A power matching control method and system applied to a construction machine

Abstract

The application relates to the technical field of mechanical control, in particular to a power matching control method and system applied to engineering machinery. The method comprises the following steps: acquiring a real-time engine speed corresponding to a current control time period of the engineering machinery; acquiring a current working condition parameter, performing working condition recognition analysis on the current working condition parameter according to a preset full-working-condition database, and obtaining a target real-time working condition; determining an initial control current according to the real-time engine speed and the target real-time working condition; performing a dynamic closed-loop correction operation on the initial control current to obtain a target control current; and controlling a proportional valve controller of the engineering machinery according to the target control current to perform power matching on the engineering machinery in the current control time period. Through the construction of the database covering full working conditions, the power matching can comprehensively adapt to multi-working-condition changes, the accuracy of the power matching is guaranteed, and the accuracy of the power matching is further improved through the dynamic closed-loop correction for adjusting the control current.

Description

Technical Field

[0001] This invention belongs to the field of mechanical control technology, specifically relating to a power matching control method and system for engineering machinery. Background Technology

[0002] Currently, power matching for construction machinery such as excavators and loaders mainly focuses on the current control of the main pump's proportional valve. The required pump power is determined based on the engine's gear position, the actual pump power is calculated using the collected system main pressure, and real-time power PID control is applied to the pump to ultimately determine its displacement.

[0003] However, in practice, it has been found that traditional matching control methods cannot adapt to changes in multiple operating conditions. Under different combinations of oil temperature, speed, and load pressure, the current-flow characteristics of the proportional valve vary significantly, resulting in low power matching accuracy and difficulty in fully utilizing engine power.

[0004] Therefore, improving the power matching accuracy of construction machinery in order to fully utilize engine power is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a power matching control method and system for construction machinery, which can improve the power matching accuracy of construction machinery and make full use of engine power.

[0006] To address the aforementioned technical problems, the first aspect of this invention discloses a power matching control method applied to engineering machinery, the method comprising: Obtain the real-time engine speed of the construction machinery in the current control time period; The current operating condition parameters are obtained, and the operating condition identification and analysis are performed on the current operating condition parameters according to the preset full operating condition database to obtain the target real-time operating condition; the full operating condition database is obtained by clustering a pre-collected full operating condition sample dataset. The initial control current is determined based on the real-time engine speed and the target real-time operating condition. A dynamic closed-loop correction operation is performed on the initial control current to obtain the target control current; The proportional valve controller of the construction machinery is controlled according to the target control current to perform power matching of the construction machinery within the current control time period.

[0007] As an optional implementation, in the first aspect of the present invention, the full-condition database includes multiple standard clustered conditions; The step of performing operating condition identification and analysis on the current operating condition parameters based on a preset full-operating condition database to obtain the target real-time operating condition includes: For each of the standard clustering conditions, calculate the membership degree between the current condition parameter and the standard clustering condition to obtain the current membership degree corresponding to the standard clustering condition; The maximum value is filtered for the current membership degree corresponding to all the standard clustering conditions to obtain the target clustering condition corresponding to the maximum current membership degree, and the target clustering condition is confirmed as the target real-time condition.

[0008] As an optional implementation, in the first aspect of the present invention, determining the initial control current based on the real-time engine speed and the target real-time operating condition includes: Obtain the speed and torque curve data corresponding to the engine of the construction machinery; The real-time engine speed is matched based on the speed-torque curve data to obtain the target maximum torque corresponding to the real-time engine speed; The initial control current is determined based on the target maximum torque and the target real-time operating conditions.

[0009] As an optional implementation, in the first aspect of the present invention, determining the initial control current based on the target maximum torque and the target real-time operating condition includes: Obtain pre-stored torque-pressure curve data corresponding to the target's real-time operating conditions; The target maximum torque is matched based on the torque-pressure curve data to obtain the target load pressure corresponding to the target maximum torque; Obtain pre-stored current-flow curve data corresponding to the target's real-time operating conditions; Using the target load pressure as an index, the current flow curve data is reverse-queried to obtain the initial control current corresponding to the target load pressure.

[0010] As an optional implementation, in the first aspect of the present invention, performing a dynamic closed-loop correction operation on the initial control current to obtain the target control current includes: A dynamic optimization correction operation is performed on the initial control current to obtain a first corrected current; A double-closed-loop correction operation is performed on the first correction current to obtain the target control current.

[0011] As an optional implementation, in the first aspect of the present invention, the step of performing a dynamic optimization correction operation on the initial control current to obtain a first corrected current includes: The target reference flow rate is calculated based on the target load pressure and the target maximum torque; the target reference flow rate is used to characterize the engine power requirement of the construction machinery. The initial control current is predicted and optimized based on the target reference flow rate to obtain a target current sequence, and the first sequence of the target current sequence is confirmed as the target predicted current. Obtain the proportional valve hysteresis characteristic data of the engineering machinery; The target predicted current is compensated and corrected based on the proportional valve hysteresis characteristic data to obtain the first corrected current.

[0012] As an optional implementation, in the first aspect of the invention, performing a double closed-loop correction operation on the first corrected current to obtain the target control current includes: The target static current is obtained by calculating the static current based on the target reference flow rate. The difference between the first corrected current and the target static current is calculated to obtain the first deviation current; The first deviation current is adjusted and corrected by an outer loop to obtain the second corrected current; The target actual current is obtained by acquiring the current of the proportional valve controller of the engineering machinery at a prior time. The difference between the second corrected current and the target actual current is calculated to obtain the second deviation current; The second deviation current is corrected by inner-loop regulation to obtain the target control current.

[0013] A second aspect of this invention discloses a power matching control system for engineering machinery, the system comprising: The real-time speed acquisition module is used to acquire the real-time engine speed of the construction machinery in the current control time period. The real-time operating condition confirmation module is used to acquire the current operating condition parameters and perform operating condition identification and analysis on the current operating condition parameters according to the preset full operating condition database to obtain the target real-time operating condition; the full operating condition database is obtained by clustering a pre-collected full operating condition sample dataset. The first current determination module is used to determine the initial control current based on the real-time engine speed and the target real-time operating condition. The second current determination module is used to perform a dynamic closed-loop correction operation on the initial control current to obtain the target control current; The power matching control module is used to control the proportional valve controller of the construction machinery to operate according to the target control current, so as to perform power matching on the construction machinery within the current control time period.

[0014] As an optional implementation, in a second aspect of the present invention, the full-condition database includes multiple standard clustered conditions; The real-time operating condition confirmation module performs operating condition identification and analysis on the current operating condition parameters based on a preset full operating condition database to obtain the target real-time operating condition in the following specific ways: For each of the standard clustering conditions, calculate the membership degree between the current condition parameter and the standard clustering condition to obtain the current membership degree corresponding to the standard clustering condition; The maximum value is filtered for the current membership degree corresponding to all the standard clustering conditions to obtain the target clustering condition corresponding to the maximum current membership degree, and the target clustering condition is confirmed as the target real-time condition.

[0015] As an optional implementation, in a second aspect of the present invention, the specific method by which the first current determining module determines the initial control current based on the real-time engine speed and the target real-time operating condition includes: Obtain the speed and torque curve data corresponding to the engine of the construction machinery; The real-time engine speed is matched based on the speed-torque curve data to obtain the target maximum torque corresponding to the real-time engine speed; The initial control current is determined based on the target maximum torque and the target real-time operating conditions.

[0016] As an optional implementation, in a second aspect of the present invention, the specific method by which the first current determining module determines the initial control current based on the target maximum torque and the target real-time operating condition includes: Obtain pre-stored torque-pressure curve data corresponding to the target's real-time operating conditions; The target maximum torque is matched based on the torque-pressure curve data to obtain the target load pressure corresponding to the target maximum torque; Obtain pre-stored current-flow curve data corresponding to the target's real-time operating conditions; Using the target load pressure as an index, the current flow curve data is reverse-queried to obtain the initial control current corresponding to the target load pressure.

[0017] As an optional implementation, in a second aspect of the present invention, the second current determination module performs a dynamic closed-loop correction operation on the initial control current to obtain the target control current in the following specific ways: A dynamic optimization correction operation is performed on the initial control current to obtain a first corrected current; A double-closed-loop correction operation is performed on the first correction current to obtain the target control current.

[0018] As an optional implementation, in a second aspect of the present invention, the second current determining module performs a dynamic optimization correction operation on the initial control current to obtain the first corrected current in the following specific ways: The target reference flow rate is calculated based on the target load pressure and the target maximum torque; the target reference flow rate is used to characterize the engine power requirement of the construction machinery. The initial control current is predicted and optimized based on the target reference flow rate to obtain a target current sequence, and the first sequence of the target current sequence is confirmed as the target predicted current. Obtain the proportional valve hysteresis characteristic data of the engineering machinery; The target predicted current is compensated and corrected based on the proportional valve hysteresis characteristic data to obtain the first corrected current.

