Cooperative scheduling method and device of robot and refrigeration equipment in target area

Abstract

The application provides a method and device for cooperative scheduling of robots and refrigeration equipment in a target area, which can be applied to the fields of financial technology and Internet of Things technology. The method comprises the following steps: determining the operation parameters of the robots in the target cold aisle, the occupation parameters of the robots in the target cold aisle, the environmental parameters in the target area, and the refrigeration parameters of the refrigeration equipment; calculating a first congestion factor according to the occupation parameters and the operation parameters, and calculating a second congestion factor according to the operation parameters, the refrigeration parameters and the environmental parameters; in response to the successful calculation of the first congestion factor and the second congestion factor, performing first fusion calculation according to the first congestion factor and the second congestion factor to obtain a congestion risk value, determining and executing a scheduling strategy for the robots in the target cold aisle based on the congestion risk value, and adjusting the refrigeration equipment based on the congestion risk value.

Description

Technical Field

[0001] This application relates to the fields of financial technology and Internet of Things (IoT), and more specifically to a method, apparatus, device, medium, and product for the coordinated scheduling of robots and refrigeration equipment within a target area. Background Technology

[0002] Data centers in the financial industry typically feature enclosed cold aisle layouts for their server rooms. Cooling equipment delivers cold air into the aisle, with multiple server racks positioned on either side. The racks usually face the aisle, and the cool air enters through the rack inlets to dissipate heat from devices such as servers. Meanwhile, existing data centers increasingly utilize robots for equipment inspection and maintenance, sometimes with multiple robots operating simultaneously within the same cold aisle area. For example, a 10-square-meter cold aisle might have one floor cleaning robot, two inspection robots, and three maintenance robots operating concurrently. Existing technologies have the following drawbacks: 1) Limited cold aisle space can lead to congestion and reduced robot efficiency due to excessive robot activity; 2) Continuous robot operation releases heat into the environment, causing localized temperature increases within the aisle and affecting server cooling; 3) Too many robots can obstruct and interfere with airflow within the cold aisle, reducing airflow to servers and consequently impacting cooling performance. Summary of the Invention

[0003] In view of the above problems, embodiments of this application provide a method, apparatus, device, medium, and product for the coordinated scheduling of robots and refrigeration equipment within a target area.

[0004] According to a first aspect of this application, a method for collaborative scheduling of robots and refrigeration equipment within a target area is provided. The target area includes a target cold aisle and target cabinets affected by the target cold aisle. The target cold aisle is used to connect with the air outlet of the refrigeration equipment and also to accommodate robots. The method includes: determining operating parameters of the robot within the target cold aisle, occupancy parameters of the robot within the target cold aisle, environmental parameters in the target area, and refrigeration parameters of the refrigeration equipment; calculating a first congestion factor based on the occupancy parameters and the operating parameters, and calculating a second congestion factor based on the operating parameters, the refrigeration parameters, and the environmental parameters; responding to successful calculation of the first congestion factor and the second congestion factor, performing a first fusion calculation based on the first congestion factor and the second congestion factor to obtain a congestion risk value; determining and executing a scheduling strategy for the robot within the target cold aisle based on the congestion risk value; and adjusting the refrigeration equipment based on the congestion risk value.

[0005] In one embodiment of this application, the occupancy parameters include the projected area of ​​the robot on the ground of the target cold aisle and the number of robot bodies in the target cold aisle; the operating parameters include the moving speed of the robot in the target cold aisle; the calculation of the first congestion factor based on the occupancy parameters and the operating parameters includes: calculating the space occupancy rate of the robot in the target cold aisle based on the projected area and the aisle ground area; calculating the speed dispersion of the robot in the target cold aisle based on the moving speed and the number of robot bodies; calculating the motion disorder of the robot in the target cold aisle based on the speed dispersion and a first preset speed threshold; and performing a second fusion calculation based on the space occupancy rate and the motion disorder to obtain the first congestion factor.

