A multi-element dynamic support device cooperative control method

Abstract

The application discloses a kind of multi-element dynamic support equipment collaborative control methods, it is related to support equipment collaborative control technical field, including obtaining the state data of equipment itself, and the posture data of supported object is synchronously collected, state data and posture data are fused, system state snapshot is generated;Through the priority calculation unit based on rule base, each support equipment is assigned immediate task, and the preliminary action instruction set is formed, according to the preset collaborative action protocol, the timing and amplitude adjustment of preliminary action instruction is carried out, and the final coordinated action instruction is generated and is issued to each support equipment execution;Collect new round system state snapshot and compare with expected target, according to the comparison result, the rule weight in the priority calculation unit based on rule base is updated.The application can be reasonably distributed according to system state and realize overall coordination, improve the stability and consistency of support process;Long-term running performance of multi-support equipment collaborative control is improved.

Description

Technical Field

[0001] This invention relates to the field of collaborative control technology for support equipment, and in particular to a collaborative control method for multi-element dynamic support equipment. Background Technology

[0002] With the development of high-end equipment manufacturing, intelligent structures, and automated control technologies, the application scenarios of multi-support equipment working collaboratively are increasing. Typical applications include large workpiece assembly and adjustment, complex structure posture maintenance, precision platform support, and flexible support systems. Related technologies typically provide multi-point support and posture adjustment capabilities to the supported object through hydraulic, electric, or mechatronic support devices, and are gradually introducing sensor networks and control systems to achieve perception and control of the support status. In recent years, with the improvement of sensor accuracy and the development of control algorithms, support control methods based on multi-source information perception have emerged. Systems are beginning to focus on the coupling relationship between the operating status of the support equipment and the overall posture of the supported object to improve support stability and control accuracy.

[0003] However, existing multi-support device control technologies generally suffer from insufficient coordination and limited adaptability. On the one hand, some technologies only control a single support device independently, lacking unified modeling and coordinated scheduling of the overall state of multiple support devices, which can easily lead to support conflicts or attitude fluctuations under complex operating conditions. On the other hand, even with the introduction of centralized control, existing methods mostly rely on fixed control strategies or static parameter configurations, making it difficult to dynamically adjust based on system performance, resulting in insufficient adaptability to environmental and load changes. Furthermore, existing technologies have a shallow level of fusion between support device state data and the attitude data of the supported objects, failing to fully explore the decision-making value of multi-source state information in task assignment and coordinated control, thus hindering further improvements in overall system stability and control efficiency. Summary of the Invention

[0004] In view of the problems existing in the collaborative control method of multiple dynamic support devices, this invention is proposed. Therefore, the problem to be solved by this invention is how to provide a collaborative control method for multiple dynamic support devices.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a collaborative control method for multiple dynamic support devices, which includes: acquiring the status data of the device itself through the sensors built into each support device, and synchronously collecting the attitude data of the supported object, fusing the status data and attitude data to generate a system status snapshot;

[0007] Based on the system status snapshot, an immediate task is assigned to each support device through a priority calculation unit based on a rule base, forming a preliminary action instruction set. The preliminary action instruction set is input to the central coordination controller, which adjusts the timing and magnitude of the preliminary action instructions according to the preset coordination action protocol, generates the final coordinated action instructions, and sends them to each support device for execution.

[0008] After each supporting device executes the final coordinated action command, it collects a new round of system status snapshots and compares them with the expected target. Based on the comparison results, it updates the rule weights in the priority calculation unit based on the rule base.

[0009] As a preferred embodiment of the collaborative control method for multi-element dynamic support equipment described in this invention, the generation of system state snapshots includes:

[0010] The sensors built into each support device are uniformly initialized and their working status is verified. Status data acquisition is started. The status data includes the current output displacement, displacement change rate, output thrust, and working temperature of the drive unit of the support device. A time stamp is added to each data point.

[0011] Based on the same time reference, the attitude feature data of the supported object are collected synchronously through the attitude sensing device.

[0012] The state data and attitude data are preprocessed and aligned using time markers to form the original dataset;

[0013] The state data and attitude data within the same time slice are fused to generate a system state snapshot that includes real-time state information of each supporting device and the overall attitude characteristics of the supported object.

