Modular detection robot cooperative control method based on reinforced concrete tower

Abstract

The invention relates to the technical field of reinforced concrete tower detection, in particular to a modular detection robot cooperative control method based on a reinforced concrete tower. Comprising the following steps: constructing a plurality of inspection sub-regions according to structural parameters of the reinforced concrete tower, and generating equipment sub-requirements of each inspection sub-region; generating an equipment distribution strategy according to all the equipment sub-demands, and generating a first-level control strategy of each inspection sub-region according to the equipment distribution strategy; acquiring a monitoring data packet of each inspection sub-region according to a preset feedback time node, and judging whether an early warning instruction is generated or not according to the monitoring data packet; dynamic division is performed according to equipment parameters of the reinforced concrete tower, a plurality of inspection subareas are constructed, corresponding detection robots are selected according to structure parameters of the inspection subareas, expected tracks and working parameters of the detection robots are set based on a path optimization technology, mutual collision is avoided, and all core areas of the reinforced concrete tower are covered. And the detection efficiency of the reinforced concrete tower is improved.

Description

Technical Field

[0001] The present application relates to the technical field of steel-concrete tower detection, and in particular to a modular detection robot collaborative control method based on steel-concrete tower. Background Art

[0002] Currently, steel-concrete tower inspections mainly rely on manual inspections or inspection robots. Traditional inspection robots are only equipped with a single sensor and require multiple equipment replacements to complete comprehensive inspections.

[0003] When multiple robots are working, there is a lack of an intelligent task allocation mechanism, which makes it easy for detection blind spots or repeated coverage to occur. Fixed-structure robots cannot flexibly adapt to different tower diameters and surface features (steel / concrete mixed materials). In the high-altitude strong electromagnetic interference environment, the data transmission packet loss rate between multiple robots is high. Summary of the Invention

[0004] The purpose of this application is: to solve the above technical problems, this application provides a modular inspection robot collaborative control method based on steel-concrete tower, aiming to improve the inspection efficiency of steel-concrete tower.

[0005] In some embodiments of the present application, dynamic division is performed according to the equipment parameters of the steel-concrete tower, multiple inspection sub-areas are constructed, and corresponding inspection robots are selected according to the structural parameters of each inspection sub-area. The expected trajectory and working parameters of each inspection robot are set based on path optimization technology to avoid mutual collisions and cover the entire core area of ​​the steel-concrete tower, thereby improving the inspection efficiency of the steel-concrete tower.

[0006] In some embodiments of the present application, by setting an anchor submodule in each inspection sub-area, a double-layer communication channel is constructed to ensure the data transmission quality of each detection robot and avoid the risk of packet loss, thereby improving the control efficiency of each detection robot.

[0007] In some embodiments of the present application, a collaborative control method for a modular inspection robot based on a steel-concrete tower is provided, comprising: Construct multiple inspection sub-areas based on the structural parameters of the steel-concrete tower and generate equipment sub-requirements for each inspection sub-area; Generate equipment allocation strategies based on all equipment sub-demands, and generate primary control strategies for each inspection sub-area based on the equipment allocation strategies; Obtain monitoring data packets for each inspection sub-area according to the preset feedback time node, and determine whether to generate an early warning instruction based on the monitoring data packets; When building multiple inspection sub-areas, it includes: Establish a sequence of inspection sub-areas A, A=(a1, a2…a i …a n ), where a iis the i-th inspection sub-area; n is the number of inspection sub-areas.

[0008] In some embodiments of the present application, generating a device allocation policy includes: Set a in sequence according to the inspection sub-area sequence A i is the target sub-region; Obtain the environmental data package and equipment sub-requirements of the target sub-area, and generate a demand evaluation value b of the target sub-area; Generate demand evaluation values ​​for each inspection sub-area in sequence; Establish a demand evaluation value series B, B=(b1, b2…b i …b n ), where b i is the demand evaluation value of the i-th inspection sub-area; Generate a first-level allocation order based on the demand evaluation value sequence B; Generate allocation sub-strategies for each inspection sub-area based on the first-level allocation order; Generate inspection evaluation values ​​for each inspection sub-area based on all allocation sub-strategies; Determine whether to generate a correction instruction based on all inspection evaluation values, and generate a device allocation strategy based on the correction results.