[0006] In one embodiment of this application, the operating parameters further include the robot's operating power, the environmental parameters include the actual airflow velocity at the air inlet of the target cabinet and the air inlet temperature of the target cabinet, the cooling parameters include the set temperature of the cooling equipment, and the calculation of the second congestion factor based on the operating parameters, the cooling parameters, and the environmental parameters includes: calculating the airflow blockage degree based on the actual airflow velocity, the preset airflow velocity, and the attenuation coefficient, wherein the attenuation coefficient is calculated based on the number of units and the cross-sectional area of ​​the target cold aisle; calculating the temperature rise degree based on the air inlet temperature, the set temperature, and a first preset temperature rise threshold; calculating the heat source intensity based on the operating power and a first energy conversion coefficient; and calculating the second congestion factor based on the airflow blockage degree, the temperature rise degree, and the heat source intensity.

[0007] In one embodiment of this application, determining and executing a scheduling strategy for robots within the target cold aisle based on the congestion risk value includes: in response to the congestion risk value being within a first risk threshold range, determining and executing a first scheduling strategy as the scheduling strategy, the first scheduling strategy including: further determining the operating parameters, the occupancy parameters, the environmental parameters, and the cooling parameters; in response to the congestion risk value being within a second risk threshold range, determining and executing a second scheduling strategy as the scheduling strategy, the second scheduling strategy including: prohibiting robots outside the target cold aisle from entering the target cold aisle, adjusting the movement paths of robots within the target cold aisle, and sending a first airflow speed increase request to the cooling equipment for the air outlet connected to the target cold aisle; in response to the congestion risk value being within a third risk threshold range, determining and executing a second scheduling strategy as the scheduling strategy. Based on the risk threshold range, a third scheduling strategy is determined and executed. The third scheduling strategy includes: sending an emergency cooling request to the refrigeration equipment and repeatedly performing evacuation operations until the evacuation termination conditions are met. The evacuation operations include: obtaining a priority list of tasks performed by robots within the target cold aisle; determining low-priority tasks based on the priority list; issuing a pause task command and a withdrawal command from the cold aisle to the robots performing the low-priority tasks; re-determining the operating parameters, the occupancy parameters, the environmental parameters, and the refrigeration parameters; and updating the congestion risk value. The evacuation termination conditions include at least one of the following: the congestion risk value is less than the minimum value of the third risk threshold range; the low-priority task does not exist in the priority list; and the number of robots within the target cold aisle is less than a first robot number threshold.

[0008] In one embodiment of this application, adjusting the motion path of the robot within the target cold aisle includes: determining the temperature gradient field within the target cold aisle; calculating an avoidance vector based on an avoidance algorithm; calculating a thermal repulsion velocity component based on the temperature gradient field and the temperature gradient weighting coefficient; calculating a control velocity vector of the robot within the target cold aisle based on the avoidance vector and the thermal repulsion velocity component; and adjusting the motion path of the robot within the target cold aisle based on the control velocity vector.

[0009] In one embodiment of this application, adjusting the refrigeration equipment based on the congestion risk value includes: calculating a target opening degree based on the congestion risk value and a reference opening degree of the air valve of the refrigeration equipment, wherein the target opening degree is positively correlated with the congestion risk value; and adjusting the opening degree of the air valve of the refrigeration equipment based on the target opening degree.

[0010] In one embodiment of this application, the method further includes: adjusting the refrigeration equipment according to the second congestion factor in response to the failure of calculating only the first congestion factor; and determining and executing a fourth scheduling strategy as the scheduling strategy according to the first congestion factor in response to the failure of calculating only the second congestion factor, the fourth scheduling strategy including: calculating an avoidance vector according to an avoidance algorithm, and adjusting the movement path of the robot in the target cold aisle based on the avoidance vector.

[0011] According to a second aspect of this application, a collaborative scheduling device for robots and refrigeration equipment within a target area is provided. The target area includes a target cold aisle and target cabinets affected by the target cold aisle. The target cold aisle is used to connect with the air outlet of the refrigeration equipment and also to accommodate robots. The device includes: a determining module, used to determine the operating parameters of the robot in the target cold aisle, the occupancy parameters of the robot in the target cold aisle, environmental parameters in the target area, and refrigeration parameters of the refrigeration equipment; a calculating module, used to calculate a first congestion factor based on the occupancy parameters and the operating parameters, and calculate a second congestion factor based on the operating parameters, the refrigeration parameters, and the environmental parameters; and an execution module, used to, in response to the successful calculation of the first congestion factor and the second congestion factor, perform a first fusion calculation based on the first congestion factor and the second congestion factor to obtain a congestion risk value, determine and execute a scheduling strategy for the robot in the target cold aisle based on the congestion risk value, and adjust the refrigeration equipment based on the congestion risk value.