[0014] As a preferred embodiment of the collaborative control method for multi-element dynamic support equipment described in this invention, the step of forming a preliminary action instruction set includes:

[0015] The system state snapshot is mapped to a decision input vector. Based on the predefined structured condition rules in the rule base, rule matching and weighted calculation are performed on the decision input vector to calculate the task priority value for each supporting device, expressed as:

[0016] ;

[0017] in, Indicates the first The task priority value of each supporting device in the current system state; This indicates the number of rules in the rule base that participate in priority calculation; Indicates the first The rule weights corresponding to each rule; Indicates input in system status Conditions, No. Rule number 1 The rule response results given by each supporting device;

[0018] Based on the calculated task priority value, the immediate task content of each supporting device is determined and converted into preliminary action instructions to form a preliminary action instruction set;

[0019] The rules in the rule base include state triggering conditions, objects of action, and corresponding response results; immediate tasks include extension tasks with specified displacement increments, retraction tasks with specified displacement decrements, and hold tasks that maintain the current state.

[0020] As a preferred embodiment of the collaborative control method for multi-element dynamic support equipment described in this invention, the generation of the final coordinated action command includes:

[0021] All preliminary action commands are centrally analyzed within the same control cycle to identify the coupling relationship between the actions of different support equipment in terms of time and effect;

[0022] Based on the preset cooperative action protocol, a rule-based heuristic coordination algorithm is used to reorder the execution order of action instructions and modify the action amplitude while satisfying the system stability constraints.

[0023] Generate the final coordinated action instructions and distribute them to each supporting device according to the device identifier.

[0024] As a preferred embodiment of the collaborative control method for multi-element dynamic support equipment described in this invention, the step of collecting a new round of system state snapshots and comparing them with the expected target includes:

[0025] The system acquires the operating status of each supporting device after its actions are executed and the overall posture of the supported object, and generates a new round of system status snapshots.

[0026] The deviation metric between the current system state snapshot's state feature vector and the target state feature vector corresponding to the expected target is calculated using the following formula:

[0027] ;

[0028] in, This represents the overall deviation of the system's current state from the expected target. This represents the state feature vector corresponding to the current system state snapshot; This represents the target state feature vector corresponding to the expected target; Represents norm operations;

[0029] The expected target is composed of the target attitude state of the supported object, the target displacement and stress range of each supporting device, and the overall stability index of the system.

[0030] As a preferred embodiment of the collaborative control method for multi-element dynamic support equipment described in this invention, the step of updating the rule weights in the priority calculation unit based on the rule base according to the comparison results includes:

[0031] After obtaining the deviation measurement results, the deviation measurement results are correlated with the triggering status of each rule in the rule base during the current control cycle. The actual impact of each rule on the system state changes is evaluated, and the rule weights are updated and adjusted. The rule weight update process is represented as follows:

[0032] ;

[0033] in, Indicates the first The rule weight of each rule in the next control cycle. Indicates the first The rule weight of each rule in the current control cycle; This represents the adjustment coefficient for updating rule weights; Indicates the first The rule in the current deviation result The following are the evaluation results of the contribution to the achievement of system goals;

[0034] The contribution evaluation result is determined based on the trend of system deviation change before and after the rule is triggered: if the system state and expected target are within the preset range after the rule is triggered, a positive value is taken; otherwise, a negative value is taken. The value is related to the magnitude of the deviation change.

[0035] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a collaborative control method for multiple dynamic support devices.

[0036] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements the steps of a collaborative control method for multiple dynamic support devices.

[0037] The beneficial effects of this invention are as follows: This invention enables the characterization of the overall state of a multi-support system, providing a reliable data foundation for collaborative decision-making; based on priority calculation and central collaborative control using a rule base, the action tasks of each support device can be reasonably allocated according to the system state and achieve overall coordination in terms of timing and amplitude, effectively avoiding local control conflicts and improving the stability and consistency of the support process; it can continuously correct control strategies, enhance the adaptability to changes in operating conditions, and improve the accuracy, reliability, and long-term operating performance of collaborative control of multi-support devices. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0039] Figure 1 This is a flowchart of a collaborative control method for multiple dynamic support devices. Detailed Implementation

[0040] To make the above-mentioned objects, features, and advantages of the present invention more readily understood, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0041] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0043] Reference Figure 1 This is the first embodiment of the present invention, which provides a collaborative control method for multiple dynamic support devices, including:

[0044] S1: Acquire the status data of the device itself through the built-in sensors of each support device, and simultaneously collect the attitude data of the supported object. Merge the status data and attitude data to generate a system status snapshot.

