An industrial automation management and control system and method based on multi-sensor cooperation

Abstract

The application discloses an industrial automation management and control system and method based on multi-sensor cooperation, comprising the following steps: S1, when a multi-data acquisition device cooperative visual detection task is executed, a structured visual detection process description file is acquired, relates to the technical field of industrial automation detection, a delay probability distribution model is constructed for each data acquisition device, an invisible physical delay is changed into a quantifiable parameter, and adaptive time compensation of a logical time sequence instruction sequence is carried out according to an adaptive trigger time point based on the delay probability distribution model, mapping from ideal logical time sequence to real physical time sequence is realized, thereby effectively relieving the technical problem that logical time sequence and physical time sequence are not reproducible when cross-system deployment is caused by individual delay differences of hardware, and stability and consistency of a precision visual measurement process on different hardware are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial automation detection, and in particular to an industrial automation management and control system and method based on multi-sensor cooperation. BACKGROUND

[0002] In the industrial automation visual measurement of high-end precision components, the cooperative work of multi-sensors such as cameras and lasers needs to be strictly executed according to the preset logical time sequence. For example, a typical process may be "trigger camera A → delay 2ms → trigger laser B → read A, B data at the same time and fuse". At present, the industry usually uses a process description file to define and store the logical sequence. In the laboratory environment, the process is debugged without error on a specific hardware to generate a process description file, and is expected to be deployed to other "same model" hardware on the production line.

[0003] However, in actual execution, there are the following problems: there is an unreproducible drift between the logical time sequence and the physical time sequence. For example, the "delay 2ms" recorded in the process description file is a logical command, but the command is issued from the core control engine to the physical I / O card for actual execution, and the physical time consumption has a small but critical difference on different hardware individuals. This difference is due to the inherent characteristics of different hardware individuals, such as signal delay and drive response time, resulting in that the logical time sequence cannot be accurately reproduced in the physical time sequence when deployed across systems. For example, the logical "2ms" delay may actually take 1.9ms on hardware A, and 2.1ms on hardware B. For precision measurement tasks that require microsecond-level synchronization accuracy, this millisecond-level drift is enough to cause multi-sensor data fusion failure, resulting in unexplained and inconsistent measurement deviation. Therefore, the existing scheme only guarantees the logical correctness of the process, but cannot guarantee the consistency of the physical time sequence across hardware platforms. SUMMARY

[0004] To solve the technical problems in the background art, the present application proposes an industrial automation management and control system and method based on multi-sensor cooperation.

[0005] The industrial automation management and control method based on multi-sensor cooperation proposed by the present application comprises the following steps:

[0006] S1, when executing a visual detection task of multi-data acquisition equipment cooperation, a structured visual detection process description file is obtained; the structured visual detection process description file records a logical time sequence instruction sequence of multi-data acquisition equipment cooperative visual detection;

[0007] S2, extract the data acquisition device involved in the visual detection process from the logical time sequence instruction sequence, for each data acquisition device involved in the visual detection process, execute a delay modeling process to generate a delay probability distribution model corresponding to each data acquisition device;

[0008] S3, adaptively adjust the logical time sequence instruction sequence according to the delay probability distribution model to generate a data acquisition device execution sequence;

[0009] S4, execute a visual detection task with multiple data acquisition devices according to the data acquisition device execution sequence.

[0010] Preferably, in S1, the logical time sequence instruction sequence includes a plurality of instruction nodes arranged in execution order; the instruction nodes include action instructions and delay instructions; the action instructions are used to instruct the corresponding data acquisition device to perform a collection operation; and the delay instructions are used to represent the logical time interval between two adjacent action instructions.