[0012] According to a third aspect of this application, an electronic device is provided, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0013] According to a fourth aspect of this application, a computer-readable storage medium is also provided, on which a computer program or instructions are stored, wherein the computer program or instructions, when executed by a processor, implement the steps of the above-described method.

[0014] According to a fifth aspect of this application, a computer program product is also provided, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0015] The above-described one or more embodiments have the following advantages or beneficial effects: Through the collaborative scheduling method for robots and cooling equipment within the target area according to the above embodiments of this application, the operating parameters of the robots in the target cold aisle, the occupancy parameters of the robots in the target cold aisle, the environmental parameters in the target area, and the cooling parameters of the cooling equipment are determined. A first congestion factor is calculated based on the occupancy parameters and operating parameters. A second congestion factor is calculated based on the operating parameters, cooling parameters, and environmental parameters. In response to the successful calculation of the first and second congestion factors, a first fusion calculation is performed based on the first and second congestion factors to obtain a congestion risk value. Based on the congestion risk value, a scheduling strategy for robots in the target cold aisle is determined and executed. The cooling equipment is adjusted based on the congestion risk value, thus realizing the collaborative scheduling of robots and cooling equipment within the target area. This not only avoids robot congestion and ensures robot efficiency but also ensures the heat dissipation effect of servers and other equipment in the rack, avoiding or mitigating the impact of robot heat generation and airflow obstruction on heat dissipation, thereby improving the long-term stability of the data center.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0017] The above-mentioned contents, other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0018] Figure 1 This illustration schematically depicts an application scenario of a collaborative scheduling method and apparatus for robots and refrigeration equipment within a target area according to an embodiment of this application.

[0019] Figure 2 A flowchart illustrating a collaborative scheduling method for robots and refrigeration equipment within a target area according to an embodiment of this application is shown in the schematic diagram.

[0020] Figure 3 This illustration schematically depicts an application scenario of a collaborative scheduling method and apparatus for robots and refrigeration equipment within a target area according to yet another embodiment of this application;

[0021] Figure 4 This schematic diagram illustrates the structural block diagram of a collaborative scheduling device for robots and refrigeration equipment within a target area according to an embodiment of this application;

[0022] Figure 5 This schematic diagram illustrates a structural block diagram of a collaborative scheduling system for robots and refrigeration equipment within a target area according to an embodiment of this application.

[0023] Figure 6A block diagram of an electronic device suitable for implementing a collaborative scheduling method for robots and refrigeration equipment within a target area, according to an embodiment of this application, is shown schematically. Detailed Implementation

[0024] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0025] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0026] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0027] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0028] First, the technical terms used in this application are explained and clarified as follows.

[0029] A cold aisle is a passageway space formed by two adjacent rows of server racks in a data center. It can enclose the cold air generated by the cooling equipment in the cold aisle, allowing the cold air to enter the server rack from the air inlet and dissipate heat from the servers and other equipment.

[0030] Robots refer to autonomous mobile devices capable of performing inspections, maintenance, and cleaning within data center cold aisles, including but not limited to inspection robots, maintenance robots, and cleaning robots.

[0031] Refrigeration equipment refers to equipment that provides cooling air for the cold aisle of a data center. Refrigeration equipment typically includes a cold source side, a conveying and distribution side, and a terminal side. The cold source side usually includes components such as compressors and evaporators, the conveying and distribution side usually includes components such as conveying pipes and water pumps, and the terminal side includes components such as air supply fans and filters.

