program product are executed by the processor of an electronic device, the electronic device performs the adaptive control method for dynamic risk classification of engineering machinery as described above.

[0017] Based on the aforementioned adaptive control method for dynamic risk classification of construction machinery, the initial operating parameters of the construction machinery are obtained and normalized to obtain target operating parameters. The dynamic weights of each target operating parameter are determined using the entropy weight method. The risk level of the construction machinery is determined based on the dynamic weights and risk scores of each target operating parameter. The dynamic safety boundary of the construction machinery is determined based on preset benchmark PID parameters for different risk levels and the risk level itself. The PID parameter correction value is determined based on the target operating parameters and preset operating parameters. If the PID parameter correction value exceeds the dynamic safety boundary, a safety interruption is triggered. Thus, by conducting real-time risk assessment and safety judgment for construction machinery, the safety of construction machinery operation can be improved. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the adaptive control method for dynamic risk classification of engineering machinery provided in the embodiments of this application; Figure 2 This is a flowchart illustrating an adaptive control method for dynamic risk classification of engineering machinery provided in a specific embodiment of this application. Figure 3 This is a schematic diagram of the adaptive control device for dynamic risk classification of engineering machinery provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0021] The adaptive control method and device for dynamic risk classification of engineering machinery provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0022] Please see Figure 1 This is a flowchart illustrating the adaptive control method for dynamic risk classification of engineering machinery provided in this application embodiment. This method is applied to electronic devices. Figure 1 As shown, the adaptive control method for dynamic risk classification of construction machinery includes the following steps S100 to S600.

[0023] Step S100: Obtain the initial operating parameters of the construction machinery and normalize the initial operating parameters to obtain the target operating parameters.

[0024] In this embodiment, the initial operating parameters may include, but are not limited to, load, tilt angle, speed, and environmental disturbance factors. Environmental disturbance factors can be understood as the sum of uncertain disturbances generated by the external environment on the safe and stable operation of the system; they are environmental variables that affect the magnitude of risk but are not part of the equipment's own state. Specifically, initial operating parameters can be collected by setting up multiple sensors. These sensors may include, but are not limited to, tension sensors, tilt sensors, and speed sensors. The initial operating parameters are then normalized to eliminate the influence of dimensions, resulting in normalized target operating parameters.

[0025] Step S200: Determine the dynamic weights of each of the target operating parameters based on the entropy weight method.

[0026] In this embodiment, the entropy weight method can be understood as an objective weighting method based entirely on data and without subjective scoring. Its core principle is: the greater the variation in the indicator (the smaller the entropy), the higher the weight. Specifically, it first calculates the weight based on the normalized target operating parameter values. The probability values ​​of each operating parameter at each time point within each application window can be calculated. ,Right now:

[0027] in, This represents the normalized probability value of the i-th operating parameter at time k within the m-th adoption window. The information entropy of each target operating parameter is then calculated based on these probability values. Information entropy satisfies:

[0028] in, Let represent the information entropy of the i-th running parameter at time k, m represent the number of sampling windows, and N represent the total number of sampling windows. This represents the normalized probability value of the i-th running parameter at time k in the m-th adoption window.

[0029] After calculating the information entropy of each target operating parameter, the dynamic weight of each target operating parameter is determined based on its information entropy. The sum of the dynamic weights of all target operating parameters is 1. The dynamic weights can be expressed as:

[0030] This application breaks through the traditional fixed-weight risk assessment model and calculates the dynamic weights of each risk factor in real time based on the entropy weight method, which can solve the problem of low risk assessment accuracy under different working conditions.

[0031] Step S300: Determine the risk level of the engineering machinery based on the dynamic weights and risk scores of each of the target operating parameters.

[0032] In this embodiment, the target operating parameters are linearly mapped according to a preset ratio to obtain a risk score for each target operating parameter. The core of mapping the risk score is to map the normalized target operating parameter values ​​(range [0,1]) to a risk range of 0 to 10 points according to rules. The mapping rules may include, but are not limited to, linear mapping, piecewise linear mapping, etc.

[0033] After obtaining the risk score for each target operating parameter, the sum of the products of each target operating parameter's risk score and its dynamic weight is determined as the risk score of the construction machinery. The risk level of the construction machinery is then classified based on its risk score. The risk score of the construction machinery satisfies the following:

[0034] in, This represents the risk score of the construction machinery at time k. This represents the dynamic weight of the i-th running parameter at time k. This represents the risk score of the i-th running parameter at time k.