[0045] S2: Based on the system status snapshot, the priority calculation unit based on the rule base assigns immediate tasks to each support device to form a preliminary action instruction set. The preliminary action instruction set is input to the central coordination controller, and the timing and amplitude of the preliminary action instructions are adjusted according to the preset coordination action protocol to generate the final coordinated action instructions and send them to each support device for execution.

[0046] S3: After each supporting device executes the final coordinated action command, it collects a new round of system status snapshots and compares them with the expected target. Based on the comparison results, it updates the rule weights in the priority calculation unit based on the rule base.

[0047] Specifically, after the system enters the running state, the sensors built into each supporting device are uniformly initialized and their working status is verified to ensure that each sensor is in a stable data output state.

[0048] The support device is an electric push rod type support device, which achieves axial extension and retraction through motor drive, and is used to provide active support and attitude adjustment capability for the supported object; the status data includes the current output displacement of the push rod, the rate of displacement change, the output thrust, and the operating temperature of the drive unit.

[0049] Start collecting status data from each support device to continuously acquire status data that reflects the current operating conditions, structural response, and dynamic change trends of the support device, and add a time stamp to each status data.

[0050] While collecting status data of the supporting equipment, the attitude of the supported object is synchronously sensed according to the same time reference benchmark. The attitude characteristic data of the supported object in space is obtained through the attitude sensing device. The attitude data is used to reflect the spatial distribution status and direction of change of the supported object at the current moment.

[0051] The status data of each supporting device and the attitude data of the supported object are preprocessed to ensure that the data from different sensors are consistent in data format, units and structural hierarchy, and the data are aligned by time stamp to form the original data set.

[0052] The state data and attitude data within the same time slice are fused to generate a system state snapshot that describes the overall operation of the system within the time slice. The system state snapshot includes the real-time status information of each supporting device and the overall attitude characteristics of the supported object.

[0053] After the system status snapshot is generated, it is input into the priority calculation unit based on the rule base for joint parsing, and the parsing result is mapped into a decision input vector to characterize the comprehensive operating status of the current system within the control cycle.

[0054] The rule base consists of multiple structured conditional rules. Each rule contains the state triggering conditions, the target object, and the corresponding response result, which are used to describe the control requirements of each supporting device under a specific system state.

[0055] Example: When the output thrust of a certain support device is continuously higher than the normal operating range, and the attitude deviation of the supported object in the corresponding direction exceeds the allowable range, it is determined that the support device has an overload trend, and a higher priority extension task is assigned to the adjacent support devices to achieve force redistribution; when the overall attitude of the supported object is close to the target state, and the displacement change rate of multiple support devices is lower than the predetermined rate, a holding task is assigned to each support device.

[0056] The priority calculation unit performs rule matching and weighted calculation on the judgment input vector based on multiple predefined rules in the rule base, and calculates the corresponding task priority value for each supporting device. The task priority value is expressed as:

[0057] ;

[0058] in, Indicates the first The task priority value of each supporting device in the current system state; This indicates the number of rules in the rule base that participate in priority calculation; Indicates the first The rule weights corresponding to each rule; Indicates input in system status Conditions, No. Rule number 1 The rule response results given by each supporting device;

[0059] The initial values ​​of the rule weights are uniformly allocated based on the functional importance and structural position of the supporting equipment in the system, and normalization is used to ensure that all rules participate in priority calculation in the initial stage.

[0060] Based on the calculated task priority values, the control requirements of each supporting device are sorted, and combined with the corresponding priority results, the immediate task content that the supporting device needs to execute in the current control cycle is determined.

[0061] The immediate tasks are transformed into preliminary action instructions corresponding to the actions of the supporting equipment. The preliminary action instructions corresponding to each supporting equipment together constitute the preliminary action instruction set.

[0062] Real-time tasks are control command types that can be directly mapped to equipment actions, including extension tasks with specified displacement increments, retraction tasks with specified displacement reductions, and hold tasks that maintain the current displacement and thrust state.