[0032] Data centers in the financial industry typically feature enclosed cold aisle layouts for their server rooms. Cooling equipment delivers cold air into the aisle, with multiple server racks positioned on either side. The racks usually face the aisle, and the cool air enters through the rack inlets to dissipate heat from devices such as servers. Meanwhile, existing data centers increasingly utilize robots for equipment inspection and maintenance, sometimes with multiple robots operating simultaneously within the same cold aisle area. For example, a 10-square-meter cold aisle might have one floor cleaning robot, two inspection robots, and three maintenance robots operating concurrently. Existing technologies have the following drawbacks: 1) Limited cold aisle space can lead to congestion and reduced robot efficiency due to excessive robot activity; 2) Continuous robot operation releases heat into the environment, causing localized temperature increases within the aisle and affecting server cooling; 3) Too many robots can obstruct and interfere with airflow within the cold aisle, reducing airflow to servers and consequently impacting cooling performance.

[0033] Based on this, one embodiment of this application provides a collaborative scheduling method for robots and refrigeration equipment within a target area. The target area includes a target cold aisle and target cabinets affected by the target cold aisle. The target cold aisle is used to connect with the air outlet of the refrigeration equipment and also to accommodate robots. The method includes: determining the operating parameters of the robot in the target cold aisle, the occupancy parameters of the robot in the target cold aisle, the environmental parameters in the target area, and the refrigeration parameters of the refrigeration equipment; calculating a first congestion factor based on the occupancy parameters and operating parameters, and calculating a second congestion factor based on the operating parameters, refrigeration parameters, and environmental parameters; in response to the successful calculation of the first and second congestion factors, performing a first fusion calculation based on the first and second congestion factors to obtain a congestion risk value; determining and executing a scheduling strategy for the robot in the target cold aisle based on the congestion risk value; and adjusting the refrigeration equipment based on the congestion risk value.

[0034] The collaborative scheduling method for robots and cooling equipment within a target area, as described in the above embodiments of this application, determines the operating parameters of the robots in the target cold aisle, the occupancy parameters of the robots in the target cold aisle, the environmental parameters of the target area, and the cooling parameters of the cooling equipment. A first congestion factor is calculated based on the occupancy parameters and operating parameters. A second congestion factor is calculated based on the operating parameters, cooling parameters, and environmental parameters. Upon successful calculation of the first and second congestion factors, a first fusion calculation is performed based on the first and second congestion factors to obtain a congestion risk value. A scheduling strategy for the robots in the target cold aisle is determined and executed based on the congestion risk value. The cooling equipment is adjusted based on the congestion risk value, thus achieving collaborative scheduling of robots and cooling equipment within the target area. This not only avoids robot congestion and ensures robot efficiency but also guarantees the heat dissipation effect of servers and other equipment within the racks. It avoids or mitigates the impact of robot heat generation and airflow obstruction on heat dissipation during operation in the target cold aisle, thereby improving the long-term stability of the data center.

[0035] It should be noted that the collaborative scheduling method, apparatus, device, medium, and product for robots and refrigeration equipment within the target area provided in the embodiments of this application can be used in the fields of fintech and IoT technologies, as well as in various other fields besides fintech and IoT technologies. The application fields of the collaborative scheduling method, apparatus, device, medium, and product for robots and refrigeration equipment within the target area provided in the embodiments of this application are not limited.

[0036] Figure 1 The illustration schematically depicts an application scenario of a collaborative scheduling method and apparatus for robots and refrigeration equipment within a target area according to an embodiment of this application. For example... Figure 1 As shown, application scenario 100 according to an embodiment of this application may include a first terminal device 101, a second terminal device 102, a robot 103, a network 104, and a cooling device 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the robot 103, and the cooling device 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables. For example, a user can use the first terminal device 101 and the second terminal device 102 to interact with the robot 103 and the cooling device 105 through the network 104 to receive or send information, etc.

[0037] The first terminal device 101 and the second terminal device 102 can be electronic devices such as smartphones, wearable devices, personal computers, intelligent voice interaction devices, smart home appliances, intelligent vehicles, in-vehicle terminals, aircraft, unmanned vending terminals, and extended reality devices. Extended reality devices can include virtual reality devices, augmented reality devices, and mixed reality devices. A client application for the target application can be installed and run on the terminal device. This target application can include, but is not limited to, applications for financial device management, financial transactions, payment, shopping, web browsers, search applications, instant messaging tools, email clients, and social media platforms (only examples). Furthermore, this application embodiment does not limit the form of the target application, including but not limited to applications, mini-programs, etc., installed on the terminal device, and can also be in web page form.