[0035] In one example, a risk score in the range [0,3) can be classified as low risk (L), a risk score in the range [3,5) as medium risk (M), a risk score in the range [5,7) as high risk (H), and a risk score greater than or equal to 7 as extremely high risk (VH). Step S400: Determine the dynamic safety boundary of the construction machinery based on the preset benchmark PID parameters for different risk levels and the risk level.

[0036] In this embodiment, baseline PID parameters for different preset risk levels are first obtained. These baseline PID parameters can be obtained through offline simulation and bench testing. In one example, the baseline PID parameters corresponding to each risk level are shown in Table 1. Table 1

[0037] Then, based on the baseline PID parameters and risk level, the dynamic safety boundary of the construction machinery is determined, which can be narrowed in high-risk situations. The positive correction range is expanded. The positive correction range is used to enhance stability.

[0038] Specifically,

[0039]

[0040]

[0041] Step S500: Determine the PID parameter correction value based on the target operating parameters and the preset operating parameters; In this embodiment, the control error, error rate of change, and error variance of each target operating parameter are determined based on the target operating parameters and preset operating parameters. To ensure that the fuzzy judgment closely reflects the actual physical state of the construction machinery, the membership function is optimized to reduce the error variance during operating condition fluctuations, thereby ensuring that safety rules are correctly activated. Error Variance Represented as:

[0042]

[0043] Where M is the length of the sliding window. The width of the membership function. This is the initial width.

[0044] Taking the membership function of error e as an example, with an initial width Δ0 = 1.0, the online adjustment formula is as follows:

[0045] The adjusted membership function is:

[0046] First, a "risk level - rule priority" mapping relationship is established, assigning initial priorities (levels 1-5) to 56 basic fuzzy rules, and then dynamically adjusting them according to the risk level. The fuzzy rule base contains 56 rules, specifically 36 performance-related rules and 20 safety-related rules. Performance-related rules include 12 speed loop performance rules, 12 current loop performance rules, and 12 voltage / efficiency / dynamic response rules. Safety-related rules include 6 overcurrent / overload safety rules, 5 overvoltage / undervoltage safety rules, 5 overshoot / instability / runaway suppression rules, and 4 overtemperature / hardware failure rules. Risk levels can include low risk, medium risk, high risk, and extremely high risk. For low risk (L): priority = initial priority (performance priority); for medium risk (M): priority = initial priority + 1; for high risk (H): priority = initial priority + 2, with an additional + 1 for safety-related rules (such as overshoot suppression); for extremely high risk (VH): priority = initial priority + 3, with safety-related rules at the top priority level. Automatically prioritizing safety constraint rules (such as rules for suppressing overshoot and limiting speed) under high-risk operating conditions can solve the safety and performance imbalance problem caused by the static nature of traditional fuzzy PID rules. This application improves the triggering probability of safety rules under extremely high risks and enhances the overshoot suppression effect by setting a two-level linkage correction mechanism of risk level and fuzzy rules.

