Hardware-software integrated collaborative system and method applied to the robot ecosystem

Abstract

This invention provides a hardware-software integrated collaborative system and method for robot ecosystems. The system includes: a business determination module for acquiring the business processes of the robot ecosystem, determining a set of business items within the ecosystem based on the business processes, and extracting attribute information for each business item; a function determination module for determining the action type and execution purpose of each business item performed by the robot hardware based on the attribute information, determining a target low-code combination based on the action type and execution purpose, and binding the target low-code combination to the robot hardware based on a preset controller; and an execution module for real-time monitoring of work instructions based on the binding results, and controlling the robot hardware to execute the target action according to the target low-code combination based on the preset controller upon detecting a work instruction. This ensures the high efficiency and accuracy of the hardware-software integrated collaboration, ensures more stable robot collaborative operations, and improves the collaborative operation effect.

Description

Technical Field

[0001] This invention relates to the field of digital data processing technology, and in particular to a hardware-software integrated collaborative system and method for use in the robot ecosystem. Background Technology

[0002] Robots can replace or assist humans in completing various tasks. In addition to their widespread use in the manufacturing sector, robots are also used in resource exploration and development, disaster relief and risk mitigation, medical services, home entertainment, military and aerospace, and other fields. They are important production and service equipment in both industrial and non-industrial sectors, and are also indispensable automation equipment in the field of advanced manufacturing technology.

[0003] However, at present, most robots are controlled by predetermined control programs when performing production operations. This means that the actions or types of business that robots can perform are relatively simple and cannot be adjusted in a timely manner according to production requirements, resulting in poor production efficiency. Secondly, since robots are controlled by fixed control programs, it is inevitable that the actions performed will not match the production requirements, which greatly reduces the effectiveness of the robot's software and hardware system.

[0004] Therefore, this invention provides a hardware-software integrated collaborative system and method for application in the robot ecosystem. Summary of the Invention

[0005] This invention provides a hardware-software integrated collaborative system and method for the robot ecosystem. By analyzing the business processes of the robot ecosystem, it effectively confirms the target low-code combinations required by the robot based on the action types and execution purposes of each business item in the business process. Finally, it binds the target low-code combinations to the robot hardware and controls the robot hardware to execute the corresponding target actions according to the binding results. This ensures the high efficiency and accuracy of the hardware-software integrated collaboration of the robot, ensures more stable robot collaborative operations, and improves the effectiveness of collaborative operations.

[0006] This invention provides a hardware-software integrated collaborative system for use in the robot ecosystem, comprising:

[0007] The business determination module is used to obtain the business processes of the robot ecosystem, determine the set of business projects in the robot ecosystem based on the business processes, and extract the attribute information of each business project in the set of business projects.

[0008] The function determination module is used to determine the action type and execution purpose of each business project executed by the robot hardware based on attribute information, and to determine the target low code combination based on the action type and execution purpose, and to bind the target low code combination to the robot hardware based on the preset controller.

[0009] The execution module is used to monitor work instructions in real time based on the binding results, and when a work instruction is detected, it controls the robot hardware to execute the target action based on the target low code combination according to the preset controller.

[0010] Preferably, a hardware-software integrated collaborative system applied to the robot ecosystem includes a business determination module, comprising:

[0011] The request parsing unit is used to obtain business processing requests issued by the backend management terminal, parse the business processing requests, extract target keywords from the business processing requests, and determine the type of business to be executed based on the target keywords.

[0012] The business process determination unit is used to match the target robot ecosystem chain from the preset robot ecosystem chain list based on the type of business to be executed, and to extract the business process corresponding to the target robot ecosystem chain from the preset management library based on the production chain identifier of the target robot ecosystem chain.

[0013] Business item identification unit, used for:

[0014] The business process is analyzed to identify the production nodes contained in the business process, and the target relevance of each production node is determined based on the production characteristics of the production nodes.

[0015] Production nodes are grouped based on target relevance, and the set of business projects in the robot ecosystem is obtained based on the grouping results and the production characteristics of each group.

[0016] Preferably, a hardware-software integrated collaborative system applied to the robot ecosystem includes the following business project determination units:

[0017] The production feature analysis subunit is used to parse the production features of production nodes, determine the edge production steps corresponding to each production node, and determine the execution logic between the edge production steps of adjacent production nodes.

[0018] The correlation analysis subunit is used to determine the target correlation of each production node based on the execution logic, and to evaluate the target correlation based on the preset correlation evaluation index to obtain the target correlation value.

[0019] The grouping subunit is used to compare the target correlation value with the preset correlation threshold, and based on the comparison result, determine the production nodes whose target correlation value is less than the preset correlation threshold as edge nodes, and group the production nodes based on the edge nodes.

[0020] Preferably, a hardware-software integrated collaborative system applied to the robot ecosystem includes a business determination module, comprising:

[0021] The business project acquisition unit is used to acquire the obtained set of business projects and retrieve the historical operation data corresponding to each business project from the preset operation database based on the project identifier of each business project in the set of business projects.

[0022] The feature determination unit is used to analyze historical operation data, determine the target operation links corresponding to the historical operation data, and determine the operation characteristics of each target operation link based on the target values ​​of the historical operation data.

[0023] The attribute determination unit is used to summarize the operational characteristics corresponding to the target operation links in each business project, obtain a business project characteristic list, and associate the business project characteristic list with the corresponding business projects to obtain the attribute information of each business project.

[0024] Preferably, a hardware-software integrated collaborative system applied to the robot ecosystem includes a functional determination module comprising:

[0025] The attribute information acquisition unit is used to acquire the attribute information of each business item, convert the attribute information into target text, and perform word segmentation on the target text to obtain the vocabulary set corresponding to the attribute information.

[0026] Vocabulary classification units are used for:

[0027] The preset action label and preset purpose label are respectively used as the first category center and the second category center. The vocabulary set is clustered based on the first category center and the second category center. Based on the clustering results, the first Hamming distance and the second Hamming distance between each word and the first category center and the second category center are determined.

[0028] Words whose first Hamming distance is less than the second Hamming distance are assigned to the first category center; otherwise, they are assigned to the second category center. Based on the classification results, action word sets and target word sets are obtained. The first feature information of each action word in the action word set and the second feature information of each target word in the target word set are extracted. At the same time, the extracted first and second feature information are matched with preset production indicators, and the action type and execution purpose of each business project are obtained based on the matching results.