[0038] It should be noted that the collaborative scheduling method for robots and refrigeration equipment within the target area provided in this application embodiment can generally be executed by terminal devices 101-102. Accordingly, the collaborative scheduling device for robots and refrigeration equipment within the target area provided in this application embodiment can generally be set in terminal devices 101-102.

[0039] It should be understood that Figure 1 The number of terminal devices and networks shown is merely illustrative. Depending on implementation needs, any number of terminal devices and networks can be included.

[0040] Figure 2 A flowchart illustrating a collaborative scheduling method for robots and refrigeration equipment within a target area according to an embodiment of this application is shown. Figure 2 As shown, a collaborative scheduling method 200 for robots and refrigeration equipment within a target area according to an embodiment of this application may include steps S210 to S230.

[0041] In step S210, the operating parameters of the robot in the target cold aisle, the occupancy parameters of the robot in the target cold aisle, the environmental parameters in the target area, and the refrigeration parameters of the refrigeration equipment are determined.

[0042] In this embodiment, the operating power in the robot's operating parameters can be directly obtained from feedback from the robot control system. The moving speed in the operating parameters can be calculated by monitoring the robot's position changes using LiDAR and ultra-wideband technology. The projected area in the occupancy parameters can be obtained by acquiring 3D point cloud data using LiDAR and / or cameras to obtain the footprint outline, and then calculating the projected area based on the footprint outline. The number of robot bodies in the occupancy parameters can be obtained through image recognition from cameras. The actual wind speed in the environmental parameters can be obtained by detecting it using an ultrasonic anemometer. The inlet temperature in the environmental parameters can be obtained by scanning the air inlet of the cabinet using an infrared thermal imager. The set temperature in the cooling parameters can be directly obtained from feedback from the cooling equipment control system. It is understood that the specific methods for determining and obtaining the above parameters are only illustrative examples and are not intended to limit the embodiments of this application. Users can choose other methods to determine and obtain the above parameters according to actual conditions.

[0043] In step S220, a first congestion factor is calculated based on the occupancy parameter and the operating parameter, and a second congestion factor is calculated based on the operating parameter, the cooling parameter, and the environmental parameter.

[0044] In this embodiment, the first congestion factor calculated based on the occupancy parameters and operating parameters can, to some extent, reflect the degree of congestion caused by the robot in the target cold aisle. The second congestion factor calculated based on the operating parameters, refrigeration parameters, and environmental parameters can, to some extent, reflect the degree of risk of environmental changes in the target cold aisle affected by the robot.

[0045] In step S230, in response to the successful calculation of the first congestion factor and the second congestion factor, a first fusion calculation is performed based on the first congestion factor and the second congestion factor to obtain a congestion risk value. Based on the congestion risk value, a scheduling strategy for the robot in the target cold aisle is determined and executed, and the refrigeration equipment is adjusted based on the congestion risk value.

[0046] In this embodiment, successful calculation of the first and second congestion factors means that the operating parameters, placeholder parameters, environmental parameters, and cooling parameters are all within the normal threshold range, which can be set based on practical experience. If at least one of the operating parameters, placeholder parameters, environmental parameters, and cooling parameters is outside the normal threshold range, the calculation of either the first or second congestion factor fails. Specifically, it can be determined which of the first and second congestion factors failed, or both, based on whether the specific parameters involved in calculating each factor are within the normal threshold range. It is understood that when at least one of the first and second congestion factors fails to be calculated, a warning message can be issued to prompt user intervention. Typically, calculation failures are due to problems in the acquisition of relevant parameters, such as sensor damage causing the corresponding parameters to deviate from the normal threshold range. This avoids errors in the final scheduling result due to unexpected situations such as obtaining incorrect parameters or parameter acquisition failures.

[0047] Figure 3 This illustration schematically depicts an application scenario of a collaborative scheduling method and apparatus for robots and refrigeration equipment within a target area according to another embodiment of this application. Figure 3 As shown, the target area 301 includes a target cold aisle 302 and cabinets 303. Multiple cabinets 303 are arranged in two rows to enclose and form the target cold aisle 302. The air outlets 305 of the refrigeration equipment are set on the floor of the target cold aisle 302. An air supply fan (not shown in the figure) is installed in the air outlet 305. Multiple robots 304 are running in the target cold aisle 302.