[0047] In a specific embodiment of this application, the fuzzy rule base can be represented as: I. Speed ​​Ring Performance Rules (12 rules) 1. If the speed deviation is small and the speed change rate is small → weak output adjustment | initial priority 1 | risk L; 2. If the speed deviation is small and the speed change rate is medium → weak output adjustment | initial priority 1 | risk L; 3. If the speed deviation is small and the speed change rate is large → adjust the output: |Initial Priority 2 |Risk L; 4. If the speed deviation is moderate and the speed change rate is small → adjust the output | initial priority 2 | risk L; 5. If the speed deviation is moderate and the speed change rate is moderate → emphasize the following in the output: | Initial priority 2 | Risk L; 6. If the speed deviation is moderate and the speed change rate is large → output emphasis adjustment | initial priority 3 | risk M; 7. If the speed deviation is large and the speed change rate is small → output emphasis mode | initial priority 3 | risk M; 8. If the speed deviation is large and the speed change rate is medium → output emphasis section | initial priority 3 | risk M; 9. If the speed deviation is large and the speed change rate is large → output is extremely regulated | initial priority 3 | risk H; 10. If the rotational speed is close to the given value and the error changes smoothly → fine-tune the output | initial priority 1 | risk L; 11. If the speed overshoot is small and the speed recovery is slow → output weak damping | initial priority 2 | risk L; 12. If the speed overshoot is too large and falls back quickly → strong output damping | initial priority 3 | risk M; II. Current Loop Performance Rules (12 rules) 13. If the current deviation is small and the current change rate is small → weak output correction | initial priority 1 | risk L; 14. If the current deviation is small and the current change rate is medium → weak output correction | initial priority 1 | risk L; 15. If the current deviation is small and the current change rate is large, adjust the output by | initial priority 2 | risk L; 16. If the current deviation is moderate and the rate of change of current is small → correct the output | initial priority 2 | risk L; 17. If the current deviation is medium and the current change rate is medium → strong correction in output | initial priority 2 | risk L; 18. If the current deviation is moderate and the current change rate is large → strong output correction | initial priority 3 | risk M; 19. If the current deviation is large and the current change rate is small → strong output correction | initial priority 3 | risk M; 20. If the current deviation is large and the current change rate is medium → strong output correction | initial priority 3 | risk M; 21. If the current deviation is large and the current change rate is large → output limiting correction | initial priority 3 | risk H; 22. If the steady-state fluctuation of the current is small → output micro-compensation | initial priority 1 | risk L; 23. If the current steady-state fluctuation → weak output suppression | initial priority 2 | risk L; 24. If the steady-state current fluctuation is large → strong output suppression | initial priority 3 | risk M; III. Voltage / Efficiency / Dynamic Response Rules (12 rules) 25. If the bus voltage is low and the load is light → fine-tune the output boost | initial priority 1 | risk L; 26. If the bus voltage is low and the load is medium → weak output boost adjustment | initial priority 2 | risk L; 27. If the bus voltage is low and the load is heavy → increase output boost pressure | Initial priority 3 | Risk M; 28. If the bus voltage is normal → maintain output voltage regulation | Initial priority 1 | Risk L; 29. If the bus voltage is too high → fine-tune the output voltage reduction | Initial priority 2 | Risk M; 30. If efficiency is low and load is light → optimize output for light load | initial priority 1 | risk L; 31. If efficiency is low and the load is medium → Optimize output parameters | Initial priority 2 | Risk L; 32. If efficiency is low and load is heavy → optimize output overload | initial priority 2 | risk L; 33. If the dynamic response is slow → output "Increase response speed" | Initial priority 2 | Risk L; 34. If the dynamic response oscillation is small → output is weakly damped | initial priority 2 | risk L; 35. If the dynamic response oscillates → the output is damped | initial priority 3 | risk M; 36. If the dynamic response oscillates greatly → output strong damping | initial priority 3 | risk M; IV. Overcurrent / Overload Safety (6 items) 37. If the current slightly exceeds the limit → Output current limiting protection | Safety | Initial priority 3 | Risk H; 38. If the current significantly exceeds the limit → strong current limiting | safety | initial priority 4 | risk VH; 39. If the current exceeds the limit rapidly → output instantaneous shutdown | Safety | Initial priority 5 | Risk VH (top); 40. If the load is slightly overloaded → reduce output load | Safety | Initial priority 3 | Risk H; 41. If the load is moderately overloaded → output forced load reduction | safety | initial priority 4 | risk VH; 42. If the load is severely overloaded → output shutdown protection | Safety | Initial priority 5 | Risk VH (top); V. Overvoltage / Undervoltage Safety (5 items) 43. If there is a slight overvoltage → Output voltage limiting protection | Safety | Initial priority 3 | Risk H; 44. If the voltage is severely overvoltage → output rapid discharge | Safety | Initial priority 4 | Risk VH; 45. If the voltage is extremely low → Output undervoltage protection | Safety | Initial priority 3 | Risk H; 46. ​​If the voltage drops drastically → Output lockout | Safety | Initial priority 4 | Risk VH; 47. If the bus voltage changes drastically → Output voltage smoothing | Safety | Initial priority 3 | Risk H; VI. Overshoot / Instability / Runaway Suppression (5 items) 48. If the speed overshoot is slight → output overshoot suppression | safety | initial priority 3 | risk H (additional +1); 49. If the speed overshoot is significant → output strong overshoot suppression | safety | initial priority 4 | risk VH (additional +1); 50. If the speed becomes unstable and oscillates → output stabilization correction | safety | initial priority 4 | risk VH; 51. If the speed rises out of control → output forced braking | Safety | Initial priority 5 | Risk VH (top); 52. If the system is close to divergence → output emergency shutdown | safety | initial priority 5 | risk VH (top); 7. Over-temperature / Hardware failure (4 items) 53. If the temperature is too high → reduce output power and cool down | Safety | Initial priority 3 | Risk H; 54. If the temperature is too high → output forced shutdown | Safety | Initial priority 4 | Risk VH; 55. If the sensor malfunctions → Output fault tolerance switching | Safety | Initial priority 4 | Risk VH; 56. If the driver fails, output hardware protection | safety | initial priority 5 | risk VH (top).