[0011] Preferably, in S2, the delay modeling process is executed to generate a delay probability distribution model corresponding to each data acquisition device, wherein the delay modeling process for a data acquisition device as a target data acquisition device is as follows:

[0012] S21, the execution subject sends a synchronization instruction to the target data acquisition device and records the sending time T1;

[0013] S22, the target data acquisition device receives the synchronization instruction and records the receiving time T2; the target data acquisition device sends a response instruction after receiving the synchronization instruction, and records the sending time T3;

[0014] S23, the execution subject receives the response instruction and records the receiving time T4;

[0015] S24, repeat steps S21 to S23 for N times, N is an integer greater than 1; calculate a single path delay estimation value based on T1, T2, T3 and T4 obtained in each interaction;

[0016] S25, statistically analyze the single path delay estimation values obtained in N times to obtain the mean μ and the standard deviation σ of the single path delay estimation values; generate a delay probability distribution model of the target data acquisition device according to the mean μ and the standard deviation σ of the single path delay estimation values using a normal distribution, and the delay probability distribution model is used to evaluate the probability of different delay times occurring;

[0017] As an illustration, the execution subject includes any entity that implements this function, such as a CPU, a dedicated processor, a control engine, or a software module.

[0018] Preferably, the data acquisition device comprises an industrial camera and a laser.

[0019] Preferably, in S24, the single-path delay estimation value is calculated in the following manner: taking the difference between the receiving time T2 and the sending time T1 as the first transmission duration;

[0020] taking the difference between the receiving time T4 and the sending time T3 as the second transmission duration;

[0021] taking the arithmetic mean of the first transmission duration and the second transmission duration as the single-path delay estimation value;

[0022] For illustration, assuming that the single-path delay estimation value is D, the calculation formula of the single-path delay estimation value is:

[0023] D = [(T2-T1) + (T4-T3)] / 2.

[0024] Preferably, in S3, the logical timing instruction sequence is adaptively adjusted according to the delay probability distribution model to generate a data acquisition device execution sequence, as follows:

[0025] In step S31, sequentially traversing the logical timing instruction sequence, an action instruction, a delay instruction, and an action instruction successively appearing in the logical timing instruction sequence are constituted into an instruction unit group, and the following adjustment is performed on each instruction unit group:

[0026] the starting action instruction in the instruction unit group is taken as a predecessor action instruction, and the ending action instruction in the instruction unit group is taken as a successor action instruction;

[0027] In step S32, the execution time of the predecessor action instruction is obtained.

[0028] In step S33, the delay probability distribution model of the successor action device corresponding to the successor action instruction is obtained as a successor action device delay probability distribution model, and the mean and standard deviation of the successor action device delay probability distribution model are obtained.

[0029] In step S34, the adaptive trigger time point of the successor action instruction is calculated according to the execution time of the predecessor action instruction, the delay instruction, and the mean and standard deviation of the successor action device:

[0030] assuming that the adaptive trigger time point of the successor action instruction is t1, and the execution time of the predecessor action instruction is t0;

[0031] assuming that the mean of the successor action device is μ1 and the standard deviation of the successor action device is σ1;

[0032] the logical time interval between the predecessor action instruction and the successor action instruction in the delay instruction is t2;

[0033] t1=max(t0, t0+t2-(μ1+k·σ1)), wherein k is a preset coefficient, and k is in a range of 1 to 3;

[0034] As an illustration, since the single-path delay estimation value is a time amount calculated based on a timestamp difference value, the mean μ and the standard deviation σ are also in a time dimension, and thus the formula t1=t0+t2-(μ1+k·σ1) is dimensionally consistent;

[0035] In step S35, adaptive trigger time points corresponding to all successor action instructions and execution time points of all predecessor action instructions are obtained; and the data acquisition device sorts the adaptive trigger time points corresponding to the successor action instructions and the execution time points of the predecessor action instructions in time sequence to form a data acquisition device execution sequence.

[0036] Preferably, in S4, according to the data acquisition device execution sequence, a visual detection task of multiple data acquisition devices is executed, as follows:

[0037] According to the data acquisition device execution sequence;

[0038] At the execution time point of the predecessor action instruction, the predecessor action instruction is sent to the data acquisition device corresponding to the predecessor action instruction, and the data acquisition device corresponding to the predecessor action instruction outputs a detection result;

[0039] At the adaptive trigger time point, the successor action instruction is sent to the data acquisition device corresponding to the successor action instruction; and the data acquisition device corresponding to the successor action instruction outputs a detection result.