[0048] Furthermore, the occupancy parameters include the projected area of ​​the robot on the ground of the target cold aisle and the number of robot bodies in the target cold aisle, and the operating parameters include the moving speed of the robot in the target cold aisle; the first congestion factor is calculated based on the occupancy parameters and operating parameters, including: calculating the space occupancy rate of the robot in the target cold aisle based on the projected area and the aisle ground area; calculating the speed dispersion of the robot in the target cold aisle based on the moving speed and the number of robot bodies; calculating the motion disorder of the robot in the target cold aisle based on the speed dispersion and a first preset speed threshold; and performing a second fusion calculation based on the space occupancy rate and motion disorder to obtain the first congestion factor.

[0049] In this embodiment, the floor area of ​​the target cold aisle is a known quantity and can be pre-calculated. Specifically, the ratio of the projected area to the floor area is the space occupancy rate. The speed dispersion in this embodiment is the speed standard deviation, which is calculated from the moving speed, average speed (calculated from the moving speed), and number of robots within the target cold aisle. The first preset speed threshold can be set based on practical experience, for example, specifically 0.8 m / s, which typically refers to the maximum safe speed within the cold aisle. The ratio of the aforementioned speed standard deviation to the first preset speed threshold is the motion turbulence degree. Space occupancy rate and motion disorder each have a first weighting coefficient and a second weighting coefficient (the sum of the first and second weighting coefficients equals 1). The specific values ​​of the first and second weighting coefficients can be designed as needed. For example, when there are many robots, the first weighting coefficient can be appropriately increased; when some robots move faster, the first weighting coefficient can be appropriately decreased (i.e., the second weighting coefficient can be increased). In this way, the space occupancy rate and motion disorder are weighted and fused to obtain the first congestion factor. The first congestion factor is positively correlated with both space occupancy rate and motion disorder. The specific calculation formula for the first congestion factor CI is as follows.

[0050] .

[0051] In the formula, Let be the projected area of ​​a single robot within the target cold aisle. The area of ​​the passageway ground. Let the standard deviation of the robot's moving speed within the target cold aisle be denoted as . The first preset speed threshold is α, the first weighting coefficient is β, and the second weighting coefficient is β.

[0052] Furthermore, considering that some robots have variable forms, such as robots with telescopic arms, their projected area may not accurately reflect their footprint. Therefore, determining the projected area also includes determining the robot's deformation state. Specifically, the operating parameters also include the deformation state, which can be obtained directly through feedback from the robot's control system. If the deformation state is contracted, the projected area is multiplied by a preset reduction factor (e.g., 0.6) to appropriately reduce the projected area. If the deformation state is extended, the projected area is multiplied by a preset expansion factor (e.g., 1.2) to appropriately expand the projected area, resulting in a more accurate projected area.

[0053] In this way, by calculating the first congestion factor, the degree of congestion of robots in the target cold aisle can be measured. By combining the robot's space occupancy rate and motion disorder, the first congestion factor can more accurately describe the degree of congestion, thereby improving the accuracy of subsequent congestion risk values ​​and ensuring the accuracy of robot scheduling strategies and refrigeration equipment adjustment operations.

[0054] Furthermore, the operating parameters also include the robot's operating power, environmental parameters including the actual airflow velocity at the air inlet of the target cabinet and the air inlet temperature of the target cabinet, and cooling parameters including the set temperature of the cooling equipment. A second congestion factor is calculated based on the operating parameters, cooling parameters, and environmental parameters, including: calculating the airflow blockage degree based on the actual airflow velocity, the preset airflow velocity, and the attenuation coefficient (the attenuation coefficient is calculated based on the number of units and the cross-sectional area of ​​the target cold aisle); calculating the temperature rise degree based on the air inlet temperature, the set temperature, and the first preset temperature rise threshold; calculating the heat source intensity based on the operating power and the first energy conversion coefficient; and calculating the second congestion factor based on the airflow blockage degree, the temperature rise degree, and the heat source intensity.