[0019] As an optional implementation, in a second aspect of the present invention, the second current determination module performs a double closed-loop correction operation on the first correction current to obtain the target control current in the following specific ways: The target static current is obtained by calculating the static current based on the target reference flow rate. The difference between the first corrected current and the target static current is calculated to obtain the first deviation current; The first deviation current is adjusted and corrected by an outer loop to obtain the second corrected current; The target actual current is obtained by acquiring the current of the proportional valve controller of the engineering machinery at a prior time. The difference between the second corrected current and the target actual current is calculated to obtain the second deviation current; The second deviation current is corrected by inner-loop regulation to obtain the target control current.

[0020] A third aspect of this invention discloses another power matching control system applied to engineering machinery, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute a power matching control method for engineering machinery disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked by a processor, are used to execute a power matching control method for engineering machinery disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows: After acquiring real-time engine speed and current operating parameters, real-time operating condition identification is performed based on clustered full-condition sample data. Then, an initial control current is determined based on the real-time engine speed and operating conditions. Dynamic closed-loop correction is applied to the initial control current to obtain the target control current. Finally, the proportional valve controller of the construction machinery is controlled according to the target control current to achieve power distribution. By constructing a database covering all operating conditions, power matching can comprehensively adapt to multiple operating condition changes, ensuring the accuracy of power matching and fully utilizing engine power. Furthermore, the accuracy of power matching is further improved by adjusting the control current through dynamic closed-loop correction. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of a power matching control method for engineering machinery disclosed in an embodiment of the present invention; Figure 2 This is a schematic diagram of current-flow curves tested at a fixed rotation speed, different temperatures, and different currents according to an embodiment of the present invention. Figure 3 This is a schematic diagram of torque-pressure curves calculated under different temperatures and currents according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a power matching control system for engineering machinery disclosed in an embodiment of the present invention; Figure 5 This is a schematic diagram of another power matching control system for engineering machinery disclosed in an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, or product may include a series of steps or units, or may not be limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or processes.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] Currently, power matching for construction machinery such as excavators and loaders mainly focuses on the current control of the main pump's proportional valve. The required pump power is determined based on the engine's gear position, the actual pump power is calculated using the collected system main pressure, and real-time power PID control is applied to the pump to ultimately determine its displacement.

[0029] However, in practice, it has been found that traditional matching control methods cannot adapt to changes in multiple operating conditions. Under different combinations of oil temperature, speed, and load pressure, the current-flow characteristics of the proportional valve vary significantly, resulting in low power matching accuracy and difficulty in fully utilizing engine power.

[0030] Therefore, improving the power matching accuracy of construction machinery in order to fully utilize engine power is a technical problem that urgently needs to be solved.

[0031] To address the aforementioned technical problems, this invention discloses a power matching control method and system for construction machinery, aiming to improve the power matching accuracy of construction machinery and fully utilize engine power. Detailed descriptions follow.

[0032] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a power matching control method for engineering machinery disclosed in an embodiment of the present invention. Figure 1 The method shown can be applied to power matching control systems, which can improve the power matching accuracy of construction machinery to fully utilize engine power. For example... Figure 1 As shown, the power matching control method for engineering machinery disclosed in this embodiment of the invention includes, but is not limited to, the following operations: 101. Obtain the real-time engine speed of the construction machinery in the current control time period; 102. Obtain the current operating condition parameters, and perform operating condition identification and analysis on the current operating condition parameters according to the preset full operating condition database to obtain the target real-time operating condition; the full operating condition database is obtained by clustering the pre-collected full operating condition sample dataset. 103. Determine the initial control current based on the real-time engine speed and the target real-time operating conditions; 104. Perform dynamic closed-loop correction on the initial control current to obtain the target control current; 105. The proportional valve controller of the engineering machinery is operated according to the target control current to perform power matching of the engineering machinery within the current control time period.

[0033] In this embodiment of the invention, after acquiring the real-time engine speed and current operating parameters, real-time operating condition identification is performed based on clustered full-condition sample data. Then, an initial control current is determined based on the real-time engine speed and real-time operating conditions. The initial control current is then dynamically closed-loop corrected to obtain the target control current. Finally, the proportional valve controller of the construction machinery is controlled according to the target control current to achieve power distribution. By constructing a database covering all operating conditions, the system can comprehensively adapt to multiple operating condition changes during power matching, ensuring the accuracy of power matching and fully utilizing engine power. Furthermore, the accuracy of power matching is further improved by adjusting the control current through dynamic closed-loop correction.

[0034] It should be noted that, in this embodiment of the invention, a full-condition simulation test bench covering oil temperature (0~80℃), engine speed (1000~2000 r / min), and load pressure (0~35 MPa) is constructed. Dual IQ (current-flow) curve data during the "rise + fall" process are collected and converted into TP (torque-pressure) curves related to engine power, thus building a full-condition database of nearly 200,000 sets of operating condition-related data. The specific process of constructing the full-condition database includes the following: Step 1: Set up the working condition simulation test bench A test bench for actual excavator application was constructed. The bench included a proportional valve test fixture, an oil temperature control module (adjustable range 0℃~80℃), a speed regulation module (matching the typical speed range of the excavator engine), a load pressure loading module (outputting pressure from 0 to 35MPa), a flow sensor, a current sensor, a pressure sensor, and a data acquisition unit. The servo proportional valve of the excavator to be tested was installed on the test fixture and connected to the oil temperature control module and the load pressure loading module through pipelines. The sensors collected the input current I, output flow rate Q, and load pressure P of the proportional valve, respectively.

[0035] Load preset programs (torque calculation, engine characteristic curve, FCM / MPC algorithm parameters (cluster number k=6, fuzzy coefficient m=2, etc.) into the calculation module, addressing module, and FC-MPC algorithm operation module, and set the data acquisition cycle to 5ms.

[0036] Step 2: Constructing an IQ curve database under multiple operating conditions Set oil temperature gradient: Divide the oil temperature into 17 levels with increments of 5℃, namely 0℃, 5℃, 10℃, 15℃...25℃, 50℃, 80℃ (covering the common oil temperature range for excavator operation), and maintain the stable oil temperature on the test bench through the oil temperature control module.

[0037] Set the speed gear: Start at 1000 RPM of the excavator engine and increase in 50 RPM increments (e.g., 1200 RPM, 1800 RPM, 2000 RPM) to set the output speed of the speed adjustment module. There are a total of 21 gears to simulate the engine speed under different working intensities.

[0038] Set the load pressure point: In 2.5MPa increments, set the output pressure of the load pressure loading module to 16 levels: 0MPa, 2.5MPa, 5MPa, 7.5MPa, 10MPa...32.5MPa, 35MPa (covering the excavator load pressure range).

[0039] Under each "oil temperature-speed-load pressure" combination, the input current of the proportional valve is first increased from 0A to 750mA (step size 50mA), and the flow rate Q corresponding to each current is recorded to obtain 16 IQ curves for the rising process; then it is reduced from 750mA to 0mA (same step size), and the data is recorded to obtain 16 IQ curves for the falling process (compensating for the hysteresis characteristics of the proportional valve).

[0040] Please see Figure 2 , Figure 2 This is a schematic diagram of current-flow curves tested at a fixed rotation speed, different temperatures, and different currents according to an embodiment of the present invention.

[0041] Each operating condition combination was collected 5 times, and the average flow rate was taken as the final IQ data for that operating condition to avoid random errors. All operating condition data were integrated to form 17 (oil temperature) × 21 (speed) × 16 (pressure) × 32 (current) = 182,784 sets of basic data, which were then integrated into a large model database.

[0042] Step 3: Calculate TP curve data Based on the power balance relationship of the hydraulic system, the IQ curve data is converted into a TP (torque-pressure) curve, establishing a correlation with engine power. The core formula is as follows: Equation (1) Where: P is the load pressure (i.e., the load pressure set in step 2, unit: MPa); Q is the flow rate collected in step 2 (unit: 1 L / min); n is the rotational speed set in step 2 (unit: 1 r / min); T is the proportional valve output torque (unit: N). m).

[0043] Please see Figure 3 , Figure 3 This is a schematic diagram of torque-pressure curves calculated under different temperatures and currents, as disclosed in one embodiment of the present invention.

[0044] Using Equation (1), the (I,Q) data in the "rising / falling IQ curve" under each working condition is converted into (P,T) data, and the TP curve at the current rising time and the TP curve at the current falling time are obtained, which are stored in the large model database together with the IQ curve.

[0045] Step 4: Perform working condition clustering using the FCM (Fuzzy C-means) algorithm. The core of FCM clustering is to classify samples into multiple typical categories based on operating condition characteristics (the number of operating condition categories can be set according to actual needs; in this embodiment, it is set to 6 categories), achieving rapid real-time location of operating conditions and avoiding full data traversal. Specifically, it selects the cluster with the highest membership degree. For a single sample's membership degree row vector, it finds the element with the largest value; the corresponding column index is the cluster to which the sample belongs. The specific process is as follows: Selecting "oil temperature T (°C), speed n (r / min), and load pressure P (MPa)" as clustering features (three-dimensional vectors), and using Min-Max normalization to eliminate dimensional interference, the data is mapped to the [0,1] interval: Equation (2) For T: x_min=0, x_max=80; if T_i=25℃, then T'_i=(25-0) / (80-0)=0.3125; for n: x_min=1000, x_max=2000; if n_i=1800r / min, then n'_i=(1800-1000) / (2000-1000)=0.8; for P: x_min=0, x_max=35; if P_i=20MPa, then P'_i=(20-0) / 35≈0.5714. The final preprocessed sample is x'_i=[T'_i,n'_i,P'_i]. Subsequent calculations use normalized data, and after calculation, the actual physical quantities can be restored by inverse normalization.