[0029] Low-code assembly units, used for:

[0030] Extract the first target attribute of the action type, and determine the business dimension and instruction logic corresponding to the action type based on the first target attribute. Match the target low-code instruction characters from the preset low-code database based on the business dimension. At the same time, logically associate the target low-code instruction characters under each business dimension based on the instruction logic to obtain the initial low-code combination.

[0031] Extract the second target attribute of the execution purpose, and determine the action type and the target association relationship based on the first target attribute and the second target attribute. At the same time, determine the execution intensity of each action type based on the second target attribute and the target association relationship, assign code values ​​to the initial low code combination based on the execution intensity, and obtain the target low code combination based on the assignment result.

[0032] Preferably, a hardware-software integrated collaborative system applied to the robot ecosystem includes a functional determination module comprising:

[0033] The code acquisition unit is used to acquire the target low-code combination, encapsulate the target low-code combination, and compile the encapsulated target low-code component to obtain the target program file.

[0034] The program input unit is used to read the target program file based on the preset code burning program, and to control the preset code burning program to input the read target program file into the target storage area in the preset controller through the auxiliary channel based on the timing controller.

[0035] Binding unit, used for:

[0036] Based on the input results, the target control components corresponding to different program blocks in the target program file are determined, and the target program file is analyzed based on the preset controller to determine the collaborative control time and collaborative control sequence of the target control components.

[0037] Based on the collaborative control time and sequence, a collaborative control strategy is formulated, and based on the collaborative control strategy, the preset controller is docked with the microcontrollers corresponding to each target control component. Based on the docking results, the target low-code combination is bound to the robot hardware.

[0038] Preferably, a hardware-software integrated collaborative system for use in the robot ecosystem includes a binding unit comprising:

[0039] The pre-operation subunit is used to control the robot to perform pre-operations based on the binding results, monitor the robot's pre-operation process in real time, and determine the robot's set of operational behaviors based on the pre-operation process.

[0040] The verification subunit is used to match each operation behavior in the operation behavior set with the standard operation behavior corresponding to the target program file, and when there is an operation behavior in the operation behavior set that is inconsistent with the standard operation behavior, the operation behavior that is inconsistent with the standard operation behavior is determined to be an abnormal operation behavior.

[0041] The code updates the sub-unit for:

[0042] Determine the behavior type of the abnormal operation, determine the code address of the abnormal low code segment corresponding to the abnormal operation in the target program file based on the behavior type, and lock the abnormal low code segment of the target program file based on the code address;

[0043] Based on the locking results, the associated low code segments that have a logical connection with the abnormal low code segments are identified, and the interaction relationship between the associated low code segments and the abnormal low code segments is determined. At the same time, the behavior difference between the abnormal operation behavior and the standard operation behavior is determined, and the structure and code values ​​of the abnormal low code segments are adjusted based on the behavior difference. Based on the adjustment results and the interaction relationship, the associated low code segments are updated synchronously.

[0044] Preferably, an integrated hardware and software collaborative system for the robot ecosystem includes an execution module comprising:

[0045] The instruction monitoring unit is used to configure a trigger monitoring mechanism in the preset controller, and to monitor the instruction input channel in the preset controller in real time based on the trigger monitoring mechanism. When a work instruction is detected, the work instruction is parsed to determine the instruction characteristics corresponding to the work instruction.

[0046] The instruction analysis unit is used to match instruction features with preset trigger conditions, and when the instruction features match the preset trigger conditions, it extracts sensitive instruction characters from the instruction features and determines the target production item to be controlled based on the sensitive instruction characters.

[0047] The control unit is used to determine the corresponding target low-code combination based on the target production project to be controlled, and to parse the target low-code combination to determine the robot's collaborative hardware. At the same time, based on the parsing result of the target low-code combination, the motion parameters of each hardware in the collaborative hardware are determined, and the motion trajectory of each hardware is determined based on the motion parameters. Furthermore, based on a preset controller, each hardware is controlled to perform the target action based on the motion trajectory.

[0048] Preferably, an integrated hardware and software collaborative system for the robot ecosystem includes an execution module comprising:

[0049] The accuracy calculation unit is used to monitor the total number of times the preset controller controls the robot within the target time period, determine the number of times the software and hardware integration coordination error occurs in the total number of times, and calculate the accuracy of the software and hardware integration coordination control of the robot based on the total number of times the control is performed and the number of software and hardware integration coordination errors.

[0050] The efficiency calculation unit is used to determine the transmission speed of work instructions during each instance of integrated hardware and software control of the robot based on the total number of robot control operations, and to calculate the efficiency of integrated hardware and software control of the robot based on the transmission speed.

[0051] Evaluation unit, used for:

[0052] Determine the influence weights of accuracy and efficiency in the integrated hardware and software control of the robot, and calculate the comprehensive control evaluation value of the integrated hardware and software control of the robot based on the influence weights, accuracy and efficiency.

[0053] When the comprehensive control evaluation value is greater than or equal to the preset evaluation threshold, the robot's integrated hardware and software control effect is deemed qualified; otherwise, the robot's integrated hardware and software control is deemed unqualified, and the integrated hardware and software control strategy is re-executed until the comprehensive control evaluation value is greater than or equal to the preset evaluation threshold.

[0054] This invention provides a hardware-software integrated collaborative method for application in the robot ecosystem, comprising:

[0055] Step 1: Obtain the business processes of the robot ecosystem, determine the set of business projects in the robot ecosystem based on the business processes, and extract the attribute information of each business project in the set of business projects.

[0056] Step 2: Determine the action type and execution purpose of each business project by the robot hardware based on the attribute information, determine the target low code combination based on the action type and execution purpose, and bind the target low code combination to the robot hardware based on the preset controller;

[0057] Step 3: Monitor work instructions in real time based on the binding results, and when a work instruction is detected, control the robot hardware to execute the target action based on the target low code combination according to the preset controller.

[0058] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0059] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0060] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0061] Figure 1 This is a structural diagram of a hardware-software integrated collaborative system applied to the robot ecosystem in an embodiment of the present invention;

[0062] Figure 2This is a structural diagram of a business determination module in a hardware-software integrated collaborative system applied to the robot ecosystem, as described in an embodiment of the present invention.

[0063] Figure 3 This is a flowchart of a hardware-software integrated collaborative method applied to the robot ecosystem in an embodiment of the present invention. Detailed Implementation

[0064] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0065] Example 1:

[0066] This embodiment provides a hardware-software integrated collaborative system for application in the robot ecosystem, such as... Figure 1 As shown, it includes:

[0067] The business determination module is used to obtain the business processes of the robot ecosystem, determine the set of business projects in the robot ecosystem based on the business processes, and extract the attribute information of each business project in the set of business projects.