[0009] In some embodiments of the present application, generating the demand evaluation value b of the target sub-region includes: b=e1*Q1*[ η 1i *s i ]+e2*Q2*[ η 2i *w i ]; Among them, e1 is the first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of environmental characteristic indicators; η 1i is the influencing factor of the i-th environmental characteristic index; s i is the reference value of the i-th environmental characteristic index generated based on the environmental data package of the target sub-area; θ2 is the number of required characteristic indicators; η 2i is the influencing factor of the i-th demand characteristic indicator; w i It is the reference value of the i-th demand characteristic indicator generated based on the equipment sub-demand of the target sub-area.

[0010] In some embodiments of the present application, when generating the inspection evaluation value of each inspection sub-area, the following steps are included: Set a in sequence according to the inspection sub-area sequence A i is the sub-region to be evaluated; Obtain the allocation sub-strategy of the sub-region to be evaluated; Generate the inspection evaluation value c of the sub-region to be evaluated according to the preset simulation model and the allocation sub-strategy; c = β i * v i ; Among them, θ3 is the number of inspection evaluation indicators; β i is the influence factor of the i-th inspection evaluation indicator; v i is the reference value of the i-th inspection evaluation indicator in the sub-region to be evaluated generated based on the preset simulation model.

[0011] In some embodiments of the present application, when judging whether to generate a correction instruction according to all inspection evaluation values, it includes: Establish an inspection evaluation value sequence C, C = (c1, c2…c i …c n ), where c i is the inspection evaluation value of the i-th inspection sub-region Preset the inspection evaluation value threshold C1; If c i < C1, generate a first-level correction instruction for the allocation sub-strategy corresponding to the i-th inspection sub-region.

[0012] In some embodiments of the present application, when generating the first-level control strategy for each inspection sub-region, it includes: Set a i as the sub-region to be controlled in sequence according to the inspection sub-region sequence A; Generate a sequence of device sub-modules P for the sub-region to be controlled according to the device allocation strategy, P = (p1, p2…p i …p m ), where p i is the i-th device sub-module of the sub-region to be controlled; m is the number of device sub-modules in the sub-region to be controlled; Generate the expected operating trajectories of each device sub-module; Generate the operating evaluation values of each device sub-module according to all the expected operating trajectories; Establish an operating evaluation value sequence D, D = (d1, d2…d i …d m ), where d i is the operating evaluation value of the i-th device sub-module; Set the device sub-module corresponding to the maximum value d max in the operating evaluation value D as the anchor sub-module; Set the transmission sub-strategy of the sub-region to be controlled according to the anchor sub-module; Set the control sub-strategy of the sub-region to be controlled according to the expected operating trajectories and the transmission sub-strategy of each device sub-module; Generate control sub-strategies for each inspection sub-area in turn; Generate a first-level control strategy based on all control sub-strategies.

[0013] In some embodiments of the present application, generating the operation evaluation value of each device submodule includes: Set p in sequence according to the number of device submodules P i is the target submodule; Generate the running evaluation value d of the target submodule; d=e3*Q3*[ µ i *j i ]+e4*Q4*[ g i *k i ]; Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third weight coefficient; Q4 is the preset fourth weight coefficient; is the number of running evaluation indicators; µ i is the influencing factor of the i-th operation evaluation index; j i is the reference value of the i-th operation evaluation index in the target submodule; g i is the impact factor of the i-th equipment submodule; k i is the association evaluation value between the target submodule and the i-th device submodule.

[0014] In some embodiments of the present application, setting a transmission sub-strategy for a sub-area to be controlled includes: According to the equipment submodule sequence P, a primary transmission channel is established between each equipment submodule and the central control platform; Building a first transmission network based on all first-level transmission channels; Establish a secondary transmission channel between the anchor submodule and each device submodule; Building a second transmission network based on all secondary transmission channels; A transmission sub-strategy for the sub-area to be controlled is generated according to the first transmission network and the second transmission network.