[0048] Rule strength = membership degree matching degree × priority coefficient (L→1.0, M→1.2, H→1.5, VH→2.0), ensuring that security rules are triggered first when there is high risk.

[0049] Preset operating parameters can be understood as pre-defined operating parameter values. Then, fuzzification processing is applied to the control error, error rate of change, and error variance to obtain fuzzy membership degrees. Rule-based reasoning and defuzzification processing are performed on the fuzzy membership degrees to obtain the PID parameter correction values, i.e., the adjustment amounts for the three initial PID parameters. This application introduces the statistical characteristics of control error to adjust the width parameter of the membership function in real time. When the error variance is large (due to severe operating condition fluctuations), the membership function is widened to improve robustness; when the error variance is small, the function is narrowed to improve control accuracy.

[0050] Step S600: If the PID parameter correction value exceeds the dynamic safety boundary, a safety interruption is triggered.

[0051] In this embodiment, a safety interruption is triggered when the PID parameter correction value exceeds the dynamic safety boundary. In one example, if the defuzzified... If the boundary value is exceeded for three consecutive cycles, a safety warning will be triggered. The proposed solution is based on the Codesys Safety SIL3 safety controller. If the boundary is exceeded for three consecutive cycles, an emergency stop at the SIL3 level will be initiated.

[0052] Through steps S100-S600, the initial operating parameters of the construction machinery are obtained and normalized to obtain the target operating parameters. The dynamic weights of each target operating parameter are determined using the entropy weight method. The risk level of the construction machinery is determined based on the dynamic weights and risk scores of each target operating parameter. The dynamic safety boundary of the construction machinery is determined based on preset benchmark PID parameters for different risk levels and the risk level itself. The PID parameter correction value is determined based on the target operating parameters and preset operating parameters. If the PID parameter correction value exceeds the dynamic safety boundary, a safety interruption is triggered. Thus, by conducting real-time risk assessment and safety judgment for the construction machinery, the safety of its operation can be improved.

[0053] Please see Figure 2 This is a flowchart illustrating an adaptive control method for dynamic risk classification of engineering machinery provided in a specific embodiment of this application. Figure 2 As shown, in this specific embodiment, a Codesys safety controller is used, integrating an FSoE protocol stack and expanding two Ethernet interfaces; a data preprocessing unit (entropy weighting calculation, delay ≤3ms); sensors: tension sensor (0~50t, ±0.1%), tilt sensor (±30°, ±0.05°), speed sensor (0~1000rpm, ±0.02%); and a safety monitoring module (SIL3 certified, emergency stop response ≤2ms). First, the operating parameters of the construction machinery (load F, tilt angle) are collected. The risk factors (speed v, environment d) are normalized and stored in a sliding window. The dynamic weights of each risk factor are calculated using the entropy weight method, and the risk level and weight vector are output based on the risk matrix. If the risk level exceeds the dynamic safety boundary, and the construction machinery is in an extremely high-risk state for more than 20ms, an emergency stop (SIL3) is initiated and an alarm is triggered. The control error e and the error change rate are calculated. and error variance The system performs online optimization of fuzzy membership functions; adjusts the priority of fuzzy rules according to risk levels, prioritizing safety rules for high-risk situations; obtains PID parameter correction values ​​through fuzzification, rule inference, and defuzzification; verifies the correction values ​​based on SIL3 safety constraints; if successful, updates PID parameters and outputs control commands; otherwise, triggers a safety interruption; provides real-time feedback on operating status, and repeats the above steps cyclically. If a check fails, a SIL3 emergency stop and alarm are triggered; if three consecutive limits are exceeded, a SIL3 emergency stop and alarm are triggered.

[0054] Please see Figure 3 This is a schematic diagram of the adaptive control device for dynamic risk classification of construction machinery provided in the embodiments of this application. A second aspect of the embodiments of this application provides an adaptive control device for dynamic risk classification of construction machinery, the device comprising: The acquisition module is used to acquire the initial operating parameters of the construction machinery and normalize the initial operating parameters to obtain the target operating parameters; The first determining module is used to determine the dynamic weights of each of the target operating parameters based on the entropy weight method; The second determining module is used to determine the risk level of the engineering machinery based on the dynamic weights and risk scores of each of the target operating parameters; The third determining module is used to determine the dynamic safety boundary of the construction machinery based on the preset benchmark PID parameters of different risk levels and the risk level. The fourth determining module is used to determine the PID parameter correction value based on the target operating parameters and the preset operating parameters; The control module is used to trigger a safety interrupt when the PID parameter correction value exceeds the dynamic safety boundary.