[0040] An industrial automation management and control system based on multi-sensor cooperation, comprising:

[0041] A graphical flow description file construction module: receiving, through a graphical user interface, an operation of a user dragging a tool node, and generating a structured flow description file according to the operation of the user dragging the tool node; the structured flow description file describes a logical time sequence instruction sequence of multi-data acquisition device cooperative visual detection, and a visual algorithm tool type required by the flow; the logical time sequence instruction sequence comprises a plurality of instruction nodes arranged in execution order; the instruction nodes comprise action instructions and delay instructions; the action instructions are used to instruct the corresponding data acquisition devices to perform acquisition operations; and the delay instructions are used to represent a logical time interval between two adjacent action instructions;

[0042] A dynamic loading modular algorithm tool module: according to the structured flow description file, a plurality of independent dynamic link library files stored in a preset directory are dynamically loaded through a reflection mechanism, and a control handle of the action instructions is instantiated for the corresponding data acquisition device instance;

[0043] The visual detection task execution module of the multi-data acquisition device cooperation: when executing the visual detection task of the multi-data acquisition device cooperation, the logical timing instruction sequence in the structured flow description file is acquired; the data acquisition device involved in the visual detection flow is extracted from the logical timing instruction sequence, and for each data acquisition device involved in the visual detection flow, a delay modeling flow is executed to generate a delay probability distribution model corresponding to each data acquisition device; the logical timing instruction sequence is adaptively adjusted according to the delay probability distribution model to generate a data acquisition device execution sequence; and the control handle of the data acquisition device is called according to the data acquisition device execution sequence to execute the visual detection task of the multi-data acquisition device cooperation.

[0044] Preferably, in the dynamic loading modular algorithm tool module, according to the structured flow description file, a plurality of independent dynamic link library files stored in a preset directory are dynamically loaded through a reflection mechanism, and a control handle of the data acquisition device corresponding to the action instruction is instantiated, as follows:

[0045] The plurality of independent dynamic link library files stored in the preset directory are dynamically loaded through the reflection mechanism by scanning the preset directory;

[0046] Whether each independent dynamic link library file has a predefined interface related to the control of the data acquisition device is verified through the reflection mechanism;

[0047] For the independent dynamic link library file having the predefined interface, a tool type identifier embedded therein is extracted through the reflection mechanism;

[0048] A mapping relationship between the tool type identifier and the corresponding independent dynamic link library file is established to form a mapping library;

[0049] According to the structured flow description file, a visual algorithm tool type required by the flow is obtained;

[0050] In the mapping library, a tool type identifier corresponding to the visual algorithm tool type required by the flow is matched to obtain an independent dynamic link library file corresponding to the visual algorithm tool type required by the flow as a required independent dynamic link library file;

[0051] The required independent dynamic link library file is instantiated to generate the control handle of the data acquisition device.

[0052] In the present application, the industrial automation management and control system and method based on multi-sensor cooperation have the following beneficial technical effects:

[0053] By constructing a delay probability distribution model for each data acquisition device, the invisible physical delay is changed into a quantifiable parameter, and based on the delay probability distribution model, adaptive time compensation is performed on the logical timing instruction sequence according to an adaptive trigger time point, mapping from ideal logical timing to real physical timing is realized, so that the.vsp file debugged in the laboratory can obtain highly consistent results on any same type hardware on the production line without re-debugging, thereby effectively alleviating the technical problem that logical timing and physical timing are not reproducible in cross-system deployment due to individual delay differences of hardware, and ensuring the stability and consistency of the precise visual measurement process on different hardware. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 is a principle block diagram of the system of the present application;

[0055] Figure 2 is a flowchart of the method of the present application. DETAILED DESCRIPTION

[0056] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar symbols represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as a limitation on the present application.

[0057] As Figure 1 shown in a kind of industrial automation management and control method based on multi-sensor cooperation, S1, when executing the visual inspection task of multiple data acquisition equipment cooperation, obtains structured visual inspection process description file;The structured visual inspection process description file describes the logical timing instruction sequence of multiple data acquisition equipment cooperative visual inspection;

[0058] S2, the data acquisition equipment involved in the visual inspection process is extracted from the logical timing instruction sequence, for each data acquisition equipment involved in the visual inspection process, delay modeling process is executed, to generate the delay probability distribution model corresponding to each data acquisition equipment;

[0059] S3, the logical timing instruction sequence is adaptively adjusted according to the delay probability distribution model, and the data acquisition equipment execution sequence is generated;