[0046] The cluster number is set to k=6 (corresponding to 6 typical working conditions), the fuzzy coefficient is set to m=2, and the termination error is set to ε=10. -5Maximum number of iterations N_max = 50; Randomly generate initial membership matrix U (0) ;U (0) It is a k×N matrix (6 rows × 17) 5 (column), each element u_ij (0) The mathematical foundation of the FCM algorithm must be met: Condition 1, Value Constraint, 0 ≤ u_ij (0) ≤1 (Membership degree is a probability value, which cannot be negative or exceed 1. For example, u_ij=1 means that sample i completely belongs to class j, and u_ij=0 means that it does not belong to class j at all); Condition 2, row sum constraint, for each sample i, ∑u_ij (0) =1 (The sample must belong to a certain type of working condition to ensure that the sample does not "belong to multiple types of working conditions at the same time and the weight exceeds the limit").

[0047] Combined with the excavator working condition clustering settings (6 types of working conditions, N≥17) 5 (Group of samples), the membership matrix U is defined as an "N rows × 6 columns" two-dimensional matrix, in the following format: Sample 1 [u 11 ,u 12 ,u 13 ,u 14 ,u 15 ,u 16 ] Sample 2[u 21 ,u 22 ,u 23 ,u 24 ,u 25 ,u 26 ] ...... Sample N[u_N1,u_N2,u_N3,u_N4,u_N5,u_N6] Here, the core element u_ij represents "the membership degree of the i-th working condition sample to the j-th working condition", for example, u 12 =0.85 means that "sample 1 has an 85% tendency to belong to the medium load and normal temperature condition". The condition category (columns: j=1~6) is as follows: the first column corresponds to the light load and low temperature condition, the second column corresponds to the medium load and normal temperature condition, the third column corresponds to the heavy load and high temperature condition, the fourth column corresponds to the idling and standby condition, the fifth column corresponds to the strain transition condition, and the sixth column corresponds to the low temperature and heavy load condition.

[0048] Based on the initially defined six typical working conditions of excavators (such as light load and low temperature, heavy load and high temperature, etc.), the samples are preliminarily classified according to experience, and the sample u_ij is classified into the j-th category. (0) We set 0.9, and for the other classes we set 0.1 / 5 = 0.02, to improve the convergence speed of subsequent iterations.

[0049] The initial cluster centers c_j are calculated using the following formula.(0) (j=1~6): Equation (3) Among them, c_j (0) The initial cluster center of the j-th class (three-dimensional vector: [T_cj, n_cj, P_cj]) is to be calculated, corresponding to the initial core points of 6 typical working conditions; u_ij (0) Let ∑u_ij be the initial membership degree of the i-th sample belonging to the j-th class, satisfying ∑u_ij (0) =1 (the sum of the membership degrees of each sample is 1), m is the fuzzy coefficient (controlling the degree of clustering fuzziness); x_i represents the feature vector of the i-th full-condition sample; N is the total number of full-condition samples, and in this embodiment of the invention, N≥17 is taken. 5 Group (covering 0℃~80℃, 1000~2000r / min, 0~35MPa); j=1~6 (corresponding to the 6 typical working conditions of the present invention embodiments).

[0050] Based on the current cluster center c (t) Update the membership matrix U according to the following formula. (t+ ¹ ) : Equation (4) Use the current cluster center c_j (t) The matrix is ​​updated to U using the membership formula. (t+ ¹ ) Then use the new matrix U (t+ ¹ ) Calculate the new cluster centers c_j (t+ ¹ ) This refers to "reassigning membership degrees based on the distance between samples and cluster centers." The specific calculation method and steps are as follows: Given input: the cluster centers c_j of the current iteration (t) (j=1~6, such as j=2, the class center is (25℃, 1800r / min, 15MPa), which needs to be normalized first), normalized sample x'_i (200,000 groups, such as x'_i=[0.3125,0.8,0.5714]), fuzzy coefficient m=2 (fixed parameter).

[0051] Core objective: For each sample i, calculate its new membership degree u_ij belonging to the 6 working conditions. (t+ ¹ ) This forms a new N×6 matrix U. (t+ ¹ ) .

[0052] Calculate the Euclidean distance from the sample to each cluster center: For a single normalized sample x'_i (e.g., x'_i = [0.3125, 0.8, 0.5714] corresponding to "25℃, 1800r / min, 20MPa"), calculate its distance to the 6 cluster centers c_j. (t) Euclidean distance ||x'_i-c_j (t) The Euclidean distance formula (for three-dimensional eigenvectors) is as follows: ||x'_i-c_j (t) ||=

[0053] Calculate the class center c_2 from x'_i to j=2. (t) The distance = [0.3125, 0.8, 0.4286] (normalized to 0.3125 at 25℃, 0.8 at 1800 r / min, and 0.4286 at 15 MPa): ||x'_i-c_2 (t) || =

[0054] ≈0.1428; Similarly, the distances to classes j=1~6 are calculated, resulting in the distance set: {0.35, 0.1428, 0.52, 0.41, 0.18, 0.32}.

[0055] The core ratio term in formula (4) is: for each cluster j, calculate "2 / (m-1) power of "distance from sample to center of cluster j / distance from sample to center of cluster l", where l traverses clusters 1 to 6 (when m=2, 2 / (m-1)=2). Ratio term formula: (‖x'_i-c_j (t) || / ||x'_i-c_l (t) Example: Taking class j=2 and class l=5 as an example, ||x'_i-c_2 (t) ||=0.1428, ||x'_i-c_5 (t) If ||=0.18, then the ratio term = (0.1428 / 0.18)²≈0.63.

[0056] Calculate the summation term in the denominator: Summing the ratio terms of the above categories l=1~6, we obtain the denominator of equation (4): ∑ l =1 6 (‖x'_i-c_j (t) || / ||x'_i-c_l (t)Example: For class j=2, the denominator = (0.1428 / 0.35)² + (0.1428 / 0.1428)² + (0.1428 / 0.52)² + (0.1428 / 0.41)² + (0.1428 / 0.18)² + (0.1428 / 0.32)² ≈ 0.16 + 1 + 0.075 + 0.12 + 0.63 + 0.20 ≈ 2.185.

[0057] Calculate the membership degree u_ij (t+ ¹ ) According to formula (4), u_ij (t+ ¹ ) =1 / the sum of the terms in the denominator, and we get the new membership degree of sample i belonging to class j. Example: u_i2 for class j=2 (t+ ¹ ) =1 / 2.185≈0.457, meaning that 45.7% of this sample tends to belong to the medium-load, normal-temperature operating condition; repeat steps 1-4 to calculate the membership degree of this sample to the other 5 categories (e.g., u_i5). (t+ ¹ ) (≈0.32), ultimately ensuring that the sum of the 6 memberships of the sample is 1 (satisfying the row sum constraint).

[0058] Batch calculation and matrix generation: Perform the above four steps for each of the 200,000 samples to obtain 6 membership values ​​for each sample. Combine these values ​​in the format of "sample row × working condition column" to generate a new membership matrix U. (t+ ¹ ) Matrix features: Each row corresponds to one sample, each column corresponds to one type of working condition, and the element u_ij (t+ ¹ ) ∈[0,1], the sum of the elements in each row is 1.

[0059] If sample x'_i is clustered with a certain cluster center c_j (t) If they completely overlap (distance is 0), then the membership degree u_ij of that class is... (t+ ¹ ) =1, all other classes are 0 (samples completely belong to this class); after calculation, rows and constraints need to be verified. If the deviation exceeds 10... -4 Normalization correction is required to ensure that the mathematical requirements of the FCM algorithm are met; this calculation needs to be performed alternately with "cluster center update" (using U). (t+ ¹ ) Substitute into equation (3) to calculate c_j (t+ ¹ ) ), until the change in cluster centers Δc < ε (10 -5 The iteration terminates at this point.

[0060] Calculate the change in cluster centers Δc = max‖c_j (t +¹ ) -c_j(t) If Δc < ε or N_max is reached, the iteration stops. At this point, U is the "final membership matrix," which accurately reflects the working condition affiliation of all samples. The final cluster centers and membership matrix are output. After convergence, the final cluster centers for the six working conditions are shown in the table below: Table 1: Cluster Centers and Preloading Parameters for 6 Typical Working Conditions

[0061] The specific values ​​of "(T,n,P)" in the table are the final cluster centers of the 6 types of working conditions. These values ​​are the "core features of the same type of working conditions" mined by the algorithm from massive amounts of data. The MPC parameters (Np, Nc, ω1, ω2) and outer loop PID parameters (Kp1, Ki1, Kd1) corresponding to each type of working condition are based on the "calculated cluster centers" and calibrated in conjunction with engineering experiments.