[0063] After the initial set of action instructions is input into the central coordinating controller, the central coordinating controller performs centralized analysis of all action instructions within the same control cycle. By identifying the coupling relationship between the actions of different supporting equipment in terms of time and effect, it coordinates and adjusts the execution order and action range of each action instruction according to the preset coordinating action protocol, and generates the final coordinated action instruction. After the adjustment is completed, the central coordinating controller sends the final coordinated action instruction to each supporting equipment according to the equipment identifier.

[0064] A rule-based heuristic coordination algorithm is adopted to determine the conflict and cooperation relationships between preliminary action instructions, reorder the action execution order, and modify the action amplitude under the premise of satisfying system stability constraints, thereby generating the final action instructions.

[0065] After each supporting device completes the execution of the final coordinated action command, the system restarts status acquisition, re-acquires the current operating status of each supporting device and the overall posture of the supported object after the action, and generates a new round of system status snapshots.

[0066] By comparing the new system state snapshot with the pre-set expected target, and analyzing the difference between the current system state and the target state, the deviation measurement result for evaluating the control effect is obtained. The deviation measurement result is expressed as follows:

[0067] ;

[0068] in, This represents the overall deviation of the system's current state from the expected target. This represents the state feature vector corresponding to the current system state snapshot; This represents the target state feature vector corresponding to the expected target; Represents norm operations;

[0069] The expected target is composed of the target attitude state of the supported object, the target displacement and stress range of each supporting device, and the overall stability index of the system. The expected target is generated at the beginning of the mission based on the support requirements. It is achieved by calculating the difference between the current system state feature vector and the target state feature vector. The difference result is compared with the preset attitude deviation tolerance range and support error tolerance range to determine the degree of target achievement.

[0070] After obtaining the deviation measurement results, the deviation measurement results are correlated with the triggering status of each rule in the rule base during the current control cycle. The actual impact of each rule on the system state changes is evaluated, and the rule weights are updated and adjusted. The rule weight update process is represented as follows:

[0071] ;

[0072] in, Indicates the first The rule weight of each rule in the next control cycle. Indicates the first The rule weight of each rule in the current control cycle; This represents the adjustment coefficient for updating rule weights; Indicates the first The rule in the current deviation result The following are the evaluation results of the contribution to the achievement of system goals;

[0073] The adjustment coefficient is a system preset constant used to control the magnitude of weight adjustment. The contribution evaluation result is determined based on the trend of system deviation change before and after the rule is triggered. When the system state and expected target are within the preset range after the rule is triggered, the contribution evaluation result is positive; otherwise, it is negative. The value is related to the magnitude of deviation change.

[0074] After the rule weights are updated, the updated rule weights and the original rule structure are stored in the rule base as the basis for priority calculation and task assignment in subsequent control cycles, thereby continuously improving the accuracy, stability and overall control effect of collaborative control of multiple supporting devices.

[0075] This embodiment also provides a computer device applicable to a collaborative control method for multiple dynamic support devices, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement all or part of the steps of the method described in the above embodiments of the present invention.

[0076] This embodiment also provides a storage medium storing a computer program thereon. When the computer program is executed by a processor, it performs the method in any optional implementation of the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0077] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0078] In summary, this invention enables the characterization of the overall state of a multi-support system, providing a reliable data foundation for collaborative decision-making. Based on priority calculation using a rule base and central collaborative control, the actions of each support device can be rationally allocated according to the system state and coordinated in terms of timing and amplitude, effectively avoiding local control conflicts and improving the stability and consistency of the support process. Furthermore, it can continuously modify control strategies, enhancing adaptability to changes in operating conditions and improving the accuracy, reliability, and long-term operational performance of collaborative control of multiple support devices.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for coordinated control of multiple dynamic support devices, characterized in that: include, The system acquires its own status data through the built-in sensors of each support device, and simultaneously collects the attitude data of the supported object. The status data and attitude data are then fused to generate a system status snapshot. Based on the system status snapshot, an immediate task is assigned to each support device through a priority calculation unit based on a rule base, forming a preliminary action instruction set. The preliminary action instruction set is input to the central coordination controller, which adjusts the timing and magnitude of the preliminary action instructions according to the preset coordination action protocol, generates the final coordinated action instructions, and sends them to each support device for execution. After each supporting device executes the final coordinated action command, it collects a new round of system status snapshots and compares them with the expected target. Based on the comparison results, it updates the rule weights in the priority calculation unit based on the rule base.