[0060] S4, according to the data acquisition equipment execution sequence, the visual inspection task of multiple data acquisition equipment cooperation is executed;

[0061] In an optional embodiment, in S1, the sequence of logical timing instructions comprises a plurality of instruction nodes arranged in an execution order; the instruction nodes comprise action instructions and delay instructions; the action instructions are used to instruct the corresponding data acquisition device to perform a collection operation; the delay instructions are used to represent a logical time interval between two adjacent action instructions;

[0062] In an optional embodiment, in S2, a delay modeling process is performed to generate a delay probability distribution model corresponding to each data acquisition device, wherein the delay modeling process for a data acquisition device as a target data acquisition device is as follows:

[0063] S21, the execution subject sends a synchronization instruction to the target data acquisition device and records the sending time T1;

[0064] S22, the target data acquisition device receives the synchronization instruction and records the receiving time T2; the target data acquisition device sends a response instruction after receiving the synchronization instruction, and records the sending time T3;

[0065] S23, the execution subject receives the response instruction and records the receiving time T4;

[0066] S24, steps S21 to S23 are repeated for N times, N is an integer greater than 1; based on T1, T2, T3 and T4 obtained in each interaction, a single-path delay estimation value is calculated;

[0067] In S24, the calculation method of the single-path delay estimation value is: taking the difference between the receiving time T2 and the sending time T1 as a first transmission time length;

[0068] Taking the difference between the receiving time T4 and the sending time T3 as a second transmission time length;

[0069] Taking the arithmetic mean of the first transmission time length and the second transmission time length as the single-path delay estimation value;

[0070] For illustration, assuming that the single-path delay estimation value is D, the calculation formula of the single-path delay estimation value is:

[0071] D = [(T2-T1)+(T4-T3)] / 2;

[0072] S25, statistical analysis is performed on the single-path delay estimation values obtained by N times of calculation to obtain the mean μ and the standard deviation σ of the single-path delay estimation value; according to the mean μ and the standard deviation σ of the single-path delay estimation value, a delay probability distribution model of the target data acquisition device is generated by using a normal distribution, and the delay probability distribution model is used to evaluate the probability of different delay times;

[0073] For illustration, the execution subject includes any entity implementing the function, such as a CPU, a dedicated processor, a control engine, a software module, etc.

[0074] The data acquisition device includes an industrial camera and a laser;

[0075] In an optional embodiment, in S3, the logical timing instruction sequence is adaptively adjusted according to the delay probability distribution model, to generate a data acquisition device execution sequence, as follows:

[0076] In S31, sequentially traverse the logical timing instruction sequence, and form an instruction unit group with a motion instruction, a delay instruction, and a motion instruction successively appearing in the logical timing instruction sequence, and perform the following adjustment on each instruction unit group:

[0077] Take the motion instruction at the beginning of the instruction unit group as a predecessor motion instruction, and take the motion instruction at the end of the instruction unit group as a successor motion instruction;

[0078] In S32, obtain the execution time of the predecessor motion instruction;

[0079] In S33, obtain the delay probability distribution model of the data acquisition device corresponding to the successor motion instruction, as a successor motion device delay probability distribution model, and obtain the mean and standard deviation of the successor motion device delay probability distribution model;

[0080] In S34, calculate the adaptive trigger time point of the successor motion instruction according to the execution time of the predecessor motion instruction, the delay instruction, the mean, and the standard deviation of the successor motion device:

[0081] Let the adaptive trigger time point of the successor motion instruction be t1, and the execution time of the predecessor motion instruction be t0;

[0082] Let the mean of the successor motion device be μ1, and the standard deviation be σ1;

[0083] The logical time interval between the predecessor motion instruction and the successor motion instruction in the delay instruction is t2;

[0084] t1=max(t0,t0+t2-(μ1+k·σ1)), where k is a preset coefficient, and the value range of k is 1 to 3;

[0085] For illustration, since the single-path delay estimation value is a time amount calculated based on the timestamp difference value, and the mean μ and the standard deviation σ are also in the dimension of time, the dimension of the formula t1=t0+t2-(μ1+k·σ1) is consistent;