[0015] In some embodiments of the present application, determining whether to generate an early warning instruction based on a monitoring data packet includes: Set a in sequence according to the inspection sub-area sequence A i is the sub-region to be diagnosed; Obtaining the monitoring data packet of the sub-area to be diagnosed at the current feedback time node; Generate risk assessment values ​​for each equipment submodule in the subarea to be diagnosed; Establish a risk assessment value series H, H=(h1, h2…h i …hm1 ), where h i is the risk assessment value of the i-th equipment submodule in the subregion to be diagnosed; m1 is the number of equipment submodules in the subregion to be diagnosed; Preset risk assessment value threshold H1; If h>H1, generate a first-level warning instruction for the i-th equipment submodule in the sub-area to be diagnosed; Generate early warning instructions in each inspection sub-area in turn.

[0016] In some embodiments of the present application, generating a risk assessment value for each device submodule in a sub-area to be diagnosed includes: Select the submodule to be diagnosed from all the device submodules in the positive subregion to be diagnosed in turn; Generate the risk assessment value h of the submodule to be diagnosed based on the monitoring data packet; h= r i *f i ]; in, is the number of risk characteristic indicators; r i is the influencing factor of the i-th risk characteristic indicator; f i It is the reference value of the i-th risk characteristic indicator in the sub-module to be diagnosed generated based on the monitoring data packet of the sub-area to be diagnosed.

[0017] Compared with the prior art, the modular inspection robot collaborative control method based on a steel-concrete tower in the embodiment of the present application has the following beneficial effects: According to the equipment parameters of the steel-concrete tower, dynamic division is carried out to construct multiple inspection sub-areas, and the corresponding inspection robots are selected according to the structural parameters of each inspection sub-area. The expected trajectory and working parameters of each inspection robot are set based on the path optimization technology to avoid mutual collisions and cover the entire core area of ​​the steel-concrete tower, thereby improving the inspection efficiency of the steel-concrete tower.

[0018] By setting up anchor submodules in each inspection sub-area and building a double-layer communication channel, the data transmission quality of each inspection robot is guaranteed, the risk of packet loss is avoided, and the control efficiency of each inspection robot is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a modular inspection robot collaborative control method based on a steel-concrete tower in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0020] The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0021] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0022] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. Throughout this application, unless otherwise specified, "plurality" means two or more.

[0023] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0024] like Figure 1 As shown, a modular inspection robot collaborative control method based on a steel-concrete tower according to a preferred embodiment of the present application includes: S101: Construct multiple inspection sub-areas based on the structural parameters of the steel-concrete tower, and generate equipment sub-requirements for each inspection sub-area; S102: Generate an equipment allocation strategy based on all equipment sub-demands, and generate a primary control strategy for each inspection sub-area based on the equipment allocation strategy; S103: Obtain monitoring data packets of each inspection sub-area according to a preset feedback time node, and determine whether to generate an early warning instruction based on the monitoring data packets; When building multiple inspection sub-areas, it includes: Establish a sequence of inspection sub-areas A, A=(a1, a2…a i …a n ), where a i is the i-th inspection sub-area; n is the number of inspection sub-areas.

[0025] Specifically, the steel-concrete tower is divided into different inspection sub-areas through dynamic division, and appropriate inspection robots are selected for inspection according to the inspection requirements of each inspection sub-area.

[0026] Specifically, when generating a device allocation policy, include: Set a in sequence according to the inspection sub-area sequence A i is the target sub-region; Obtain the environmental data package and equipment sub-requirements of the target sub-area, and generate a demand evaluation value b of the target sub-area; Generate demand evaluation values ​​for each inspection sub-area in sequence; Establish a demand evaluation value series B, B=(b1, b2…b i …b n ), where b i is the demand evaluation value of the i-th inspection sub-area; Generate a first-level allocation order based on the demand evaluation value sequence B; Generate allocation sub-strategies for each inspection sub-area based on the first-level allocation order; Generate inspection evaluation values ​​for each inspection sub-area based on all allocation sub-strategies; Determine whether to generate a correction instruction based on all inspection evaluation values, and generate a device allocation strategy based on the correction results.