[0062] In one optional embodiment, the full-condition database includes multiple standard clustered conditions; Based on a pre-set full-condition database, the current condition parameters are analyzed to identify the target real-time condition, including: For each standard clustering condition, calculate the membership degree between the current condition parameter and the standard clustering condition to obtain the current membership degree corresponding to the standard clustering condition; The maximum value of the current membership degree corresponding to all standard clustering conditions is filtered to obtain the target clustering condition corresponding to the maximum current membership degree, and the target clustering condition is confirmed as the target real-time condition.

[0063] In this optional embodiment, the standard clustering condition is the 6 clustering conditions in Table 1. Collect the current condition parameters x0=(T0,n0,P0), substitute them into equation (4) to calculate its membership degree u_j0 (j=1~6) to the 6 clusters, and select the cluster with the largest membership degree j*=argmax(u_j0) (e.g., when x0=(25,1800,20), j*=2).

[0064] In another alternative embodiment, determining the initial control current based on the real-time engine speed and the target real-time operating condition includes: Obtain the speed and torque curve data corresponding to the engine of the construction machinery; The target maximum torque is obtained by matching the real-time engine speed with the speed-torque curve data. The initial control current is determined based on the target maximum torque and the target real-time operating conditions.

[0065] In this optional embodiment, within the current control cycle, the real-time speed n0 set by the bench speed adjustment module (i.e., the actual engine speed when the excavator is working) is obtained, and the engine characteristic database (which pre-stores the "speed-maximum torque" curve of the excavator engine of this model) is called to determine the maximum torque T_max that the engine can provide at the speed n0.

[0066] Let the discrete points of the curve be (n) l , T l ), (n l+1 , T l+1 If n l ≤n0≤n l+1 ,but: Equation (5) In yet another optional embodiment, determining the initial control current based on the target maximum torque and the target real-time operating condition includes: Acquire pre-stored torque-pressure curve data that corresponds to the target real-time operating conditions; Match the target maximum torque with the torque-pressure curve data to obtain the target load pressure corresponding to the target maximum torque; Acquire pre-stored current-flow curve data that corresponds to the target real-time operating conditions; Using the target load pressure as an index, a reverse lookup process is performed on the current-flow curve data to obtain the initial control current corresponding to the target load pressure.

[0067] In this optional embodiment, the target load pressure P_target is first determined: in the "rising / falling TP curve" corresponding to class j, the P_target that matches T_max (e.g., T_max=120N) is found. When m, P_target = 28MPa). Then, address the initial control current: using P_target as the index, look up the "rising / falling IQ curve" of class j in reverse to obtain the rising initial current I_up0 and the falling initial current I_down0 (e.g., when j* = 2 classes, P_target = 28MPa, I_up0 = 345mA, I_down0 = 330mA).

[0068] In yet another optional embodiment, a dynamic closed-loop correction operation is performed on the initial control current to obtain the target control current, including: A dynamic optimization correction operation is performed on the initial control current to obtain the first corrected current; A double-closed-loop correction operation is performed on the first correction current to obtain the target control current.

[0069] In this optional embodiment, the initial current is precisely and dynamically optimized and corrected through MPC (Model Predictive Control) to solve the control deviation caused by "hydraulic system lag and operating condition fluctuation" in traditional technology.

[0070] In yet another optional embodiment, a dynamic optimization correction operation is performed on the initial control current to obtain a first corrected current; including: The target reference flow rate is calculated based on the target load pressure and the target maximum torque; the target reference flow rate is used to characterize the engine power requirements of the construction machinery. The initial control current is predicted and optimized based on the target reference flow rate to obtain the target current sequence, and the first sequence of the target current sequence is confirmed as the target predicted current. Obtain hysteresis characteristic data of proportional valves in construction machinery; The target predicted current is compensated and corrected based on the proportional valve hysteresis characteristic data to obtain the first corrected current.

[0071] In this optional embodiment, the dynamic optimization correction operation is performed based on a pre-built dynamic optimization correction model. The process of building the dynamic optimization model is as follows: Construct a dynamic "current-flow" model for a proportional valve to predict future flow rate changes (tentatively 10 steps, 15ms each, total 150ms). Static IQ model (class j fitting): Equation (6) Where a_j, b_j, and c_j are the pre-stored fitting coefficients for the target cluster j*=2 (medium load, normal temperature). (For example, if j=2 clusters, a_j=0.12, b_j=2×10^2.) -4 c_j=5; I(k) unit: mA; Q_static(k) unit: L / min).

[0072] The pre-stored fitting coefficients reflect the inherent characteristics of the proportional valve under this operating condition: for every 1mA increase in current I, the static flow rate Q_static will contribute 0.12L / min due to a_j, and a small increment due to b_j (quadratic term) (adapting to the slight nonlinearity of the proportional valve), and c_j is the basic leakage flow rate at zero current; specific operating condition: if it is changed to j=3 (heavy load and high temperature), a_j, b_j, and c_j will change (the oil viscosity decreases at high temperature, the flow rate is greater for the same current, and a_j will increase).

[0073] This invention creatively establishes a dynamic prediction model (first-order inertial discretization) of the "current-flow" dynamic model of a proportional valve, accurately predicting the flow rate in the future k=1 to 10 steps (e.g., 15ms per step, totaling 150ms). The dynamic model (first-order inertial discretization, compensating for hysteresis) is as follows: Equation (7) Where α = 1 - e^(-T_s / τ), T_s = 15 ms, τ = 30 ms, τ = 30 ms is the lag time constant, α ≈ 0.393; Q(k) is the predicted flow rate at the k-th step.

[0074] Calculation of reference flow Q_ref: Substituting T_max, P_target, and n0 into equation (1) and transforming, we get:

[0075] Where Q_ref is in units of L / min; e.g., T_max = 120N When m, P_target=28MPa, n0=1800r / min, Q_ref=35L / min.

[0076] MPC objective function construction and optimization (the optimization aims to "ensure that the flow rate tracks the target value while preventing excessive current fluctuations"), objective function (minimizing flow deviation and current fluctuations), balancing control accuracy and hardware protection: Equation (9) (ω1 and ω2 are the preload weights for class j. If j = class 2, ω1 = 0.7 and ω2 = 0.3. ω1 determines whether the engine power can be accurately matched, and ω2 determines whether the proportional valve can work stably. Np and Nc are the preload prediction / control step size.) Constraints: Current constraint: 100mA≤I(k)≤750mA; Current change rate constraint: |I(k+1)-I(k)|≤50mA / step; Flow constraint: Q(k)≤Q_max(n0,P_target) (Q_max is the maximum flow rate of the main pump, obtained from a table); Optimization Solution: The quadratic programming problem is solved using the interior-point method to obtain the optimal current sequence I*(1)~I*(Nc). The first step is to predict the target currents I_pred_up=I*(1) and I_pred_down=β. I*(1) (β=0.95 is the compensation coefficient for the descent process, such as I_pred_up=350mA, I_pred_down=332.5mA). The specific solution process is as follows: Initial feasible solution setting: The initial current I_up0=345mA obtained by FCM addressing is used as the initial value I(0)=345mA. Combined with the current change rate constraint, the initial current sequence [345mA,370mA,390mA] is initially generated (each step increment ≤50mA).

[0077] Introducing a barrier function to transform an unconstrained problem: To handle inequality constraints, a logarithmic barrier function is added to the objective function J, integrating the constraints into the optimization objective. J'=J-μ·[ln(I(k)-100)+ln(750-I(k))+ln(50-(I(k+1)-I(k)))+ln(Q_max-Q(k))] Where μ is the barrier parameter (initially μ=1, gradually decreasing to 10 during iterations). -4 This ensures that the current and flow rate always meet the constraints during the iteration process.

[0078] Gradient descent iterative optimization: ① Take the partial derivatives of J' with respect to the current sequences I(1), I(2), and I(3) to obtain the gradient vector. J'; ② Update the current sequence using Newton's method: I_new = I_old - α·(H - ¹· J'), where H is the Hessian matrix (second derivative matrix), and α=0.1 is the step size factor; ③ Repeat the iteration until the gradient norm is reached. J'‖<10 - ³, at this point, the optimal current sequence is obtained; 4. Solution results for j*=2 working conditions: After 3 iterations, the optimal current sequence is [350mA, 375mA, 395mA], which satisfies all constraints.

[0079] MPC employs a "rolling time-domain control" strategy, executing only the first current step of the optimal sequence while compensating for the hysteresis characteristics of the proportional valve. Current output during the rising process: Take the first step of the optimal sequence, I*(1)=350mA, as the predicted target current for the rising process, I_pred_up=350mA; Current compensation during the descent process: Considering the hysteresis characteristics of the proportional valve current during descent (under the same flow rate, the descent current is 5%~8% lower than the rise current), a compensation coefficient β=0.95 (j*=measured value under type 2 operating conditions) is introduced to calculate the target descent current: I_pred_down=β·I*(1)=0.95×350=332.5mA Output verification: Before the final output, verify whether the current meets the constraints (350mA and 333mA are both within the range of 100~750mA). After ensuring that there is no risk of exceeding the limit, the current is transmitted to the dual closed-loop PID module for further correction.

[0080] Key calculation notes: The interior point method solution process can be implemented through the hardware acceleration module of the embedded DSP. The solution time plus the FCM addressing time under the j*=2 working condition meets the real-time requirements.