[0068] The function determination module is used to determine the action type and execution purpose of each business project executed by the robot hardware based on attribute information, and to determine the target low code combination based on the action type and execution purpose, and to bind the target low code combination to the robot hardware based on the preset controller.

[0069] The execution module is used to monitor work instructions in real time based on the binding results, and when a work instruction is detected, it controls the robot hardware to execute the target action based on the target low code combination according to the preset controller.

[0070] In this embodiment, the business process refers to all the business types included in the robot ecosystem and the specific work content corresponding to each business.

[0071] In this embodiment, the set of business items refers to all business items included in the business process, which may specifically include business items such as crawling, redirection, and movement.

[0072] In this embodiment, attribute information refers to the project type of each business project, as well as the effects and objectives that each business project needs to achieve during operation.

[0073] In this embodiment, robot hardware refers to the hardware devices contained in the robot, specifically devices such as a mobile base, a robotic arm, and a steering mechanism.

[0074] In this embodiment, the target low-code combination refers to the set of codes corresponding to the robot hardware when performing corresponding operations or actions. The code does not require secondary development; the corresponding action can be achieved by combining the various low codes.

[0075] In this embodiment, the preset controller is pre-set and located inside the robot. It serves as an intermediary connecting the low-code combinatorial and the robot hardware. That is, by analyzing the low-code combinatorial, the controller can control the robot hardware to perform corresponding actions based on the analysis results, thereby achieving the purpose of collaboration.

[0076] In this embodiment, binding the target low-code combination with the robot hardware based on the preset controller means associating the target low-code combination with the robot hardware, thereby facilitating the control of the robot hardware to perform corresponding actions based on the target low-code combination.

[0077] In this embodiment, the work instruction is a condition used to trigger the robot to work. It can be automatically sensed by the robot or sent by the management terminal.

[0078] In this embodiment, the target action refers to the action performed by controlling the robot hardware to be consistent with the target low-code combination requirements, that is, to achieve the purpose of integrated hardware and software collaboration.

[0079] The beneficial effects of the above technical solution are as follows: by analyzing the business processes of the robot ecosystem, the target low-code combination required by the robot can be effectively confirmed according to the action type and execution purpose of each business item in the business process. Finally, the target low-code combination is bound to the robot hardware, and the robot hardware is controlled to execute the corresponding target action according to the target low-code combination based on the binding result. This ensures the efficiency and accuracy of the robot's integrated hardware and software collaboration, ensures more stable robot collaborative operation, and improves the effect of collaborative operation.

[0080] Example 2:

[0081] Based on Example 1, this example provides a hardware-software integrated collaborative system for the robot ecosystem, such as... Figure 2 As shown, the business determination module includes:

[0082] The request parsing unit is used to obtain business processing requests issued by the backend management terminal, parse the business processing requests, extract target keywords from the business processing requests, and determine the type of business to be executed based on the target keywords.

[0083] The business process determination unit is used to match the target robot ecosystem chain from the preset robot ecosystem chain list based on the type of business to be executed, and to extract the business process corresponding to the target robot ecosystem chain from the preset management library based on the production chain identifier of the target robot ecosystem chain.

[0084] Business item identification unit, used for:

[0085] The business process is analyzed to identify the production nodes contained in the business process, and the target relevance of each production node is determined based on the production characteristics of the production nodes.

[0086] Production nodes are grouped based on target relevance, and the set of business projects in the robot ecosystem is obtained based on the grouping results and the production characteristics of each group.

[0087] In this embodiment, the business processing request is sent by the backend management terminal, such as a computer terminal. The business processing request is used to characterize the type of business that needs to be performed and the goal that the business processing needs to achieve.

[0088] In this embodiment, the target keyword refers to a data segment in the business processing request that can characterize the business processing type or business processing purpose.

[0089] In this embodiment, the type of business to be executed refers to the type of business that needs to be processed corresponding to the business processing request issued by the backend management terminal.

[0090] In this embodiment, the preset robot ecosystem list is pre-set and is used to store all existing robot ecosystems, and is not unique.

[0091] In this embodiment, the target robot ecosystem refers to the robot ecosystem that is matched from the preset robot ecosystem list and corresponds to the current business processing request.

[0092] In this embodiment, the production chain identifier is a tag used to mark different robot ecosystem chains.

[0093] In this embodiment, the preset management library is pre-set and used to store business processes corresponding to different robot ecosystems.

[0094] In this embodiment, a production node refers to a specific production item contained in the business process, which is used to characterize the specific production steps corresponding to the production item.

[0095] In this embodiment, target relevance is used to characterize the degree of association between each production node, thereby facilitating the grouping of production nodes and determining the business items included in the production process.

[0096] In this embodiment, production features are used to characterize the specific production methods and standards corresponding to each group of production nodes, thereby facilitating the determination of the corresponding production projects.

[0097] The beneficial effects of the above technical solution are as follows: by parsing the business processing requests issued by the backend management terminal, the robot ecosystem corresponding to the business processing requests can be locked. Secondly, by parsing the business processes corresponding to the robot ecosystem, the set of business projects in the robot ecosystem can be accurately and reliably confirmed. This facilitates effective confirmation based on the target low-code combination corresponding to the business projects, thereby improving the efficiency and accuracy of the integrated hardware and software collaboration of the robot.

[0098] Example 3:

[0099] Based on Example 2, this example provides a hardware-software integrated collaborative system for the robot ecosystem, wherein the business project determination unit includes:

[0100] The production feature analysis subunit is used to parse the production features of production nodes, determine the edge production steps corresponding to each production node, and determine the execution logic between the edge production steps of adjacent production nodes.

[0101] The correlation analysis subunit is used to determine the target correlation of each production node based on the execution logic, and to evaluate the target correlation based on the preset correlation evaluation index to obtain the target correlation value.

[0102] The grouping subunit is used to compare the target correlation value with the preset correlation threshold, and based on the comparison result, determine the production nodes whose target correlation value is less than the preset correlation threshold as edge nodes, and group the production nodes based on the edge nodes.

[0103] In this embodiment, the edge production step refers to the last production step in each production node, which makes it easier to determine the degree of association between the current production node and the next production node.

[0104] In this embodiment, the execution logic is used to characterize the production sequence of edge production steps between adjacent production nodes and whether there is an interaction relationship.

[0105] In this embodiment, the preset relevance evaluation index is set in advance and is used to evaluate the relevance of the target. Specifically, it can be evaluated based on the execution order and interdependence of the steps represented in the target relevance.