[0027] Specifically, based on the inspection tasks that need to be performed in the target sub-area and combined with historical data analysis, corresponding equipment sub-requirements are generated, and the equipment sub-requirements include parameters such as the required equipment type and expected usage time.

[0028] Specifically, the greater the demand evaluation value, the more difficult it is to inspect the inspection sub-area, and the higher the corresponding allocation order. This allows inspection robots in good operating condition to be selected to inspect the sub-area, improving the overall inspection efficiency of the steel-concrete tower.

[0029] Specifically, an equipment library is established, which includes all detection robots (climbing robots, drones, ground mobile robots, etc.), and the status value of each detection robot is set according to its historical operating parameters. The higher the status value of the detection robot, the better the operating status of the corresponding equipment, and the lower the possibility of operational failure during the detection process.

[0030] Specifically, the equipment library allocates inspection robots to each inspection sub-area in sequence according to the first-level allocation order, and prioritizes the deployment of inspection robots with high status values ​​based on the equipment type and quantity required by the inspection sub-area. The allocation is cyclical until all inspection robots are allocated or all inspection sub-areas obtain corresponding inspection robots, and the allocation sub-strategy for each inspection sub-area is generated based on the allocation results.

[0031] Specifically, when generating the demand evaluation value b of the target sub-region, it includes: b=e1*Q1*[ η 1i *s i ]+e2*Q2*[ η 2i *w i ]; Among them, e1 is the first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of environmental characteristic indicators; η 1i is the influencing factor of the i-th environmental characteristic index; s i is the reference value of the i-th environmental characteristic index generated based on the environmental data package of the target sub-area; θ2 is the number of required characteristic indicators; η 2i is the influencing factor of the i-th demand characteristic indicator; w i It is the reference value of the i-th demand characteristic indicator generated based on the equipment sub-demand of the target sub-area.

[0032] Specifically, the environmental characteristic indicators include the required detection area, the complexity of the steel frame structure in the inspection sub-area, the degree of signal interference in the inspection sub-area and other parameters. By quantifying each environmental characteristic indicator, accurate analysis of the detection tasks in the inspection sub-area can be achieved. The influencing factors of each environmental characteristic indicator can be set according to the degree of interference with the normal operation of the detection robot. The greater the interference degree, the greater the corresponding influencing factor.

[0033] Specifically, the larger the reference value of each environmental characteristic indicator, the more difficult it is to perform the detection task in the target sub-area.

[0034] Specifically, demand characteristic indicators include parameters such as the number of devices required, the number of device types (the more different types of inspection robots, the greater the difficulty of coordination), and expected inspection time. By quantifying each demand characteristic indicator, we can accurately analyze the difficulty of inspection tasks within the inspection sub-area. The larger the reference value of each demand characteristic indicator, the more difficult it is to execute the inspection task in the target sub-area.

[0035] Specifically, each parameter in the model is normalized by presetting a first fixed coefficient and a second fixed coefficient, so that each parameter in the model is within the same value range.

[0036] It can be understood that in the above embodiments, dynamic division is performed according to the equipment parameters of the steel-concrete tower, multiple inspection sub-areas are constructed, corresponding inspection robots are selected according to the structural parameters of each inspection sub-area, and the expected trajectories and working parameters of each inspection robot are set based on path optimization technology to avoid collisions with each other and cover all the core areas of the steel-concrete tower, thereby improving the inspection efficiency of the steel-concrete tower.

[0037] In the preferred embodiment of the embodiment of the present application, when generating the inspection evaluation value of each inspection sub-area, it includes: Set a i as the sub-area to be evaluated in sequence according to the inspection sub-area sequence A; Obtain the assigned sub-strategy of the sub-area to be evaluated; Generate the inspection evaluation value c of the sub-area to be evaluated according to the preset simulation model and the assigned sub-strategy; c = β i *v i ; Where, θ3 is the number of inspection evaluation indicators; β i is the influence factor of the i-th inspection evaluation indicator; v i is the reference value of the i-th inspection evaluation indicator in the sub-area to be evaluated generated based on the preset simulation model.