2. The collaborative control method for multiple dynamic support devices as described in claim 1, characterized in that: The generation of the system state snapshot includes: The sensors built into each support device are uniformly initialized and their working status is verified. Status data acquisition is started. The status data includes the current output displacement, displacement change rate, output thrust, and working temperature of the drive unit of the support device. A time stamp is added to each data point. Based on the same time reference, the attitude feature data of the supported object are collected synchronously through the attitude sensing device. The state data and attitude data are preprocessed and aligned using time markers to form the original dataset; The state data and attitude data within the same time slice are fused to generate a system state snapshot that includes real-time state information of each supporting device and the overall attitude characteristics of the supported object.

3. The collaborative control method for multiple dynamic support devices as described in claim 1, characterized in that: The formation of the initial action instruction set includes: The system state snapshot is mapped to a decision input vector. Based on the predefined structured condition rules in the rule base, rule matching and weighted calculation are performed on the decision input vector to calculate the task priority value for each supporting device, expressed as: ； in, Indicates the first The task priority value of each supporting device in the current system state; This indicates the number of rules in the rule base that participate in priority calculation; Indicates the first The rule weights corresponding to each rule; Indicates input in system status Conditions, No. Rule number 1 The rule response results given by each supporting device; Based on the calculated task priority value, the immediate task content of each supporting device is determined and converted into preliminary action instructions to form a preliminary action instruction set; The rules in the rule base include state triggering conditions, objects of action, and corresponding response results; immediate tasks include extension tasks with specified displacement increments, retraction tasks with specified displacement decrements, and hold tasks that maintain the current state.

4. The collaborative control method for multiple dynamic support devices as described in claim 1, characterized in that: The generation of the final coordinated action instruction includes: All preliminary action commands are analyzed centrally within the same control cycle to identify the coupling relationship between the actions of different support equipment in terms of time and effect; Based on the preset cooperative action protocol, a rule-based heuristic coordination algorithm is used to reorder the execution order of action instructions and modify the action amplitude while satisfying the system stability constraints. Generate the final coordinated action instructions and distribute them to each supporting device according to the device identifier.

5. The collaborative control method for multi-element dynamic support equipment as described in claim 1, characterized in that: The process of collecting a new round of system status snapshots and comparing them with the expected target includes: The system acquires the operating status of each supporting device after its actions are executed and the overall posture of the supported object, and generates a new round of system status snapshots. The deviation metric between the current system state snapshot's state feature vector and the target state feature vector corresponding to the expected target is calculated using the following formula: ； in, This represents the overall deviation of the system's current state from the expected target. This represents the state feature vector corresponding to the current system state snapshot; This represents the target state feature vector corresponding to the expected target; Represents norm operations; The expected target is composed of the target attitude state of the supported object, the target displacement and stress range of each supporting device, and the overall stability index of the system.

6. The collaborative control method for multiple dynamic support equipment as described in claim 1, characterized in that: The step of updating the rule weights in the rule-based priority calculation unit according to the comparison results includes: After obtaining the deviation measurement results, the deviation measurement results are correlated with the triggering status of each rule in the rule base during the current control cycle. The actual impact of each rule on the system state changes is evaluated, and the rule weights are updated and adjusted. The rule weight update process is represented as follows: ； in, Indicates the first The rule weight of each rule in the next control cycle. Indicates the first The rule weight of each rule in the current control cycle; This represents the adjustment coefficient for updating rule weights; Indicates the first The rule in the current deviation result The following are the evaluation results of the contribution to the achievement of system goals; The contribution evaluation result is determined based on the trend of system deviation change before and after the rule is triggered: if the system state and expected target are within the preset range after the rule is triggered, a positive value is taken; otherwise, a negative value is taken. The value is related to the magnitude of the deviation change.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the collaborative control method for a multi-element dynamic support device as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the collaborative control method for a multi-element dynamic support device as described in any one of claims 1 to 6.

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