[0106] In this embodiment, the target correlation value is used to characterize the degree of interrelation between each production node. The larger the value, the higher the degree of correlation between them.

[0107] In this embodiment, the preset relevant threshold is set in advance and is the minimum standard used to measure whether adjacent production nodes are related. It can be adjusted.

[0108] In this embodiment, edge nodes are used to characterize the last production step corresponding to each production step, and are the boundaries that distinguish the next production project in the robot ecosystem.

[0109] The beneficial effects of the above technical solution are: by analyzing the production characteristics of production nodes, the production nodes can be accurately and effectively grouped according to their production characteristics, thereby ensuring the accuracy of the final set of production projects, providing a guarantee for improving the accuracy of the integrated hardware and software collaboration of robots, and improving the effect of collaborative operation.

[0110] Example 4:

[0111] Based on Example 1, this example provides a hardware-software integrated collaborative system for the robot ecosystem, including a business determination module:

[0112] The business project acquisition unit is used to acquire the obtained set of business projects and retrieve the historical operation data corresponding to each business project from the preset operation database based on the project identifier of each business project in the set of business projects.

[0113] The feature determination unit is used to analyze historical operation data, determine the target operation links corresponding to the historical operation data, and determine the operation characteristics of each target operation link based on the target values ​​of the historical operation data.

[0114] The attribute determination unit is used to summarize the operational characteristics corresponding to the target operation links in each business project, obtain a business project characteristic list, and associate the business project characteristic list with the corresponding business projects to obtain the attribute information of each business project.

[0115] In this embodiment, the project identifier is a label used to distinguish different production projects.

[0116] In this embodiment, the preset operating database is pre-set and used to store historical operating data corresponding to different production projects in the robot production chain. The historical operating data is the work data generated by different production projects in carrying out production tasks.

[0117] In this embodiment, the target operation stage refers to the production steps included in the production project, and it is not unique.

[0118] In this embodiment, the target value is used to characterize the specific values ​​of historical running data.

[0119] In this embodiment, the operational characteristics refer to the specific operational methods and features corresponding to each operational stage.

[0120] In this embodiment, the business project feature list refers to a record table obtained by summarizing the operational features of the target operation process.

[0121] The beneficial effects of the above technical solution are: by analyzing the operational links contained in the set of business projects, the business characteristics of each business project can be accurately and effectively analyzed, and the attribute information of each business project can be accurately and effectively determined based on the analysis results. This facilitates the effective confirmation of the target low-code combination corresponding to the production project, thereby improving the efficiency and accuracy of the robot's integrated hardware and software collaboration.

[0122] Example 5:

[0123] Based on Example 1, this example provides a hardware-software integrated collaborative system for the robot ecosystem, including a function determination module:

[0124] The attribute information acquisition unit is used to acquire the attribute information of each business item, convert the attribute information into target text, and perform word segmentation on the target text to obtain the vocabulary set corresponding to the attribute information.

[0125] Vocabulary classification units are used for:

[0126] The preset action label and preset purpose label are respectively used as the first category center and the second category center. The vocabulary set is clustered based on the first category center and the second category center. Based on the clustering results, the first Hamming distance and the second Hamming distance between each word and the first category center and the second category center are determined.

[0127] Words whose first Hamming distance is less than the second Hamming distance are assigned to the first category center; otherwise, they are assigned to the second category center. Based on the classification results, action word sets and target word sets are obtained. The first feature information of each action word in the action word set and the second feature information of each target word in the target word set are extracted. At the same time, the extracted first and second feature information are matched with preset production indicators, and the action type and execution purpose of each business project are obtained based on the matching results.

[0128] Low-code assembly units, used for:

[0129] Extract the first target attribute of the action type, and determine the business dimension and instruction logic corresponding to the action type based on the first target attribute. Match the target low-code instruction characters from the preset low-code database based on the business dimension. At the same time, logically associate the target low-code instruction characters under each business dimension based on the instruction logic to obtain the initial low-code combination.

[0130] Extract the second target attribute of the execution purpose, and determine the action type and the target association relationship based on the first target attribute and the second target attribute. At the same time, determine the execution intensity of each action type based on the second target attribute and the target association relationship, assign code values ​​to the initial low code combination based on the execution intensity, and obtain the target low code combination based on the assignment result.

[0131] In this embodiment, the target text refers to the specific data obtained after converting the attribute information of each business item into corresponding text content.

[0132] In this embodiment, word segmentation refers to splitting the target text into multiple words, thereby facilitating the parsing of the specific content of the target text.

[0133] In this embodiment, the vocabulary set refers to the multiple vocabulary sets obtained after splitting the target text into words.

[0134] In this embodiment, the preset action labels are pre-defined and used to characterize all types of actions that the robot can perform.

[0135] In this embodiment, the preset target label is pre-set and is used to characterize the standard production target that the robot needs to achieve in each production project.

[0136] In this embodiment, the first category center refers to using preset action tags as classification criteria to categorize words that have similar characteristics to the preset action tags.

[0137] In this embodiment, the second category center refers to classifying words that have similar characteristics to the preset target label by using the preset target label as the classification standard.

[0138] In this embodiment, the first Hamming distance is used to characterize the similarity between the data features of each word in the vocabulary set and the first category center. The smaller the Hamming distance, the more likely the word is to be classified into the category corresponding to the current category center.

[0139] In this embodiment, the second Hamming distance is used to characterize the similarity between the data features of each word in the vocabulary set and the second category center. The smaller the Hamming distance, the more likely the word is to be classified into the category corresponding to the current category center.

[0140] In this embodiment, the action vocabulary set refers to the vocabulary set obtained by classifying all words related to the action in the vocabulary set corresponding to the attribute information.

[0141] In this embodiment, the target vocabulary set refers to the vocabulary set obtained by classifying all words related to the execution purpose from the vocabulary set corresponding to the attribute information.

[0142] In this embodiment, the first feature information refers to the type of action and the intensity of the action represented by each action word in the action word set.

[0143] In this embodiment, the second feature information refers to the production standards and production effects of the production projects represented by each target vocabulary in the target vocabulary set.

[0144] In this embodiment, the preset production indicators are pre-set and are characteristic information that represents the specific type and purpose of the action to be performed.

[0145] In this embodiment, the first target attribute is used to characterize the features of the action type, the interrelationship between actions, and the specific action parameters of the action type.