[0081] MPC dynamic optimization is the core of the FC-MPC algorithm—it transforms the "static initial current" obtained by FCM clustering into "dynamically optimized current," solving the core pain point of excavators' "rapid fluctuations in operating conditions and large hydraulic lag," and minimizing power matching error.

[0082] In yet another optional embodiment, a double-loop correction operation is performed on the first correction current to obtain the target control current, including: The target static current is obtained by calculating the static current based on the target reference flow rate. The difference between the first corrected current and the target static current is calculated to obtain the first deviation current; The first deviation current is corrected by outer loop adjustment to obtain the second corrected current; Obtain the target actual current obtained by collecting current data from the proportional valve controller of the construction machinery at a previous moment; The difference between the second corrected current and the target actual current is calculated to obtain the second deviation current; The second deviation current is corrected by inner-loop regulation to obtain the target control current.

[0083] In this optional embodiment, the specific process of adjusting and correcting the outer loop (target current correction loop) includes: Traditional static current I_static calculation: By solving equation (6) in reverse order of Q_ref, we get: Equation (10) Outer-loop PID control: Input deviation e_outer = I_pred - I_static, output intermediate target current I_mid:

[0084] Equation (11) Where Kp1, Ki1, and Kd1 are preloaded parameters for class j. For example, if j = 2 classes, Kp1 = 0.8, Ki1 = 0.05, and Kd1 = 0.1; when e_outer = 5mA, I_mid = 352mA.

[0085] The specific process for adjusting and correcting the inner loop (current output execution loop) is as follows: Inner-loop PID control: Input deviation e_inner = I_mid - I_act (I_act is the actual current collected by the sensor, such as 348mA), output final target current I_final:

[0086] Equation (12) With fixed parameters Kp2=1.2, Ki2=0.1, Kd2=0.05, and e_inner=4mA, I_final_up=353mA.

[0087] For the outer loop (target current correction loop): In this embodiment of the invention, the target current calculated by the "TP curve + engine torque" model is used as the command value, and the original calculated current under traditional static conditions (such as the theoretical current value under fixed conditions) is used as the feedback value. The "final target current" is output through PID regulation to ensure that the target current is dynamically matched with the real-time operating conditions (oil temperature, speed, load).

[0088] For the inner loop (current output execution loop): the “final target current” output from the outer loop is used as the command value, and the actual output current of the solenoid valve (acquired in real time by the vehicle controller) is used as the feedback value. The output of the drive module is quickly adjusted by PID to ensure that the actual current accurately tracks the command value and eliminates the output deviation caused by solenoid valve hysteresis, line loss, etc.

[0089] By using dual closed-loop PID control, this invention not only solves the fundamental problems of "incomplete working condition coverage and inaccurate current addressing", but also eliminates the two core error sources of "target mismatch" and "execution deviation" through hierarchical control, further enhancing the accuracy and stability of proportional valve hysteresis control, and making it more suitable for the complex and ever-changing operating scenarios of excavators.

[0090] Example 2 Please see Figure 4 , Figure 4 This is a schematic diagram of a power matching control system for engineering machinery disclosed in an embodiment of the present invention. Figure 4 The system shown can be used to perform the method described in Embodiment 1. Figure 4 As shown, the power matching control system for engineering machinery disclosed in this embodiment of the invention includes, but is not limited to: The real-time speed acquisition module 201 is used to acquire the real-time engine speed of the construction machinery in the current control time period. The real-time operating condition confirmation module 202 is used to acquire the current operating condition parameters and perform operating condition identification and analysis on the current operating condition parameters according to the preset full operating condition database to obtain the target real-time operating condition; the full operating condition database is obtained by clustering the pre-collected full operating condition sample dataset. The first current determination module 203 is used to determine the initial control current based on the real-time engine speed and the target real-time operating conditions. The second current determination module 204 is used to perform dynamic closed-loop correction operation on the initial control current to obtain the target control current; The power matching control module 205 is used to control the proportional valve controller of the construction machinery according to the target control current, so as to perform power matching on the construction machinery within the current control time period.

[0091] In this embodiment of the invention, after acquiring the real-time engine speed and current operating parameters, real-time operating condition identification is performed based on clustered full-condition sample data. Then, an initial control current is determined based on the real-time engine speed and real-time operating conditions. The initial control current is then dynamically closed-loop corrected to obtain the target control current. Finally, the proportional valve controller of the construction machinery is controlled according to the target control current to achieve power distribution. By constructing a database covering all operating conditions, the system can comprehensively adapt to multiple operating condition changes during power matching, ensuring the accuracy of power matching and fully utilizing engine power. Furthermore, the accuracy of power matching is further improved by adjusting the control current through dynamic closed-loop correction.

[0092] It should be noted that, in this embodiment of the invention, a full-condition simulation test bench covering oil temperature (0~80℃), engine speed (1000~2000 r / min), and load pressure (0~35 MPa) is constructed. Dual IQ (current-flow) curve data during the "rise + fall" process are collected and converted into TP (torque-pressure) curves related to engine power, thus building a full-condition database of nearly 200,000 sets of operating condition-related data. The specific process of constructing the full-condition database includes the following: Step 1: Set up the working condition simulation test bench A test bench for actual excavator application was constructed. The bench included a proportional valve test fixture, an oil temperature control module (adjustable range 0℃~80℃), a speed regulation module (matching the typical speed range of the excavator engine), a load pressure loading module (outputting pressure from 0 to 35MPa), a flow sensor, a current sensor, a pressure sensor, and a data acquisition unit. The servo proportional valve of the excavator to be tested was installed on the test fixture and connected to the oil temperature control module and the load pressure loading module through pipelines. The sensors collected the input current I, output flow rate Q, and load pressure P of the proportional valve, respectively.

[0093] Load preset programs (torque calculation, engine characteristic curve, FCM / MPC algorithm parameters (cluster number k=6, fuzzy coefficient m=2, etc.) into the calculation module, addressing module, and FC-MPC algorithm operation module, and set the data acquisition cycle to 5ms.

[0094] Step 2: Constructing an IQ curve database under multiple operating conditions Set oil temperature gradient: Divide the oil temperature into 17 levels with increments of 5℃, namely 0℃, 5℃, 10℃, 15℃...25℃, 50℃, 80℃ (covering the common oil temperature range for excavator operation), and maintain the stable oil temperature on the test bench through the oil temperature control module.

[0095] Set the speed gear: Start at 1000 RPM of the excavator engine and increase in 50 RPM increments (e.g., 1200 RPM, 1800 RPM, 2000 RPM) to set the output speed of the speed adjustment module. There are a total of 21 gears to simulate the engine speed under different working intensities.

[0096] Set the load pressure point: In 2.5MPa increments, set the output pressure of the load pressure loading module to 16 levels: 0MPa, 2.5MPa, 5MPa, 7.5MPa, 10MPa...32.5MPa, 35MPa (covering the excavator load pressure range).

[0097] Under each "oil temperature-speed-load pressure" combination, the input current of the proportional valve is first increased from 0A to 750mA (step size 50mA), and the flow rate Q corresponding to each current is recorded to obtain 16 IQ curves for the rising process; then it is reduced from 750mA to 0mA (same step size), and the data is recorded to obtain 16 IQ curves for the falling process (compensating for the hysteresis characteristics of the proportional valve).

[0098] Please see Figure 2 , Figure 2 This is a schematic diagram of current-flow curves tested at a fixed rotation speed, different temperatures, and different currents according to an embodiment of the present invention.

[0099] Each operating condition combination was collected 5 times, and the average flow rate was taken as the final IQ data for that operating condition to avoid random errors. All operating condition data were integrated to form 17 (oil temperature) × 21 (speed) × 16 (pressure) × 32 (current) = 182,784 sets of basic data, which were then integrated into a large model database.

[0100] Step 3: Calculate TP curve data Based on the power balance relationship of the hydraulic system, the IQ curve data is converted into a TP (torque-pressure) curve, establishing a correlation with engine power. The core formula is as follows:

[0101] Where: P is the load pressure (i.e., the load pressure set in step 2, unit: MPa); Q is the flow rate collected in step 2 (unit: 1 L / min); n is the rotational speed set in step 2 (unit: 1 r / min); T is the proportional valve output torque (unit: N). m).

[0102] Please see Figure 3 , Figure 3 This is a schematic diagram of torque-pressure curves calculated under different temperatures and currents, as disclosed in one embodiment of the present invention.

[0103] Using Equation (1), the (I,Q) data in the "rising / falling IQ curve" under each working condition is converted into (P,T) data, and the TP curve at the current rising time and the TP curve at the current falling time are obtained, which are stored in the large model database together with the IQ curve.