[0038] Specifically, when judging whether to generate a correction instruction according to all the inspection evaluation values, it includes: Establish an inspection evaluation value sequence C, C = (c1, c2... c i ... c n ), where, c i is the inspection evaluation value of the i-th inspection sub-area Preset an inspection evaluation value threshold C1; If c i < C1, generate a first-level correction instruction for the assigned sub-strategy corresponding to the i-th inspection sub-area.

[0039] Specifically, the larger the inspection evaluation value is, the better the effect of the relevant inspection robot in the assigned sub-strategy performing the inspection task in the current sub-area to be evaluated.

[0040] Specifically, a simulation model is constructed based on historical data, the equipment parameters in the assigned sub-strategy in the sub-area to be evaluated are processed to generate a simulation data packet, and the expected simulation result is generated according to the simulation model and the simulation data packet, so as to predict the execution effect of the inspection task in the sub-area to be evaluated.

[0041] Specifically, when the inspection evaluation value is lower than the preset inspection evaluation value threshold, it means that the detection robot in the current allocation sub-strategy of the inspection sub-area cannot complete the inspection task of the inspection sub-area and needs to be adjusted in time.

[0042] Specifically, the inspection robots in the inspection sub-areas are adjusted according to the first-level correction instructions. The adjustment method includes adding some inspection robots and partially exchanging them with the inspection robots in good operating condition in other inspection sub-areas until the inspection evaluation values ​​of all inspection sub-areas are greater than the inspection evaluation value threshold.

[0043] In a preferred embodiment of the present application, when generating the primary control strategy for each inspection sub-area, the following steps are included: Set a in sequence according to the inspection sub-area sequence A i is the sub-area to be controlled; Generate the equipment submodule sequence P of the sub-area to be controlled according to the equipment allocation strategy, P=(p1,p2…p i …p m ), where p i is the i-th equipment submodule in the sub-area to be controlled; m is the number of equipment submodules in the sub-area to be controlled; Generate the expected operation trajectory of each equipment submodule; Generate the operation evaluation value of each equipment submodule based on all expected operation trajectories; Establish the running evaluation value series D, D=(d1, d2…d i …d m ), where d i is the operation evaluation value of the i-th equipment submodule; Set the maximum value d among the operation evaluation values ​​D max The corresponding device submodule is the anchor submodule; Setting a transmission sub-strategy for the sub-area to be controlled according to the anchor sub-module; Set the control sub-strategy for the sub-area to be controlled based on the expected operation trajectory and transmission sub-strategy of each equipment sub-module; Generate control sub-strategies for each inspection sub-area in turn; Generate a first-level control strategy based on all control sub-strategies.

[0044] Specifically, the equipment sub-modules include but are not limited to climbing robots, drones, ground mobile robots, etc.

[0045] Specifically, the corresponding expected operation trajectory is generated according to the detection tasks that each device sub-module needs to perform.

[0046] Specifically, the larger the operation evaluation value is, the better the operation status of the corresponding device sub-module is, and the easier it is to communicate with the remaining device sub-modules.

[0047] Specifically, when generating the operational evaluation value of each device submodule, it includes: Set p in sequence according to the number of device submodules P i is the target submodule; Generate the running evaluation value d of the target submodule; d=e3*Q3*[ µ i *j i ]+e4*Q4*[ g i *k i ]; Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third weight coefficient; Q4 is the preset fourth weight coefficient; is the number of running evaluation indicators; µ i is the influencing factor of the i-th operation evaluation index; j i is the reference value of the i-th operation evaluation index in the target submodule; g i is the impact factor of the i-th equipment submodule; k i is the association evaluation value between the target submodule and the i-th device submodule.

[0048] Specifically, the operational evaluation indicators include, but are not limited to, the likelihood of equipment being subject to communication interference, the equipment's failure rate, and the difficulty of detecting the detection area. By quantifying each operational evaluation indicator, the operational status of the target submodule is accurately evaluated. The better the target operational status, the greater the reference value of the corresponding operational evaluation indicator. The impact factor of each operational evaluation indicator can be set based on the degree of interference it causes to the task execution process. The greater the interference, the larger the corresponding impact factor.