[0146] In this embodiment, the business dimension refers to all the types of actions that the robot needs to perform simultaneously when executing a production project.

[0147] In this embodiment, the instruction logic is the logical relationship between instruction characters when controlling the robot to perform corresponding action types, thereby facilitating accurate control of the robot.

[0148] In this embodiment, the preset low-code database is pre-set and used to store different low-code instruction characters.

[0149] In this embodiment, the target low-code instruction character refers to all low-codes corresponding to the current business dimension.

[0150] In this embodiment, the initial low-code combination refers to the code segment obtained by concatenating the target low-code instruction characters, which can control the robot.

[0151] In this embodiment, the second target attribute is used to characterize the standard or effect that needs to be achieved for the purpose of execution.

[0152] In this embodiment, the target association relationship is used to characterize the interaction between action types and execution purposes, that is, what type of action can achieve what execution purpose.

[0153] In this embodiment, the execution force is used to characterize the specific degree of action corresponding to each execution action, such as the force of a robotic arm grasping.

[0154] In this embodiment, code assignment modifies the value parameters of the obtained initial low code combination, thereby enabling the robot to execute corresponding action parameters. That is, when it is necessary to control the robot to move, the robot can be controlled to move a corresponding distance according to the assignment result.

[0155] In this embodiment, the target low-code combination refers to the final code combination obtained after assigning values ​​to the initial low-code combination, which can directly control the robot.

[0156] The beneficial effects of the above technical solution are as follows: By analyzing the attribute information of each business project, the robot can accurately and effectively determine the action type and execution purpose of each business project. Secondly, by parsing the obtained action type and execution purpose, the robot can effectively determine and combine the low-code corresponding to the action type and execution purpose. This facilitates accurate and reliable integrated hardware and software control of the robot, ensures the efficiency and accuracy of the robot's integrated hardware and software collaboration, ensures more stable robot collaborative operation, and improves the effect of collaborative operation.

[0157] Example 6:

[0158] Based on Example 1, this example provides a hardware-software integrated collaborative system for the robot ecosystem, including a function determination module:

[0159] The code acquisition unit is used to acquire the target low-code combination, encapsulate the target low-code combination, and compile the encapsulated target low-code component to obtain the target program file.

[0160] The program input unit is used to read the target program file based on the preset code burning program, and to control the preset code burning program to input the read target program file into the target storage area in the preset controller through the auxiliary channel based on the timing controller.

[0161] Binding unit, used for:

[0162] Based on the input results, the target control components corresponding to different program blocks in the target program file are determined, and the target program file is analyzed based on the preset controller to determine the collaborative control time and collaborative control sequence of the target control components.

[0163] Based on the collaborative control time and sequence, a collaborative control strategy is formulated, and based on the collaborative control strategy, the preset controller is docked with the microcontrollers corresponding to each target control component. Based on the docking results, the target low-code combination is bound to the robot hardware.

[0164] In this embodiment, program compilation refers to editing the obtained target low-code combination in order to edit the target low-code combination into a qualified control instruction, thereby achieving the purpose of inputting it into the preset controller.

[0165] In this embodiment, the target level file refers to the file obtained after compiling the encapsulated target low-code component into a program, which is directly entered into the preset controller at all times and associated with the robot.

[0166] In this embodiment, the preset code burning program is pre-set and is used to burn the obtained target program file to the preset controller.

[0167] In this embodiment, the timing controller controls the order, time, and speed at which the preset code burning program burns the target file.

[0168] In this embodiment, the target storage area is the storage space in the preset controller used to store the target program file.

[0169] In this embodiment, a program block refers to a program segment in the target program file that corresponds to different control functions.

[0170] In this embodiment, the target control component refers to the hardware corresponding to the different program blocks in the target program file when controlling the robot, specifically it can be a robotic arm, a moving component, or a gripper, etc.

[0171] In this embodiment, the cooperative control time and cooperative control sequence are used to characterize the order in which each target control component is executed during the operation of the robot (e.g., they can be performed simultaneously or sequentially).

[0172] In this embodiment, the cooperative control strategy is used to characterize the scheme for cooperative control of each target control component.

[0173] In this embodiment, the microcontroller is the controller corresponding to each target control component, thereby facilitating the independent operation of each target control component.

[0174] The beneficial effects of the above technical solution are as follows: by analyzing the obtained target low-code combination, the target low-code combination is converted into a corresponding program file, and the program file is effectively entered into a preset server. The preset server calculates the program file, locks the target control components corresponding to each program block, and effectively binds the target low-code combination with the robot hardware, ensuring more stable robot collaborative operation and improving the effect of collaborative operation.

[0175] Example 7:

[0176] Based on Example 6, this example provides a hardware-software integrated collaborative system for the robot ecosystem, including a binding unit comprising:

[0177] The pre-operation subunit is used to control the robot to perform pre-operations based on the binding results, monitor the robot's pre-operation process in real time, and determine the robot's set of operational behaviors based on the pre-operation process.

[0178] The verification subunit is used to match each operation behavior in the operation behavior set with the standard operation behavior corresponding to the target program file, and when there is an operation behavior in the operation behavior set that is inconsistent with the standard operation behavior, the operation behavior that is inconsistent with the standard operation behavior is determined to be an abnormal operation behavior.

[0179] The code updates the sub-unit for:

[0180] Determine the behavior type of the abnormal operation, determine the code address of the abnormal low code segment corresponding to the abnormal operation in the target program file based on the behavior type, and lock the abnormal low code segment of the target program file based on the code address;

[0181] Based on the locking results, the associated low code segments that have a logical connection with the abnormal low code segments are identified, and the interaction relationship between the associated low code segments and the abnormal low code segments is determined. At the same time, the behavior difference between the abnormal operation behavior and the standard operation behavior is determined, and the structure and code values ​​of the abnormal low code segments are adjusted based on the behavior difference. Based on the adjustment results and the interaction relationship, the associated low code segments are updated synchronously.

[0182] In this embodiment, pre-operation refers to simulating the operation of the robot by using the obtained target low-code combination, with the purpose of verifying whether the obtained target low-code combination is accurate.

[0183] In this embodiment, the set of operational behaviors refers to all operational actions included in the robot's pre-operation process.

[0184] In this embodiment, the standard operating behavior is known and is the standard action corresponding to the target program file.

[0185] In this embodiment, the abnormal low code segment refers to the low code combination corresponding to the abnormal operation behavior, such as the code combination that controls the robot to move.

[0186] In this embodiment, the code address is used to characterize the specific location of the abnormal low-code segment in the target program file.