[0104] Step 4: Perform working condition clustering using the FCM (Fuzzy C-means) algorithm. The core of FCM clustering is to classify samples into multiple typical categories based on operating condition characteristics (the number of operating condition categories can be set according to actual needs; in this embodiment, it is set to 6 categories), achieving rapid real-time location of operating conditions and avoiding full data traversal. Specifically, it selects the cluster with the highest membership degree. For a single sample's membership degree row vector, it finds the element with the largest value; the corresponding column index is the cluster to which the sample belongs. The specific process is as follows: Selecting "oil temperature T (°C), speed n (r / min), and load pressure P (MPa)" as clustering features (three-dimensional vectors), and using Min-Max normalization to eliminate dimensional interference, the data is mapped to the [0,1] interval:

[0105] For T: x_min=0, x_max=80; if T_i=25℃, then T'_i=(25-0) / (80-0)=0.3125; for n: x_min=1000, x_max=2000; if n_i=1800r / min, then n'_i=(1800-1000) / (2000-1000)=0.8; for P: x_min=0, x_max=35; if P_i=20MPa, then P'_i=(20-0) / 35≈0.5714. The final preprocessed sample is x'_i=[T'_i,n'_i,P'_i]. Subsequent calculations use normalized data, and after calculation, the actual physical quantities can be restored by inverse normalization.

[0106] The cluster number is set to k=6 (corresponding to 6 typical working conditions), the fuzzy coefficient is set to m=2, and the termination error is set to ε=10. -5 Maximum number of iterations N_max = 50; Randomly generate initial membership matrix U (0) ;U (0) It is a k×N matrix (6 rows × 17) 5 (column), each element u_ij (0) The mathematical foundation of the FCM algorithm must be met: Condition 1, Value Constraint, 0 ≤ u_ij(0) ≤1 (Membership degree is a probability value, which cannot be negative or exceed 1. For example, u_ij=1 means that sample i completely belongs to class j, and u_ij=0 means that it does not belong to class j at all); Condition 2, row sum constraint, for each sample i, ∑u_ij (0) =1 (The sample must belong to a certain type of working condition to ensure that the sample does not "belong to multiple types of working conditions at the same time and the weight exceeds the limit").

[0107] Combined with the excavator working condition clustering settings (6 types of working conditions, N≥17) 5 (Group of samples), the membership matrix U is defined as an "N rows × 6 columns" two-dimensional matrix, in the following format: Sample 1 [u 11 ,u 12 ,u 13 ,u 14 ,u 15 ,u 16 ] Sample 2[u 21 ,u 22 ,u 23 ,u 24 ,u 25 ,u 26 ] ...... Sample N[u_N1,u_N2,u_N3,u_N4,u_N5,u_N6] Here, the core element u_ij represents "the membership degree of the i-th working condition sample to the j-th working condition", for example, u 12 =0.85 means that "sample 1 has an 85% tendency to belong to the medium load and normal temperature condition". The condition category (columns: j=1~6) is as follows: the first column corresponds to the light load and low temperature condition, the second column corresponds to the medium load and normal temperature condition, the third column corresponds to the heavy load and high temperature condition, the fourth column corresponds to the idling and standby condition, the fifth column corresponds to the strain transition condition, and the sixth column corresponds to the low temperature and heavy load condition.

[0108] Based on the initially defined six typical working conditions of excavators (such as light load and low temperature, heavy load and high temperature, etc.), the samples are preliminarily classified according to experience, and the sample u_ij is classified into the j-th category. (0) We set 0.9, and for the other classes we set 0.1 / 5 = 0.02, to improve the convergence speed of subsequent iterations.

[0109] The initial cluster centers c_j are calculated using the following formula. (0) (j=1~6): Equation (3) Among them, c_j (0) The initial cluster center of the j-th class (three-dimensional vector: [T_cj, n_cj, P_cj]) is to be calculated, corresponding to the initial core points of 6 typical working conditions; u_ij(0) Let ∑u_ij be the initial membership degree of the i-th sample belonging to the j-th class, satisfying ∑u_ij (0) =1 (the sum of the membership degrees of each sample is 1), m is the fuzzy coefficient (controlling the degree of clustering fuzziness); x_i represents the feature vector of the i-th full-condition sample; N is the total number of full-condition samples, and in this embodiment of the invention, N≥17 is taken. 5 Group (covering 0℃~80℃, 1000~2000r / min, 0~35MPa); j=1~6 (corresponding to the 6 typical working conditions of the present invention embodiments).

[0110] Based on the current cluster center c (t) Update the membership matrix U according to the following formula. (t+ ¹ ) : Equation (4) Use the current cluster center c_j (t) The matrix is ​​updated to U using the membership formula. (t+ ¹ ) Then use the new matrix U (t+ ¹ ) Calculate the new cluster centers c_j (t+ ¹ ) This refers to "reassigning membership degrees based on the distance between samples and cluster centers." The specific calculation method and steps are as follows: Given input: the cluster centers c_j of the current iteration (t) (j=1~6, such as j=2, the class center is (25℃, 1800r / min, 15MPa), which needs to be normalized first), normalized sample x'_i (200,000 groups, such as x'_i=[0.3125,0.8,0.5714]), fuzzy coefficient m=2 (fixed parameter).

[0111] Core objective: For each sample i, calculate its new membership degree u_ij belonging to the 6 working conditions. (t+ ¹ ) This forms a new N×6 matrix U. (t+ ¹ ) .

[0112] Calculate the Euclidean distance from the sample to each cluster center: For a single normalized sample x'_i (e.g., x'_i = [0.3125, 0.8, 0.5714] corresponding to "25℃, 1800r / min, 20MPa"), calculate its distance to the 6 cluster centers c_j. (t) Euclidean distance ||x'_i-c_j (t) The Euclidean distance formula (for three-dimensional eigenvectors) is as follows: ||x'_i-c_j (t) ||=

[0113] Calculate the class center c_2 from x'_i to j=2. (t) The distance = [0.3125, 0.8, 0.4286] (normalized to 0.3125 at 25℃, 0.8 at 1800 r / min, and 0.4286 at 15 MPa): ||x'_i-c_2 (t) || =

[0114] ≈0.1428; Similarly, the distances to classes j=1~6 are calculated, resulting in the distance set: {0.35, 0.1428, 0.52, 0.41, 0.18, 0.32}.

[0115] The core ratio term in formula (4) is: for each cluster j, calculate "2 / (m-1) power of "distance from sample to center of cluster j / distance from sample to center of cluster l", where l traverses clusters 1 to 6 (when m=2, 2 / (m-1)=2). Ratio term formula: (‖x'_i-c_j (t) || / ||x'_i-c_l (t) Example: Taking class j=2 and class l=5 as an example, ||x'_i-c_2 (t) ||=0.1428, ||x'_i-c_5 (t) If ||=0.18, then the ratio term = (0.1428 / 0.18)²≈0.63.

[0116] Calculate the summation term in the denominator: Summing the ratio terms of the above categories l=1~6, we obtain the denominator of equation (4): ∑ l =1 6 (‖x'_i-c_j (t) || / ||x'_i-c_l (t) Example: For class j=2, the denominator = (0.1428 / 0.35)² + (0.1428 / 0.1428)² + (0.1428 / 0.52)² + (0.1428 / 0.41)² + (0.1428 / 0.18)² + (0.1428 / 0.32)² ≈ 0.16 + 1 + 0.075 + 0.12 + 0.63 + 0.20 ≈ 2.185.

[0117] Calculate the membership degree u_ij (t+ ¹ ) According to formula (4), u_ij (t+ ¹ ) =1 / the sum of the terms in the denominator, and we get the new membership degree of sample i belonging to class j. Example: u_i2 for class j=2 (t+ ¹) =1 / 2.185≈0.457, meaning that 45.7% of this sample tends to belong to the medium-load, normal-temperature operating condition; repeat steps 1-4 to calculate the membership degree of this sample to the other 5 categories (e.g., u_i5). (t+ ¹ ) (≈0.32), ultimately ensuring that the sum of the 6 memberships of the sample is 1 (satisfying the row sum constraint).

[0118] Batch calculation and matrix generation: Perform the above four steps for each of the 200,000 samples to obtain 6 membership values ​​for each sample. Combine these values ​​in the format of "sample row × working condition column" to generate a new membership matrix U. (t+ ¹ ) Matrix features: Each row corresponds to one sample, each column corresponds to one type of working condition, and the element u_ij (t+ ¹ ) ∈[0,1], the sum of the elements in each row is 1.

[0119] If sample x'_i is clustered with a certain cluster center c_j (t) If they completely overlap (distance is 0), then the membership degree u_ij of that class is... (t+ ¹ ) =1, all other classes are 0 (samples completely belong to this class); after calculation, rows and constraints need to be verified. If the deviation exceeds 10... -4 Normalization correction is required to ensure that the mathematical requirements of the FCM algorithm are met; this calculation needs to be performed alternately with "cluster center update" (using U). (t+ ¹ ) Substitute into equation (3) to calculate c_j (t+ ¹ ) ), until the change in cluster centers Δc < ε (10 -5 The iteration terminates at this point.

[0120] Calculate the change in cluster centers Δc = max‖c_j (t +¹ ) -c_j (t) If Δc < ε or N_max is reached, the iteration stops. At this point, U is the "final membership matrix," which accurately reflects the working condition affiliation of all samples. The final cluster centers and membership matrix are output. After convergence, the final cluster centers for the six working conditions are shown in the table below: Table 1: Cluster Centers and Preloading Parameters for 6 Typical Working Conditions

[0121] The specific values ​​of "(T,n,P)" in the table are the final cluster centers of the 6 types of working conditions. These values ​​are the "core features of the same type of working conditions" mined by the algorithm from massive amounts of data. The MPC parameters (Np, Nc, ω1, ω2) and outer loop PID parameters (Kp1, Ki1, Kd1) corresponding to each type of working condition are based on the "calculated cluster centers" and calibrated in conjunction with engineering experiments.