[0049] Specifically, the association evaluation value can be set according to the average distance between the expected operating trajectory of the target submodule and the expected operating trajectory of the corresponding equipment submodule. The smaller the average distance, the larger the corresponding association evaluation value. The influence factor of each equipment submodule can be set according to the difficulty of executing the detection subtask. The greater the difficulty, the greater the corresponding influence factor.

[0050] Specifically, each parameter in the model is normalized by presetting the third fixed coefficient and the fourth fixed coefficient, so that each parameter in the model is in the same value range.

[0051] Specifically, when setting the transmission sub-strategy for the sub-area to be controlled, it includes: According to the equipment submodule sequence P, a primary transmission channel is established between each equipment submodule and the central control platform; Building a first transmission network based on all first-level transmission channels; Establish a secondary transmission channel between the anchor submodule and each device submodule; Building a second transmission network based on all secondary transmission channels; A transmission sub-strategy for the sub-area to be controlled is generated according to the first transmission network and the second transmission network.

[0052] Specifically, the central control platform communicates with each inspection robot via a first transmission network. Transmission data from each inspection robot is directly sent to the central control platform. A second transmission network connects each inspection robot to the anchoring submodule, where data is transmitted to the anchoring submodule, which then transmits it to the central control platform. This two-tiered transmission network allows for cross-verification of data while preventing packet loss due to signal disturbances.

[0053] It can be understood that in the above embodiment, by setting anchor submodules in each inspection sub-area, a double-layer communication channel is constructed to ensure the data transmission quality of each detection robot and avoid the risk of packet loss, thereby improving the control efficiency of each detection robot.

[0054] In a preferred embodiment of the present application, determining whether to generate an early warning instruction based on a monitoring data packet includes: Set a in sequence according to the inspection sub-area sequence A i is the sub-region to be diagnosed; Obtaining the monitoring data packet of the sub-area to be diagnosed at the current feedback time node; Generate risk assessment values ​​for each equipment submodule in the subarea to be diagnosed; Establish a risk assessment value series H, H=(h1, h2…h i …h m1 ), where h i is the risk assessment value of the i-th equipment submodule in the subregion to be diagnosed; m1 is the number of equipment submodules in the subregion to be diagnosed; Preset risk assessment value threshold H1; If h>H1, generate a first-level warning instruction for the i-th equipment submodule in the sub-area to be diagnosed; Generate early warning instructions in each inspection sub-area in turn.

[0055] Specifically, the risk assessment value threshold can be set based on historical parameters.

[0056] Specifically, when generating the risk assessment value of each equipment submodule in the sub-area to be diagnosed, it includes: Select the submodule to be diagnosed from all the device submodules in the positive subregion to be diagnosed in turn; Generate the risk assessment value h of the submodule to be diagnosed based on the monitoring data packet; h= r i *f i ]; in, is the number of risk characteristic indicators; r i is the influencing factor of the i-th risk characteristic indicator; f i It is the reference value of the i-th risk characteristic indicator in the sub-module to be diagnosed generated based on the monitoring data packet of the sub-area to be diagnosed.

[0057] Specifically, risk characteristic indicators include but are not limited to abnormal equipment operating temperature, abnormal equipment communication, data loss degree and other parameters. The larger the reference value of each risk characteristic indicator, the greater the possibility that the current submodule to be diagnosed has abnormal operation.

[0058] Specifically, when the risk assessment value of the submodule to be diagnosed is greater than the risk assessment value threshold, it means that the submodule to be diagnosed cannot complete the current detection task, and it is necessary to replace the new equipment in time, or adjust the working parameters of the current remaining equipment to complete the detection task of the submodule to be diagnosed.