[0086] Step S35, obtain the adaptive trigger time points corresponding to all the subsequent action instructions and the execution time of all the preceding action instructions; the data acquisition device sorts the adaptive trigger time points corresponding to the subsequent action instructions and the execution time of the preceding action instructions in time sequence to form a data acquisition device execution sequence;

[0087] In an optional embodiment, in S4, according to the data acquisition device execution sequence, a visual detection task is performed by multiple data acquisition devices in cooperation, as follows:

[0088] According to the data acquisition device execution sequence;

[0089] At the execution time of the preceding action instruction, the preceding action instruction is sent to the data acquisition device corresponding to the preceding action instruction, and the data acquisition device corresponding to the preceding action instruction outputs a detection result;

[0090] At the adaptive trigger time point, the subsequent action instruction is sent to the data acquisition device corresponding to the subsequent action instruction; the data acquisition device corresponding to the subsequent action instruction outputs a detection result.

[0091] By constructing a delay probability distribution model for each data acquisition device, the invisible physical delay is changed into a quantifiable parameter, and the logical time sequence instruction sequence is adaptively time compensated according to the adaptive trigger time point based on the delay probability distribution model, the mapping from the ideal logical time sequence to the real physical time sequence is realized, so that the.vsp file debugged in the laboratory can obtain highly consistent results on any same type hardware on the production line without re-debugging, thereby effectively alleviating the technical problem that the logical time sequence and the physical time sequence are not reproducible when deployed across systems due to the delay difference of individual hardware, and ensuring the stability and consistency of the precision visual measurement process on different hardware.

[0092] As shown in a kind of industrial automation management and control system based on multi-sensor cooperation shown in Figure 2 It includes:

[0093] The graphical process description file construction module receives the operation of user dragging tool nodes through a graphical user interface, generates a structured process description file according to the operation of user dragging tool nodes; the structured process description file describes the logical time sequence instruction sequence of multi-data acquisition device cooperative visual detection, the visual algorithm tool type required by the process; the logical time sequence instruction sequence includes a plurality of instruction nodes arranged in execution order; the instruction nodes include action instructions and delay instructions; the action instructions are used to instruct the corresponding data acquisition device to perform acquisition operations; the delay instructions are used to represent the logical time interval between two adjacent action instructions;

[0094] The dynamic loading modular algorithm tool module dynamically loads a plurality of independent dynamic link library files stored in a preset directory according to the structured process description file through a reflection mechanism, and instantiates a control handle of a data acquisition device corresponding to the action instruction;

[0095] The visual detection task execution module for multiple data acquisition devices cooperates to execute a visual detection task, acquires a logical timing instruction sequence in the structured process description file, extracts a data acquisition device involved in a visual detection process from the logical timing instruction sequence, executes a delay modeling process for each data acquisition device involved in the visual detection process to generate a delay probability distribution model corresponding to each data acquisition device, adaptively adjusts the logical timing instruction sequence according to the delay probability distribution model to generate a data acquisition device execution sequence, and calls the control handle of the data acquisition device according to the data acquisition device execution sequence to execute the visual detection task of the multiple data acquisition devices cooperated;

[0096] In an optional embodiment, in the dynamic loading modular algorithm tool module, a plurality of independent dynamic link library files stored in a preset directory are dynamically loaded according to the structured process description file through a reflection mechanism, and a control handle of a data acquisition device corresponding to the action instruction is instantiated, as follows:

[0097] The preset directory is scanned through the reflection mechanism, and a plurality of independent dynamic link library files stored in the preset directory are dynamically loaded;

[0098] It is verified through the reflection mechanism whether each independent dynamic link library file has a predefined interface related to data acquisition device control;

[0099] For the independent dynamic link library file having the predefined interface, a tool type identifier embedded therein is extracted through the reflection mechanism;

[0100] A mapping relationship between the tool type identifier and the corresponding independent dynamic link library file is established to form a mapping library;

[0101] According to the structured process description file, a visual algorithm tool type required by a process is obtained;

[0102] In the mapping library, a tool type identifier corresponding to the visual algorithm tool type required by the process is matched to obtain an independent dynamic link library file corresponding to the visual algorithm tool type required by the process as a required independent dynamic link library file;

[0103] The required independent dynamic link library file is instantiated to generate the control handle of the data acquisition device.

[0104] As an illustration, the acquisition of the present application refers to obtaining the required content or data using prior art means.