[0122] In one optional embodiment, the full-condition database includes multiple standard clustered conditions; The real-time operating condition confirmation module 202 performs operating condition identification and analysis on the current operating condition parameters based on a preset full operating condition database, and obtains the target real-time operating condition in the following ways: For each standard clustering condition, calculate the membership degree between the current condition parameter and the standard clustering condition to obtain the current membership degree corresponding to the standard clustering condition; The maximum value of the current membership degree corresponding to all standard clustering conditions is filtered to obtain the target clustering condition corresponding to the maximum current membership degree, and the target clustering condition is confirmed as the target real-time condition.

[0123] In this optional embodiment, the standard clustering condition is the 6 clustering conditions in Table 1. Collect the current condition parameters x0=(T0,n0,P0), substitute them into equation (4) to calculate its membership degree u_j0 (j=1~6) to the 6 clusters, and select the cluster with the largest membership degree j*=argmax(u_j0) (e.g., when x0=(25,1800,20), j*=2).

[0124] In another optional embodiment, the first current determination module 203 determines the initial control current based on the real-time engine speed and the target real-time operating condition in the following specific ways: Obtain the speed and torque curve data corresponding to the engine of the construction machinery; The target maximum torque is obtained by matching the real-time engine speed with the speed-torque curve data. The initial control current is determined based on the target maximum torque and the target real-time operating conditions.

[0125] In this optional embodiment, within the current control cycle, the real-time speed n0 set by the bench speed adjustment module (i.e., the actual engine speed when the excavator is working) is obtained, and the engine characteristic database (which pre-stores the "speed-maximum torque" curve of the excavator engine of this model) is called to determine the maximum torque T_max that the engine can provide at the speed n0.

[0126] Let the discrete points of the curve be (n) l , T l ), (n l+1 , T l+1 If nl ≤n0≤n l+1 ,but: Equation (5) In yet another optional embodiment, the first current determination module 203 determines the initial control current based on the target maximum torque and the target real-time operating conditions in the following specific ways: Acquire pre-stored torque-pressure curve data that corresponds to the target real-time operating conditions; Match the target maximum torque with the torque-pressure curve data to obtain the target load pressure corresponding to the target maximum torque; Acquire pre-stored current-flow curve data that corresponds to the target real-time operating conditions; Using the target load pressure as an index, a reverse lookup process is performed on the current-flow curve data to obtain the initial control current corresponding to the target load pressure.

[0127] In this optional embodiment, the target load pressure P_target is first determined: in the "rising / falling TP curve" corresponding to class j, the P_target that matches T_max (e.g., T_max=120N) is found. When m, P_target = 28MPa). Then, address the initial control current: using P_target as the index, look up the "rising / falling IQ curve" of class j in reverse to obtain the rising initial current I_up0 and the falling initial current I_down0 (e.g., when j* = 2 classes, P_target = 28MPa, I_up0 = 345mA, I_down0 = 330mA).

[0128] In another optional embodiment, the second current determination module 204 performs a dynamic closed-loop correction operation on the initial control current to obtain the target control current in the following ways: A dynamic optimization correction operation is performed on the initial control current to obtain the first corrected current; A double-closed-loop correction operation is performed on the first correction current to obtain the target control current.

[0129] In this optional embodiment, the initial current is precisely and dynamically optimized and corrected through MPC (Model Predictive Control) to solve the control deviation caused by "hydraulic system lag and operating condition fluctuation" in traditional technology.

[0130] In yet another optional embodiment, the second current determination module 204 performs a dynamic optimization correction operation on the initial control current to obtain the first corrected current in the following specific ways: The target reference flow rate is calculated based on the target load pressure and the target maximum torque; the target reference flow rate is used to characterize the engine power requirements of the construction machinery. The initial control current is predicted and optimized based on the target reference flow rate to obtain the target current sequence, and the first sequence of the target current sequence is confirmed as the target predicted current. Obtain hysteresis characteristic data of proportional valves in construction machinery; The target predicted current is compensated and corrected based on the proportional valve hysteresis characteristic data to obtain the first corrected current.

[0131] In this optional embodiment, the dynamic optimization correction operation is performed based on a pre-built dynamic optimization correction model. The process of building the dynamic optimization model is as follows: Construct a dynamic "current-flow" model for a proportional valve to predict future flow rate changes (tentatively 10 steps, 15ms each, total 150ms). Static IQ model (class j fitting): Equation (6) Where a_j, b_j, and c_j are the pre-stored fitting coefficients for the target cluster j*=2 (medium load, normal temperature). (For example, if j=2 clusters, a_j=0.12, b_j=2×10^2.) -4 c_j=5; I(k) unit: mA; Q_static(k) unit: L / min).

[0132] The pre-stored fitting coefficients reflect the inherent characteristics of the proportional valve under this operating condition: for every 1mA increase in current I, the static flow rate Q_static will contribute 0.12L / min due to a_j, and a small increment due to b_j (quadratic term) (adapting to the slight nonlinearity of the proportional valve), and c_j is the basic leakage flow rate at zero current; specific operating condition: if it is changed to j=3 (heavy load and high temperature), a_j, b_j, and c_j will change (the oil viscosity decreases at high temperature, the flow rate is greater for the same current, and a_j will increase).

[0133] This invention creatively establishes a dynamic prediction model (first-order inertial discretization) of the "current-flow" dynamic model of a proportional valve, accurately predicting the flow rate in the future k=1 to 10 steps (e.g., 15ms per step, totaling 150ms). The dynamic model (first-order inertial discretization, compensating for hysteresis) is as follows: Equation (7) Where α = 1 - e^(-T_s / τ), T_s = 15 ms, τ = 30 ms, τ = 30 ms is the lag time constant, α ≈ 0.393; Q(k) is the predicted flow rate at the k-th step.

[0134] Calculation of reference flow Q_ref: Substituting T_max, P_target, and n0 into equation (1) and transforming, we get:

[0135] Where Q_ref is in units of L / min; e.g., T_max = 120N When m, P_target=28MPa, n0=1800r / min, Q_ref=35L / min.

[0136] MPC objective function construction and optimization (the optimization aims to "ensure that the flow rate tracks the target value while preventing excessive current fluctuations"), objective function (minimizing flow deviation and current fluctuations), balancing control accuracy and hardware protection: Equation (9) (ω1 and ω2 are the preload weights for class j. If j = class 2, ω1 = 0.7 and ω2 = 0.3. ω1 determines whether the engine power can be accurately matched, and ω2 determines whether the proportional valve can work stably. Np and Nc are the preload prediction / control step size.) Constraints: Current constraint: 100mA≤I(k)≤750mA; Current change rate constraint: |I(k+1)-I(k)|≤50mA / step; Flow constraint: Q(k)≤Q_max(n0,P_target) (Q_max is the maximum flow rate of the main pump, obtained from a table); Optimization Solution: The quadratic programming problem is solved using the interior-point method to obtain the optimal current sequence I*(1)~I*(Nc). The first step is to predict the target currents I_pred_up=I*(1) and I_pred_down=β. I*(1) (β=0.95 is the compensation coefficient for the descent process, such as I_pred_up=350mA, I_pred_down=332.5mA). The specific solution process is as follows: Initial feasible solution setting: The initial current I_up0=345mA obtained by FCM addressing is used as the initial value I(0)=345mA. Combined with the current change rate constraint, the initial current sequence [345mA,370mA,390mA] is initially generated (each step increment ≤50mA).

[0137] Introducing a barrier function to transform an unconstrained problem: To handle inequality constraints, a logarithmic barrier function is added to the objective function J, integrating the constraints into the optimization objective. J'=J-μ·[ln(I(k)-100)+ln(750-I(k))+ln(50-(I(k+1)-I(k)))+ln(Q_max-Q(k))] Where μ is the barrier parameter (initially μ=1, gradually decreasing to 10 during iterations). -4 This ensures that the current and flow rate always meet the constraints during the iteration process.

[0138] Gradient descent iterative optimization: ① Take the partial derivatives of J' with respect to the current sequences I(1), I(2), and I(3) to obtain the gradient vector. J'; ② Update the current sequence using Newton's method: I_new = I_old - α·(H - ¹· J'), where H is the Hessian matrix (second derivative matrix), and α=0.1 is the step size factor; ③ Repeat the iteration until the gradient norm is reached. J'‖<10 - ³, at this point, the optimal current sequence is obtained; 4. Solution results for j*=2 working conditions: After 3 iterations, the optimal current sequence is [350mA, 375mA, 395mA], which satisfies all constraints.

[0139] MPC employs a "rolling time-domain control" strategy, executing only the first current step of the optimal sequence while compensating for the hysteresis characteristics of the proportional valve. Current output during the rising process: Take the first step of the optimal sequence, I*(1)=350mA, as the predicted target current for the rising process, I_pred_up=350mA; Current compensation during the descent process: Considering the hysteresis characteristics of the proportional valve current during descent (under the same flow rate, the descent current is 5%~8% lower than the rise current), a compensation coefficient β=0.95 (j*=measured value under type 2 operating conditions) is introduced to calculate the target descent current: I_pred_down=β·I*(1)=0.95×350=332.5mA Output verification: Before the final output, verify whether the current meets the constraints (350mA and 333mA are both within the range of 100~750mA). After ensuring that there is no risk of exceeding the limit, the current is transmitted to the dual closed-loop PID module for further correction.