[0187] In this embodiment, the associated low code segment refers to a code segment in the target program file that is associated with an abnormal low code segment.

[0188] In this embodiment, the interaction relationship is used to characterize the degree of mutual influence between the associated low code segment and the abnormal low code segment, that is, the degree of influence on the associated low code segment during execution when the abnormal low code segment changes.

[0189] In this embodiment, the behavior difference is used to characterize the magnitude of the difference between abnormal operating behavior and standard operating behavior.

[0190] The beneficial effects of the above technical solution are: by performing pre-operation on the bound robot, the obtained target low-code combination can be accurately and effectively verified, thereby facilitating the accurate and effective judgment of whether there are abnormal low-code combinations in the target low-code combination based on the robot's execution actions, and timely adjustment of abnormal low-code segments when they exist, ensuring the accuracy and reliability of the final low-code combination, ensuring more stable robot collaborative operation, and improving the effect of collaborative operation.

[0191] Example 8:

[0192] Based on Example 1, this example provides a hardware-software integrated collaborative system for the robot ecosystem, including an execution module comprising:

[0193] The instruction monitoring unit is used to configure a trigger monitoring mechanism in the preset controller, and to monitor the instruction input channel in the preset controller in real time based on the trigger monitoring mechanism. When a work instruction is detected, the work instruction is parsed to determine the instruction characteristics corresponding to the work instruction.

[0194] The instruction analysis unit is used to match instruction features with preset trigger conditions, and when the instruction features match the preset trigger conditions, it extracts sensitive instruction characters from the instruction features and determines the target production item to be controlled based on the sensitive instruction characters.

[0195] The control unit is used to determine the corresponding target low-code combination based on the target production project to be controlled, and to parse the target low-code combination to determine the robot's collaborative hardware. At the same time, based on the parsing result of the target low-code combination, the motion parameters of each hardware in the collaborative hardware are determined, and the motion trajectory of each hardware is determined based on the motion parameters. Furthermore, based on a preset controller, each hardware is controlled to perform the target action based on the motion trajectory.

[0196] In this embodiment, the trigger monitoring mechanism is a method for monitoring whether the preset controller has received a working instruction.

[0197] In this embodiment, the instruction input channel is used to connect the smart terminal and the preset controller, that is, to transmit the working instructions issued by the smart terminal.

[0198] In this embodiment, the instruction features refer to the control purpose, control conditions, and control type carried in the work instruction.

[0199] In this embodiment, the preset triggering condition is set in advance and used to parse the work instruction to determine whether the current work instruction meets the triggering requirements.

[0200] In this embodiment, sensitive instruction characters refer to key instruction fragments in the work instructions that can characterize the control of the robot to execute the corresponding production project.

[0201] In this embodiment, the target production project to be controlled refers to the production project that the robot needs to execute in the robot ecosystem under the control of the current work instruction.

[0202] In this embodiment, collaborative hardware refers to the hardware that the robot needs to run when it is currently performing the production project of the target to be controlled.

[0203] In this embodiment, motion parameters are parameters used to characterize the rotation angle or movement distance of each piece of hardware in the collaborative hardware when performing corresponding actions.

[0204] The beneficial effects of the above technical solution are as follows: By configuring a trigger monitoring mechanism in the preset controller, and accurately monitoring whether the preset controller receives work instructions through the trigger monitoring mechanism, and after detecting that a work instruction has been received, the work instruction is parsed to achieve accurate and effective determination of the production project to be controlled. Secondly, by retrieving the corresponding target low-code combination according to the production project to be controlled, the robot is controlled to perform the corresponding target action through the target low-code combination, which ensures the efficiency and accuracy of the robot's integrated hardware and software collaboration and improves the effect of collaborative operation.

[0205] Example 9:

[0206] Based on Example 1, this example provides a hardware-software integrated collaborative system for the robot ecosystem, including an execution module comprising:

[0207] The accuracy calculation unit is used to monitor the total number of times the preset controller controls the robot within the target time period, determine the number of times the software and hardware integration coordination error occurs in the total number of times, and calculate the accuracy of the software and hardware integration coordination control of the robot based on the total number of times the control is performed and the number of software and hardware integration coordination errors.

[0208] The accuracy of the integrated hardware and software control of the robot is calculated using the following formula:

[0209]

[0210] Where η represents the accuracy of the integrated hardware and software control of the robot, and its value ranges from (0, 1); μ represents the error factor, and its value ranges from (0.005, 0.02); M represents the total number of times the preset controller controls the robot within the target time period; i represents the sequence number of the action type executed by the preset controller within the target time period, and its value ranges from [1, n]; n represents the total number of action types executed by the preset controller within the target time period; m i This represents the number of errors that occurred for the i-th type of action within the target time period; This indicates the allowable error value, and the range of the value is (-0.05, 0.05);

[0211] The efficiency calculation unit is used to determine the transmission speed of work instructions during each instance of integrated hardware and software control of the robot based on the total number of robot control operations, and to calculate the efficiency of integrated hardware and software control of the robot based on the transmission speed.

[0212] The efficiency of the integrated hardware and software control of the robot can be calculated using the following formula:

[0213]

[0214] Where ω represents the efficiency of the integrated hardware and software control of the robot, and its value ranges from (0, 1); τ represents the total amount of data corresponding to the work instruction; β represents the amount of data transmitted by the channel each time, and is less than τ; V represents the transmission speed of the work instruction; S represents the transmission distance of the work instruction; t represents the delay value generated when transmitting the work instruction; and T represents the expected transmission time of the work instruction.

[0215] Evaluation unit, used for:

[0216] Determine the influence weights of accuracy and efficiency in the integrated hardware and software control of the robot, and calculate the comprehensive control evaluation value of the integrated hardware and software control of the robot based on the influence weights, accuracy and efficiency.

[0217] The comprehensive control evaluation value for the integrated hardware and software control of the robot is calculated using the following formula:

[0218] γ = ε*η + σ*ω;

[0219] Where γ represents the comprehensive control evaluation value of the robot's integrated hardware and software control; ε represents the influence weight of accuracy in the integrated hardware and software control of the robot; η represents the accuracy of the integrated hardware and software control of the robot, and its value ranges from (0, 1); σ represents the influence weight of efficiency in the integrated hardware and software control of the robot, and ε + σ = 1; ω represents the efficiency of the integrated hardware and software control of the robot, and its value ranges from (0, 1).

[0220] The calculated comprehensive control evaluation value is compared with the preset evaluation threshold.