[0059] According to the first concept of the present application, dynamic division is performed according to the equipment parameters of the steel-concrete tower, multiple inspection sub-areas are constructed, and corresponding inspection robots are selected according to the structural parameters of each inspection sub-area. The expected trajectory and working parameters of each inspection robot are set based on the path optimization technology to avoid mutual collisions and cover the entire core area of ​​the steel-concrete tower, thereby improving the inspection efficiency of the steel-concrete tower.

[0060] According to the second concept of the present application, by setting up anchor submodules in each inspection sub-area, a double-layer communication channel is constructed to ensure the data transmission quality of each detection robot and avoid the risk of packet loss, thereby improving the control efficiency of each detection robot.

[0061] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and replacements can be made without departing from the technical principles of the present application. These improvements and replacements should also be regarded as the scope of protection of the present application.

Claims

1. A modular inspection robot collaborative control method based on a steel-concrete tower, characterized in that: include: Construct multiple inspection sub-areas based on the structural parameters of the steel-concrete tower and generate equipment sub-requirements for each inspection sub-area; Generate equipment allocation strategies based on all equipment sub-demands, and generate primary control strategies for each inspection sub-area based on the equipment allocation strategies; Obtain monitoring data packets for each inspection sub-area according to the preset feedback time node, and determine whether to generate an early warning instruction based on the monitoring data packets; When building multiple inspection sub-areas, it includes: Establish a sequence of inspection sub-areas A, A=(a1, a2…a i …a n ), where a i is the i-th inspection sub-area; n is the number of inspection sub-areas.

2. The modular inspection robot collaborative control method based on a steel-concrete tower according to claim 1, characterized in that: The generating of the device allocation strategy includes: Set a in sequence according to the inspection sub-area sequence A i is the target sub-region; Obtain the environmental data package and equipment sub-requirements of the target sub-area, and generate a demand evaluation value b of the target sub-area; Generate demand evaluation values ​​for each inspection sub-area in sequence; Establish a demand evaluation value series B, B=(b1, b2…b i …b n ), where b i is the demand evaluation value of the i-th inspection sub-area; Generate a first-level allocation order based on the demand evaluation value sequence B; Generate allocation sub-strategies for each inspection sub-area based on the first-level allocation order; Generate inspection evaluation values ​​for each inspection sub-area based on all allocation sub-strategies; Determine whether to generate a correction instruction based on all inspection evaluation values, and generate a device allocation strategy based on the correction results.

3. The modular inspection robot collaborative control method based on a steel-concrete tower according to claim 2, characterized in that: When generating the demand evaluation value b of the target sub-area, it includes: b=e1*Q1*[ or 1i *s i ]+e2*Q2*[ or 2i *w i ]; Among them, e1 is the first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of environmental characteristic indicators; η 1i is the influencing factor of the i-th environmental characteristic index; s i is the reference value of the i-th environmental characteristic index generated based on the environmental data package of the target sub-area; θ2 is the number of required characteristic indicators; η 2i is the influencing factor of the i-th demand characteristic indicator; w i It is the reference value of the i-th demand characteristic indicator generated based on the equipment sub-demand of the target sub-area.

4. The collaborative control method of modular inspection robots based on steel-concrete towers according to claim 2, characterized in that: When generating inspection evaluation values ​​for each inspection sub-area, the following are included: Set a in sequence according to the inspection sub-area sequence A i is the sub-region to be evaluated; Obtain the allocation sub-strategy of the sub-region to be evaluated; Generate inspection evaluation value c of the sub-area to be evaluated based on the preset simulation model and allocation sub-strategy; c= b i *v i ]; Among them, θ3 is the number of inspection evaluation indicators; β i is the influencing factor of the i-th inspection evaluation index; v i It is the reference value of the i-th inspection evaluation index in the sub-area to be evaluated based on the preset simulation model.

5. The collaborative control method of modular inspection robots based on steel-concrete towers according to claim 4, characterized in that: When determining whether to generate a correction instruction based on all inspection evaluation values, it includes: Establish inspection evaluation value sequence C, C=(c1,c2…c i …c n ), where c i is the inspection evaluation value of the i-th inspection sub-area Preset inspection evaluation value threshold C1; If c i <C1, generate a first-level correction instruction for the allocation sub-strategy corresponding to the i-th inspection sub-region.