[0140] Key calculation notes: The interior point method solution process can be implemented through the hardware acceleration module of the embedded DSP. The solution time plus the FCM addressing time under the j*=2 working condition meets the real-time requirements.

[0141] MPC dynamic optimization is the core of the FC-MPC algorithm—it transforms the "static initial current" obtained by FCM clustering into "dynamically optimized current," solving the core pain point of excavators' "rapid fluctuations in operating conditions and large hydraulic lag," and minimizing power matching error.

[0142] In yet another optional embodiment, the second current determination module 204 performs a double closed-loop correction operation on the first correction current to obtain the target control current in the following specific ways: The target static current is obtained by calculating the static current based on the target reference flow rate. The difference between the first corrected current and the target static current is calculated to obtain the first deviation current; The first deviation current is corrected by outer loop adjustment to obtain the second corrected current; Obtain the target actual current obtained by collecting current data from the proportional valve controller of the construction machinery at a previous moment; The difference between the second corrected current and the target actual current is calculated to obtain the second deviation current; The second deviation current is corrected by inner-loop regulation to obtain the target control current.

[0143] In this optional embodiment, the specific process of adjusting and correcting the outer loop (target current correction loop) includes: Traditional static current I_static calculation: By solving equation (6) in reverse order of Q_ref, we get: Equation (10) Outer-loop PID control: Input deviation e_outer = I_pred - I_static, output intermediate target current I_mid:

[0144] Equation (11) Where Kp1, Ki1, and Kd1 are preloaded parameters for class j. For example, if j = 2 classes, Kp1 = 0.8, Ki1 = 0.05, and Kd1 = 0.1; when e_outer = 5mA, I_mid = 352mA.

[0145] The specific process for adjusting and correcting the inner loop (current output execution loop) is as follows: Inner-loop PID control: Input deviation e_inner = I_mid - I_act (I_act is the actual current collected by the sensor, such as 348mA), output final target current I_final:

[0146] Equation (12) With fixed parameters Kp2=1.2, Ki2=0.1, Kd2=0.05, and e_inner=4mA, I_final_up=353mA.

[0147] For the outer loop (target current correction loop): In this embodiment of the invention, the target current calculated by the "TP curve + engine torque" model is used as the command value, and the original calculated current under traditional static conditions (such as the theoretical current value under fixed conditions) is used as the feedback value. The "final target current" is output through PID regulation to ensure that the target current is dynamically matched with the real-time operating conditions (oil temperature, speed, load).

[0148] For the inner loop (current output execution loop): the “final target current” output from the outer loop is used as the command value, and the actual output current of the solenoid valve (acquired in real time by the vehicle controller) is used as the feedback value. The output of the drive module is quickly adjusted by PID to ensure that the actual current accurately tracks the command value and eliminates the output deviation caused by solenoid valve hysteresis, line loss, etc.

[0149] By using dual closed-loop PID control, this invention not only solves the fundamental problems of "incomplete working condition coverage and inaccurate current addressing", but also eliminates the two core error sources of "target mismatch" and "execution deviation" through hierarchical control, further enhancing the accuracy and stability of proportional valve hysteresis control, and making it more suitable for the complex and ever-changing operating scenarios of excavators.

[0150] Example 3 Please see Figure 5 , Figure 5 This is a schematic diagram of another power matching control system for engineering machinery disclosed in an embodiment of the present invention. Figure 5 The system shown can be used to perform the method described in Embodiment 1. Figure 5 As shown, the power matching control system for engineering machinery disclosed in this embodiment of the invention includes, but is not limited to: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute some or all of the steps in the power matching control method for engineering machinery described in Embodiment 1 of the present invention.

[0151] Example 4 This invention discloses a computer storage medium storing computer instructions. When the computer instructions are invoked by a processor, they are used to execute some or all of the steps in the power matching control method for engineering machinery described in Embodiment 1 of this invention.

[0152] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules. They may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0153] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0154] Finally, it should be noted that the technical content disclosed in the embodiments of the present invention is only a preferred embodiment of the present invention and is only used to illustrate the technical solutions of the present invention, and not to limit it. Although the present invention 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 the present invention.

Claims

1. A power matching control method applied to engineering machinery, characterized in that, The method includes: Obtain the real-time engine speed of the construction machinery in the current control time period; The current operating condition parameters are obtained, and the operating condition identification and analysis are performed on the current operating condition parameters according to the preset full operating condition database to obtain the target real-time operating condition; the full operating condition database is obtained by clustering a pre-collected full operating condition sample dataset. The initial control current is determined based on the real-time engine speed and the target real-time operating condition. A dynamic closed-loop correction operation is performed on the initial control current to obtain the target control current; The proportional valve controller of the construction machinery is controlled according to the target control current to perform power matching on the construction machinery within the current control time period.

2. The power matching control method for engineering machinery according to claim 1, characterized in that, The full-condition database includes multiple standard clustered conditions; The step of performing operating condition identification and analysis on the current operating condition parameters based on a preset full-operating condition database to obtain the target real-time operating condition includes: For each of the standard clustering conditions, calculate the membership degree between the current condition parameter and the standard clustering condition to obtain the current membership degree corresponding to the standard clustering condition; The maximum value is filtered for the current membership degree corresponding to all the standard clustering conditions to obtain the target clustering condition corresponding to the maximum current membership degree, and the target clustering condition is confirmed as the target real-time condition.

3. The power matching control method for engineering machinery according to claim 1, characterized in that, The step of determining the initial control current based on the real-time engine speed and the target real-time operating condition includes: Obtain the speed and torque curve data corresponding to the engine of the construction machinery; The real-time engine speed is matched based on the speed-torque curve data to obtain the target maximum torque corresponding to the real-time engine speed; The initial control current is determined based on the target maximum torque and the target real-time operating conditions.

4. The power matching control method for engineering machinery according to claim 3, characterized in that, The step of determining the initial control current based on the target maximum torque and the target real-time operating conditions includes: Obtain pre-stored torque-pressure curve data corresponding to the target real-time operating condition; The target maximum torque is matched based on the torque-pressure curve data to obtain the target load pressure corresponding to the target maximum torque; Obtain pre-stored current-flow curve data corresponding to the target's real-time operating conditions; Using the target load pressure as an index, the current flow curve data is reverse-queried to obtain the initial control current corresponding to the target load pressure.

5. The power matching control method for engineering machinery according to claim 4, characterized in that, The step of performing a dynamic closed-loop correction operation on the initial control current to obtain the target control current includes: A dynamic optimization correction operation is performed on the initial control current to obtain a first corrected current; A double-closed-loop correction operation is performed on the first correction current to obtain the target control current.

6. The power matching control method for engineering machinery according to claim 5, characterized in that, The step of performing a dynamic optimization correction operation on the initial control current to obtain a first corrected current includes: The target reference flow rate is calculated based on the target load pressure and the target maximum torque; the target reference flow rate is used to characterize the engine power requirement of the construction machinery. The initial control current is predicted and optimized based on the target reference flow rate to obtain a target current sequence, and the first sequence of the target current sequence is confirmed as the target predicted current. Obtain the proportional valve hysteresis characteristic data of the engineering machinery; The target predicted current is compensated and corrected based on the proportional valve hysteresis characteristic data to obtain the first corrected current.

7. The power matching control method for engineering machinery according to claim 6, characterized in that, The step of performing a double closed-loop correction operation on the first corrected current to obtain the target control current includes: The target static current is obtained by calculating the static current based on the target reference flow rate. The difference between the first corrected current and the target static current is calculated to obtain the first deviation current; The first deviation current is adjusted and corrected by an outer loop to obtain the second corrected current; The target actual current is obtained by acquiring the current of the proportional valve controller of the engineering machinery at a prior time. The difference between the second corrected current and the target actual current is calculated to obtain the second deviation current; The second deviation current is corrected by inner-loop regulation to obtain the target control current.

8. A power matching control system for engineering machinery, characterized in that, The system includes: The real-time speed acquisition module is used to acquire the real-time engine speed of the construction machinery in the current control time period. The real-time operating condition confirmation module is used to acquire the current operating condition parameters and perform operating condition identification and analysis on the current operating condition parameters according to the preset full operating condition database to obtain the target real-time operating condition; the full operating condition database is obtained by clustering a pre-collected full operating condition sample dataset. The first current determination module is used to determine the initial control current based on the real-time engine speed and the target real-time operating condition. The second current determination module is used to perform a dynamic closed-loop correction operation on the initial control current to obtain the target control current; The power matching control module is used to control the proportional valve controller of the construction machinery to operate according to the target control current, so as to perform power matching on the construction machinery within the current control time period.

9. A power matching control system for engineering machinery, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the power matching control method for engineering machinery as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked by the processor, are used to execute the power matching control method for engineering machinery as described in any one of claims 1 to 7.

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