[0221] When the comprehensive control evaluation value is greater than or equal to the preset evaluation threshold, the robot's integrated hardware and software control effect is deemed qualified; otherwise, the robot's integrated hardware and software control is deemed unqualified, and the integrated hardware and software control strategy is re-executed until the comprehensive control evaluation value is greater than or equal to the preset evaluation threshold.

[0222] In this embodiment, the target time period is set in advance, that is, the time period for monitoring the control effect of the robot, such as a day or a week.

[0223] In this embodiment, the expected transmission time is obtained through multiple training sessions and is used to characterize the transmission time required under normal circumstances.

[0224] In this embodiment, the influence weight is used to characterize the importance of accuracy and efficiency in the evaluation of the integrated hardware and software control of the robot.

[0225] In this embodiment, the comprehensive control evaluation value is used to characterize the effectiveness of the integrated hardware and software control of the robot. The larger the value, the better the effect of the integrated hardware and software control of the robot.

[0226] In this embodiment, the preset evaluation threshold is set in advance and is used to measure whether the effect of the integrated hardware and software control of the robot meets the minimum standard.

[0227] The beneficial effects of the above technical solution are: by calculating the accuracy and efficiency of the integrated hardware and software control of the robot, the effect of the integrated hardware and software control of the robot can be accurately and effectively evaluated. This makes it easier to adjust the integrated hardware and software control strategy in a timely manner when the control effect does not meet the expected requirements, thereby ensuring the efficiency and accuracy of the integrated hardware and software control of the robot and improving the effect of collaborative operation.

[0228] Example 10:

[0229] This embodiment provides a hardware-software integrated collaborative method for application in the robot ecosystem, such as... Figure 3 As shown, it includes:

[0230] Step 1: Obtain the business processes of the robot ecosystem, determine the set of business projects in the robot ecosystem based on the business processes, and extract the attribute information of each business project in the set of business projects.

[0231] Step 2: Determine the action type and execution purpose of each business project by the robot hardware based on the attribute information, determine the target low code combination based on the action type and execution purpose, and bind the target low code combination to the robot hardware based on the preset controller;

[0232] Step 3: Monitor work instructions in real time based on the binding results, and when a work instruction is detected, control the robot hardware to execute the target action based on the target low code combination according to the preset controller.

[0233] The beneficial effects of the above technical solution are as follows: by analyzing the business processes of the robot ecosystem, the target low-code combination required by the robot can be effectively confirmed according to the action type and execution purpose of each business item in the business process. Finally, the target low-code combination is bound to the robot hardware, and the robot hardware is controlled to execute the corresponding target action according to the target low-code combination based on the binding result. This ensures the efficiency and accuracy of the robot's integrated hardware and software collaboration, ensures more stable robot collaborative operation, and improves the effect of collaborative operation.

[0234] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A hardware-software integrated collaborative system for application in the robot ecosystem, characterized in that, include: The business determination module is used to obtain the business processes of the robot ecosystem, determine the set of business projects in the robot ecosystem based on the business processes, and extract the attribute information of each business project in the set of business projects. The function determination module is used to determine the action type and execution purpose of each business project executed by the robot hardware based on attribute information, and to determine the target low code combination based on the action type and execution purpose, and to bind the target low code combination to the robot hardware based on the preset controller. The execution module is used to monitor work instructions in real time based on the binding results, and when a work instruction is detected, it controls the robot hardware to execute the target action based on the target low code combination according to the preset controller. The business determination module includes: The request parsing unit is used to obtain business processing requests issued by the backend management terminal, parse the business processing requests, extract target keywords from the business processing requests, and determine the type of business to be executed based on the target keywords. The business process determination unit is used to match the target robot ecosystem chain from the preset robot ecosystem chain list based on the type of business to be executed, and to extract the business process corresponding to the target robot ecosystem chain from the preset management library based on the production chain identifier of the target robot ecosystem chain. Business item identification unit, used for: The business process is analyzed to identify the production nodes contained in the business process, and the target relevance of each production node is determined based on the production characteristics of the production nodes. Production nodes are grouped based on target relevance, and a set of business projects in the robot ecosystem is obtained based on the grouping results and the production characteristics of each group. The business project identification unit includes: The production feature analysis subunit is used to parse the production features of production nodes, determine the edge production steps corresponding to each production node, and determine the execution logic between the edge production steps of adjacent production nodes. The correlation analysis subunit is used to determine the target correlation of each production node based on the execution logic, and to evaluate the target correlation based on the preset correlation evaluation index to obtain the target correlation value. The grouping subunit is used to compare the target correlation value with the preset correlation threshold, and based on the comparison result, determine the production nodes whose target correlation value is less than the preset correlation threshold as edge nodes, and group the production nodes based on the edge nodes.

2. The integrated hardware and software collaborative system for robot ecosystems according to claim 1, characterized in that, The business determination module includes: The business project acquisition unit is used to acquire the obtained set of business projects and retrieve the historical operation data corresponding to each business project from the preset operation database based on the project identifier of each business project in the set of business projects. The feature determination unit is used to analyze historical operation data, determine the target operation links corresponding to the historical operation data, and determine the operation characteristics of each target operation link based on the target values ​​of the historical operation data. The attribute determination unit is used to summarize the operational characteristics corresponding to the target operation links in each business project, obtain a business project characteristic list, and associate the business project characteristic list with the corresponding business projects to obtain the attribute information of each business project.

3. The integrated hardware and software collaborative system for robot ecosystems according to claim 1, characterized in that, The function determination module includes: The attribute information acquisition unit is used to acquire the attribute information of each business item, convert the attribute information into target text, and perform word segmentation on the target text to obtain the vocabulary set corresponding to the attribute information. Vocabulary classification units are used for: The preset action label and preset purpose label are respectively used as the first category center and the second category center. The vocabulary set is clustered based on the first category center and the second category center. Based on the clustering results, the first Hamming distance and the second Hamming distance between each word and the first category center and the second category center are determined. Words whose first Hamming distance is less than the second Hamming distance are assigned to the first category center; otherwise, they are assigned to the second category center. Based on the classification results, action word sets and target word sets are obtained. The first feature information of each action word in the action word set and the second feature information of each target word in the target word set are extracted. At the same time, the extracted first and second feature information are matched with preset production indicators, and the action type and execution purpose of each business project are obtained based on the matching results. Low-code assembly units, used for: Extract the first target attribute of the action type, and determine the business dimension and instruction logic corresponding to the action type based on the first target attribute. Match the target low-code instruction characters from the preset low-code database based on the business dimension. At the same time, logically associate the target low-code instruction characters under each business dimension based on the instruction logic to obtain the initial low-code combination. Extract the second target attribute of the execution purpose, and determine the action type and the target association relationship based on the first target attribute and the second target attribute. At the same time, determine the execution intensity of each action type based on the second target attribute and the target association relationship, assign code values ​​to the initial low code combination based on the execution intensity, and obtain the target low code combination based on the assignment result.