[0105] Meanwhile, the contents not described in detail in the present specification are all the prior art known to those skilled in the art.

[0106] In the embodiments provided by the present application, it should be understood that the disclosed system or method can be implemented in other manners. For example, the embodiments of the application described above are merely schematic, and the division of the modules is merely logical function division, and there can be another division manner in actual implementation.

[0107] The modules illustrated as separated components can or can not be physically separated, and the components illustrated as modules can or can not be physical modules, and can be located in one place or distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0108] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can be physically present alone, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of hardware plus software functional module.

[0109] It is obvious for those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and the present application can be implemented in other specific forms without departing from the essential characteristics of the present application.

[0110] The above description is merely a preferred embodiment of the present application, but the protection scope of the present application is not limited thereto, and any skilled person in the art can make equivalent replacements or changes to the technical solutions and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. An industrial automation management and control method based on multi-sensor cooperation, characterized in that, The method comprises the following steps: S1, when performing a visual inspection task in cooperation with multiple data acquisition devices, a structured visual inspection process description file is obtained; The structured visual inspection process description file describes a logical timing instruction sequence for visual inspection in cooperation with multiple data acquisition devices; S2, data acquisition devices involved in the visual inspection process are extracted from the logical timing instruction sequence, and for each data acquisition device involved in the visual inspection process, a delay modeling process is performed to generate a delay probability distribution model corresponding to each data acquisition device; S3, the logical timing instruction sequence is adaptively adjusted according to the delay probability distribution model to generate a data acquisition device execution sequence; S4, the visual inspection task in cooperation with multiple data acquisition devices is performed according to the data acquisition device execution sequence; In S2, the delay modeling process is performed to generate a delay probability distribution model corresponding to each data acquisition device, wherein for a delay modeling process taking one data acquisition device as a target data acquisition device, the following steps are performed: S21, the execution subject sends a synchronization instruction to the target data acquisition device and records the sending time T1; S22, the target data acquisition device receives the synchronization instruction and records the receiving time T2; after receiving the synchronization instruction, the target data acquisition device sends a response instruction and records the sending time T3; S23, the execution subject receives the response instruction and records the receiving time T4; S24, steps S21 to S23 are repeated N times, N is an integer greater than 1; based on T1, T2, T3, and T4 obtained in each interaction, a single-path delay estimation value is calculated; S25, statistical analysis is performed on the single-path delay estimation values calculated in N times to obtain the mean μ and the standard deviation σ of the single-path delay estimation values; according to the mean μ and the standard deviation σ of the single-path delay estimation values, a normal distribution is generated to generate a delay probability distribution model of the target data acquisition device, and the delay probability distribution model is used to evaluate the probability of different delay times occurring; In S24, the single-path delay estimation value is calculated as follows: the difference between the receiving time T2 and the sending time T1 is taken as the first transmission time; the difference between the receiving time T4 and the sending time T3 is taken as the second transmission time; the arithmetic mean of the first transmission time and the second transmission time is taken as the single-path delay estimation value.

2. The method for industrial automation management and control based on multi-sensor coordination according to claim 1, characterized in that, In S1, the logical timing instruction sequence comprises a plurality of instruction nodes arranged in execution order; the instruction nodes comprise action instructions and delay instructions; the action instructions are used to instruct the corresponding data acquisition devices to perform acquisition operations; the delay instructions are used to represent the logical time interval between two adjacent action instructions.

3. The method for industrial automation management and control based on multi-sensor coordination of claim 1, wherein, The data acquisition devices comprise industrial cameras and lasers.