[0055] The adaptive control device for dynamic risk classification of engineering machinery provided in the second aspect of this application can realize the various processes implemented in the above method embodiments and achieve the same beneficial effects. To avoid repetition, it will not be described again here.

[0056] Please see Figure 4This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. This embodiment of the application also provides an electronic device 4000, including a processor 4100 and a memory 4200. The memory 4200 stores machine-executable instructions that can be executed by the processor 4100. The processor 4100 can execute the machine-executable instructions to implement the above-mentioned adaptive control method for dynamic risk classification of engineering machinery.

[0057] In some embodiments, this application also provides a machine-readable storage medium storing instructions that, when executed by a processor, cause the processor to implement the aforementioned adaptive control method for dynamic risk classification of engineering machinery.

[0058] In some embodiments, this application also provides a computer program product, including a computer program that, when executed by a processor, implements an adaptive control method for dynamic risk classification of engineering machinery according to the above embodiments.

[0059] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0061] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0062] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0063] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0064] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0065] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0066] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

Claims

1. An adaptive control method for dynamic risk classification of engineering machinery, characterized in that, The method includes: The initial operating parameters of the construction machinery are obtained, and the initial operating parameters are normalized to obtain the target operating parameters; The dynamic weights of each target operating parameter are determined based on the entropy weight method; The risk level of the construction machinery is determined based on the dynamic weights and risk scores of each of the target operating parameters. The dynamic safety boundary of the construction machinery is determined based on the preset baseline PID parameters for different risk levels and the risk level. Determine the PID parameter correction value based on the target operating parameters and the preset operating parameters; If the PID parameter correction value exceeds the dynamic safety boundary, a safety interruption is triggered.

2. The method according to claim 1, characterized in that, The determination of the dynamic weights of each target operating parameter based on the entropy weight method includes: Calculate the information entropy of each of the target operating parameters based on the target operating parameters; The dynamic weights of each target operating parameter are determined based on the information entropy of each target operating parameter.

3. The method according to claim 2, characterized in that, The information entropy satisfies: in, Let represent the information entropy of the i-th running parameter at time k, m represent the number of sampling windows, and N represent the total number of sampling windows. This represents the normalized probability value of the i-th running parameter at time k in the m-th adoption window; in, This represents the normalized target running parameter value of the i-th running parameter in the m-th sampling window.

4. The method according to claim 1, characterized in that, The step of determining the PID parameter correction value based on the target operating parameters and preset operating parameters includes: The control error, error rate of change, and error variance of each target operating parameter are determined based on the target operating parameters and the preset operating parameters. The control error, the rate of change of error, and the variance of error are fuzzified to obtain fuzzy membership degrees. The fuzzy membership degree is subjected to rule-based reasoning and defuzzification to obtain the PID parameter correction value.

5. The method according to claim 1, characterized in that, The step of determining the risk level of the construction machinery based on the dynamic weights and risk scores of each of the target operating parameters includes: The target operating parameters are linearly mapped according to a preset ratio to obtain a risk score for each target operating parameter. The sum of the products of the risk scores of each target operating parameter and the dynamic weights is determined as the risk score of the engineering machinery. The risk level of the construction machinery is determined based on its risk score.

6. The method according to claim 5, characterized in that, The risk score of the construction machinery meets the following requirements: in, This represents the risk score of the construction machinery at time k. This represents the dynamic weight of the i-th running parameter at time k. This represents the risk score of the i-th running parameter at time k.

7. An adaptive control device for dynamic risk classification of engineering machinery, characterized in that, The device includes: The acquisition module is used to acquire the initial operating parameters of the construction machinery and normalize the initial operating parameters to obtain the target operating parameters; The first determining module is used to determine the dynamic weights of each of the target operating parameters based on the entropy weight method; The second determining module is used to determine the risk level of the engineering machinery based on the dynamic weights and risk scores of each of the target operating parameters; The third determining module is used to determine the dynamic safety boundary of the construction machinery based on the preset benchmark PID parameters of different risk levels and the risk level. The fourth determining module is used to determine the PID parameter correction value based on the target operating parameters and the preset operating parameters; The control module is used to trigger a safety interrupt when the PID parameter correction value exceeds the dynamic safety boundary.

8. An electronic device, characterized in that, include: The memory is configured to store instructions; The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the adaptive control method for dynamic risk classification of engineering machinery according to any one of claims 1 to 6.

9. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to execute the adaptive control method for dynamic risk classification of engineering machinery according to any one of claims 1 to 6.

10. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device performs the adaptive control method for dynamic risk classification of engineering machinery as described in any one of claims 1 to 6.

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