4. The integrated hardware and software collaborative system for robot ecosystems according to claim 1, characterized in that, The function determination module includes: The code acquisition unit is used to acquire the target low-code combination, encapsulate the target low-code combination, and compile the encapsulated target low-code component to obtain the target program file. The program input unit is used to read the target program file based on the preset code burning program, and to control the preset code burning program to input the read target program file into the target storage area in the preset controller through the auxiliary channel based on the timing controller. Binding unit, used for: Based on the input results, the target control components corresponding to different program blocks in the target program file are determined, and the target program file is analyzed based on the preset controller to determine the collaborative control time and collaborative control sequence of the target control components. Based on the collaborative control time and sequence, a collaborative control strategy is formulated, and based on the collaborative control strategy, the preset controller is docked with the microcontrollers corresponding to each target control component. Based on the docking results, the target low-code combination is bound to the robot hardware.

5. A hardware-software integrated collaborative system for robot ecosystems according to claim 4, characterized in that, Binding units, including: The pre-operation subunit is used to control the robot to perform pre-operations based on the binding results, monitor the robot's pre-operation process in real time, and determine the robot's set of operational behaviors based on the pre-operation process. The verification subunit is used to match each operation behavior in the operation behavior set with the standard operation behavior corresponding to the target program file, and when there is an operation behavior in the operation behavior set that is inconsistent with the standard operation behavior, the operation behavior that is inconsistent with the standard operation behavior is determined to be an abnormal operation behavior. The code updates the sub-unit for: Determine the behavior type of the abnormal operation, determine the code address of the abnormal low code segment corresponding to the abnormal operation in the target program file based on the behavior type, and lock the abnormal low code segment of the target program file based on the code address; Based on the locking results, the associated low code segments that have a logical connection with the abnormal low code segments are identified, and the interaction relationship between the associated low code segments and the abnormal low code segments is determined. At the same time, the behavior difference between the abnormal operation behavior and the standard operation behavior is determined, and the structure and code values ​​of the abnormal low code segments are adjusted based on the behavior difference. Based on the adjustment results and the interaction relationship, the associated low code segments are updated synchronously.

6. The integrated hardware and software collaborative system for robot ecosystems according to claim 1, characterized in that, The execution module includes: The instruction monitoring unit is used to configure a trigger monitoring mechanism in the preset controller, and to monitor the instruction input channel in the preset controller in real time based on the trigger monitoring mechanism. When a work instruction is detected, the work instruction is parsed to determine the instruction characteristics corresponding to the work instruction. The instruction analysis unit is used to match instruction features with preset trigger conditions, and when the instruction features match the preset trigger conditions, it extracts sensitive instruction characters from the instruction features and determines the target production item to be controlled based on the sensitive instruction characters. The control unit is used to determine the corresponding target low-code combination based on the target production project to be controlled, and to parse the target low-code combination to determine the robot's collaborative hardware. At the same time, based on the parsing result of the target low-code combination, the motion parameters of each hardware in the collaborative hardware are determined, and the motion trajectory of each hardware is determined based on the motion parameters. Furthermore, based on a preset controller, each hardware is controlled to perform the target action based on the motion trajectory.

7. The integrated hardware and software collaborative system for robot ecosystems according to claim 1, characterized in that, The execution module includes: The accuracy calculation unit is used to monitor the total number of times the preset controller controls the robot within the target time period, determine the number of times the software and hardware integration coordination error occurs in the total number of times, and calculate the accuracy of the software and hardware integration coordination control of the robot based on the total number of times the control is performed and the number of software and hardware integration coordination errors. The efficiency calculation unit is used to determine the transmission speed of work instructions during each instance of integrated hardware and software control of the robot based on the total number of robot control operations, and to calculate the efficiency of integrated hardware and software control of the robot based on the transmission speed. Evaluation unit, used for: Determine the influence weights of accuracy and efficiency in the integrated hardware and software control of the robot, and calculate the comprehensive control evaluation value of the integrated hardware and software control of the robot based on the influence weights, accuracy and efficiency. When the comprehensive control evaluation value is greater than or equal to the preset evaluation threshold, the robot's integrated hardware and software control effect is deemed qualified; otherwise, the robot's integrated hardware and software control is deemed unqualified, and the integrated hardware and software control strategy is re-executed until the comprehensive control evaluation value is greater than or equal to the preset evaluation threshold.

8. A hardware-software integrated collaborative method applied to the robot ecosystem, characterized in that, include: Step 1: Obtain the business processes of the robot ecosystem, determine the set of business projects in the robot ecosystem based on the business processes, and extract the attribute information of each business project in the set of business projects. Step 2: Determine the action type and execution purpose of each business project by the robot hardware based on the attribute information, determine the target low code combination based on the action type and execution purpose, and bind the target low code combination to the robot hardware based on the preset controller; Step 3: Monitor work instructions in real time based on the binding results, and when a work instruction is detected, control the robot hardware to execute the target action based on the target low code combination according to the preset controller; Step 1 includes: Obtain business processing requests published by the backend management terminal, parse the business processing requests, extract target keywords from the business processing requests, and determine the type of business to be executed based on the target keywords; Based on the type of business to be executed, the target robot ecosystem is matched from the preset robot ecosystem list, and the business process corresponding to the target robot ecosystem is extracted from the preset management library based on the production chain identifier of the target robot ecosystem. The business process is analyzed to identify the production nodes contained in the business process, and the target relevance of each production node is determined based on the production characteristics of the production nodes. Production nodes are grouped based on target relevance, and a set of business projects in the robot ecosystem is obtained based on the grouping results and the production characteristics of each group. The production characteristics of the production nodes are analyzed to determine the edge production steps corresponding to each production node, and the execution logic between the edge production steps of adjacent production nodes is determined. The target relevance of each production node is determined based on the execution logic, and the target relevance is evaluated based on the preset relevance evaluation index to obtain the target relevance value; The target correlation value is compared with a preset correlation threshold. Based on the comparison result, production nodes whose target correlation value is less than the preset correlation threshold are identified as edge nodes, and production nodes are grouped based on edge nodes.

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