6. The collaborative control method of modular inspection robots based on steel-concrete towers according to claim 2, characterized in that: When generating the primary control strategy for each inspection sub-area, it includes: Set a in sequence according to the inspection sub-area sequence A i is the sub-area to be controlled; Generate the equipment submodule sequence P of the sub-area to be controlled according to the equipment allocation strategy, P=(p1,p2…p i …p m ), where p i is the i-th equipment submodule in the sub-area to be controlled; m is the number of equipment submodules in the sub-area to be controlled; Generate the expected operation trajectory of each equipment submodule; Generate the operation evaluation value of each equipment submodule based on all expected operation trajectories; Establish the running evaluation value series D, D=(d1, d2…d i …d m ), where d i is the operation evaluation value of the i-th equipment submodule; Set the maximum value d among the operation evaluation values ​​D max The corresponding device submodule is the anchor submodule; Setting a transmission sub-strategy for the sub-area to be controlled according to the anchor sub-module; Set the control sub-strategy for the sub-area to be controlled based on the expected operation trajectory and transmission sub-strategy of each equipment sub-module; Generate control sub-strategies for each inspection sub-area in turn; Generate a first-level control strategy based on all control sub-strategies.

7. The collaborative control method of modular inspection robots based on steel-concrete towers according to claim 6, characterized in that: When generating the operational evaluation values ​​of each device submodule, it includes: Set p in sequence according to the number of device submodules P i is the target submodule; Generate the running evaluation value d of the target submodule; d=e3*Q3*[ µ i *j i ]+e4*Q4*[ g i *k i ]; Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third weight coefficient; Q4 is the preset fourth weight coefficient; is the number of running evaluation indicators; µ i is the influencing factor of the i-th operation evaluation index; j i is the reference value of the i-th operation evaluation index in the target submodule; g i is the impact factor of the i-th equipment submodule; k i is the association evaluation value between the target submodule and the i-th device submodule.

8. The collaborative control method of modular inspection robots based on steel-concrete towers according to claim 6, characterized in that: When setting the transmission sub-policy for the sub-area to be controlled, include: According to the equipment submodule sequence P, a primary transmission channel is established between each equipment submodule and the central control platform; Building a first transmission network based on all first-level transmission channels; Establish a secondary transmission channel between the anchor submodule and each device submodule; Building a second transmission network based on all secondary transmission channels; A transmission sub-strategy for the sub-area to be controlled is generated according to the first transmission network and the second transmission network.

9. The collaborative control method of modular inspection robots based on steel-concrete towers according to claim 6, characterized in that: When determining whether to generate an early warning instruction based on the monitoring data packet, it includes: Set a in sequence according to the inspection sub-area sequence A i is the sub-region to be diagnosed; Obtaining the monitoring data packet of the sub-area to be diagnosed at the current feedback time node; Generate risk assessment values ​​for each equipment submodule in the subarea to be diagnosed; Establish a risk assessment value series H, H=(h1, h2…h i …h m1 ), where h i is the risk assessment value of the i-th equipment submodule in the subregion to be diagnosed; m1 is the number of equipment submodules in the subregion to be diagnosed; Preset risk assessment value threshold H1; If h>H1, generate a first-level warning instruction for the i-th equipment submodule in the sub-area to be diagnosed; Generate early warning instructions in each inspection sub-area in turn.

10. The collaborative control method of modular inspection robots based on steel-concrete towers according to claim 7, characterized in that: When generating the risk assessment value of each equipment submodule in the sub-area to be diagnosed, it includes: Select the submodule to be diagnosed from all the device submodules in the positive subregion to be diagnosed in turn; Generate the risk assessment value h of the submodule to be diagnosed based on the monitoring data packet; h= r i *f i ]; in, is the number of risk characteristic indicators; r i is the influencing factor of the i-th risk characteristic indicator; f i It is the reference value of the i-th risk characteristic indicator in the sub-module to be diagnosed generated based on the monitoring data packet of the sub-area to be diagnosed.

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