4. The method for industrial automation management and control based on multi-sensor coordination of claim 2, wherein, In S3, the logical timing instruction sequence is adaptively adjusted according to the delay probability distribution model to generate a data acquisition device execution sequence, as follows: In step S31, the logical timing instruction sequence is sequentially traversed, the action instructions, delay instructions, and action instructions that appear continuously in the logical timing instruction sequence are combined to form an instruction unit group, and the following adjustments are performed on each instruction unit group: The action instruction at the beginning of the instruction unit group is regarded as a preceding action instruction, and the action instruction at the end of the instruction unit group is regarded as a subsequent action instruction; In step S32, the execution time of the preceding action instruction is obtained. In step S33, the delay probability distribution model of the data acquisition device corresponding to the subsequent action instruction is obtained as a subsequent action device delay probability distribution model; the mean and standard deviation of the subsequent action device delay probability distribution model are obtained. In step S34, the adaptive trigger time point of the subsequent action instruction is calculated according to the execution time of the preceding action instruction, the time delay instruction, the mean and standard deviation of the subsequent action device. In step S35, the adaptive trigger time points corresponding to all subsequent action instructions and the execution time of all preceding action instructions are obtained; the data acquisition device is sorted in time sequence according to the adaptive trigger time points corresponding to the subsequent action instructions and the execution time of the preceding action instructions to form a data acquisition device execution sequence.

5. The method for industrial automation management and control based on multi-sensor coordination of claim 4, wherein, In S4, the visual detection task of the multi-data acquisition device cooperation is executed according to the data acquisition device execution sequence, as follows: According to the data acquisition device execution sequence; At the execution time of the preceding action instruction, the preceding action instruction is sent to the data acquisition device corresponding to the preceding action instruction, and the detection result is output by the data acquisition device corresponding to the preceding action instruction; At the adaptive trigger time point, the subsequent action instruction is sent to the data acquisition device corresponding to the subsequent action instruction; the detection result is output by the data acquisition device corresponding to the subsequent action instruction.

6. An industrial automation management and control system based on multi-sensor collaboration, used for the industrial automation management and control method based on multi-sensor collaboration according to any one of claims 1 to 5, characterized in that, It comprises: A graphical flow description file construction module: receiving the operation of dragging the tool node by the user through the graphical user interface, and generating a structured flow description file according to the operation of dragging the tool node by the user; the structured flow description file describes the logical time sequence instruction sequence of the multi-data acquisition device cooperative visual detection, and the visual algorithm tool type required by the flow; the logical time sequence instruction sequence comprises a plurality of instruction nodes arranged in execution order; the instruction nodes comprise action instructions and time delay instructions; the action instructions are used to instruct the corresponding data acquisition device to execute the acquisition operation; the time delay instructions are used to represent the logical time interval between two adjacent action instructions; A dynamic loading modular algorithm tool module: according to the structured flow description file, a plurality of independent dynamic link library files stored in a preset directory are dynamically loaded through a reflection mechanism, and a control handle thereof is instantiated for the data acquisition device corresponding to the action instruction; The multi-data acquisition device cooperative visual detection task execution module: when executing the multi-data acquisition device cooperative visual detection task, acquires the logical timing instruction sequence in the structured flow description file; extracts the data acquisition device involved in the visual detection flow from the logical timing instruction sequence, and for each data acquisition device involved in the visual detection flow, executes the delay modeling flow to generate a delay probability distribution model corresponding to each data acquisition device; according to the delay probability distribution model, adaptively adjusts the logical timing instruction sequence to generate a data acquisition device execution sequence; and according to the data acquisition device execution sequence, calls the control handle of the data acquisition device to execute the multi-data acquisition device cooperative visual detection task.

7. The multi-sensor synergy based industrial automation management and control system, as claimed in claim 6, wherein, In the dynamic loading modular algorithm tool module, according to the structured flow description file, a plurality of independent dynamic link library files stored in a preset directory are dynamically loaded through a reflection mechanism, and a control handle of the data acquisition device corresponding to the action instruction is instantiated, as follows: The preset directory is scanned through the reflection mechanism, and a plurality of independent dynamic link library files stored in the preset directory are dynamically loaded; It is verified through the reflection mechanism whether each independent dynamic link library file has a predefined interface related to data acquisition device control; For the independent dynamic link library file having the predefined interface, the tool type identifier embedded therein is extracted through the reflection mechanism; A mapping relationship between the tool type identifier and the corresponding independent dynamic link library file is established to form a mapping library; According to the structured flow description file, the visual algorithm tool type required by the flow is obtained; In the mapping library, the tool type identifier corresponding to the visual algorithm tool type required by the flow is matched to obtain the independent dynamic link library file corresponding to the visual algorithm tool type required by the flow as a required independent dynamic link library file; The required independent dynamic link library file is instantiated to generate the control handle of the data acquisition device.

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