[0055] In this embodiment, the operating power can be obtained directly from the equipment information provided by the robot control system, or calculated using the rated power of all motors used by the robot. The actual intake air velocity can be obtained by setting an ultrasonic anemometer array at the air inlet of the cabinet. The preset intake air velocity typically refers to the air velocity at the cabinet air inlet when there are no obstructions in the target cold aisle (i.e., the ideal design air velocity is the preset intake air velocity). To ensure the accuracy of the preset intake air velocity value, in response to the absence of a robot in the target cold aisle, the air velocity at this time is obtained using an ultrasonic anemometer as the preset intake air velocity, thereby continuously updating the preset intake air velocity and improving the accuracy of the second congestion factor calculation. The air inlet temperature can be obtained by scanning the air inlet of the target cabinet with an infrared thermal imager. The set temperature can be obtained directly from the control system of the refrigeration equipment, or obtained by receiving refrigeration operation information sent by the refrigeration equipment via a network. The specific value of the set temperature is usually adjusted manually by the user or automatically by the control system of the refrigeration equipment, and the set temperature is usually also the target temperature of the refrigeration equipment. The airflow blockage degree is calculated based on the actual intake air velocity, the preset intake air velocity, and the attenuation coefficient. The attenuation coefficient is calculated based on the number of robots and the cross-sectional area of ​​the target cold aisle. The ratio of the number of robots to the cross-sectional area of ​​the target cold aisle (the cross-sectional area intercepted by planes perpendicular to the ground and facing both ends of the target cold aisle along its length) is the attenuation coefficient, which measures the robot density within the target cold aisle. Specifically, the ratio of the actual inlet air velocity to the preset inlet air velocity is multiplied by the negative of the attenuation coefficient to obtain a product. An exponential function with the natural constant e as the base and this product as the exponent is the airflow obstruction degree, which measures the degree of obstruction of airflow within the target cold aisle due to the robots. The temperature rise is calculated based on the inlet temperature, the set temperature, and a first preset temperature rise threshold. Specifically, the ratio of the maximum difference between each inlet temperature and the set temperature (the inlet temperature is usually higher than the set temperature) to the first preset temperature rise threshold is the temperature rise degree, which measures the degree to which the cooling effect of the current target cold aisle on the target cabinet is affected by the robots. The heat source intensity is calculated based on the operating power and the first energy conversion coefficient. Specifically, the sum of the products of each robot's operating power and the first energy conversion coefficient is the total heat generation power. The square root of the total heat generation power is the heat source intensity, which measures the degree of heat generated during robot operation. The first energy conversion coefficient can be determined based on practical experience, for example, 0.35. The second congestion factor is calculated based on airflow obstruction, temperature rise, and heat source intensity. Specifically, the product of these three factors is the second congestion factor, which is positively correlated with airflow obstruction, temperature rise, and heat source intensity. The specific calculation formula for the second congestion factor CF is as follows.

[0056] .

[0057] In the formula, This refers to the actual intake air velocity. To preset the intake air velocity, The attenuation coefficient is... This represents the maximum difference between the temperature at each air inlet and the set temperature. The first preset temperature rise threshold, This represents the total power generated by heat.

[0058] In this way, by calculating the second congestion factor, the impact of robot congestion on the heat dissipation effect of the target cold aisle can be measured. By combining airflow obstruction, temperature rise, and heat source intensity, the second congestion factor can more accurately describe the degree to which robot congestion reduces the heat dissipation effect of the target cold aisle, thereby improving the accuracy of subsequent congestion risk values ​​and ensuring the accuracy of scheduling strategies for robots and adjustment operations for refrigeration equipment.

[0059] Based on the first congestion factor CI and the second congestion factor CF mentioned above, the specific formula for calculating the congestion risk value CRV is as follows.

[0060] .

[0061] In the formula, CI is the first congestion factor, and CF is the second congestion factor. This is the maximum difference between the inlet air temperature and the set temperature. The second preset temperature rise threshold, , The third weighting coefficient of the first congestion factor. , This is the fourth weighting coefficient for the second congestion factor. Wherein, and The sum is 1. and The sum is 1. Less than .

[0062] In other words, the congestion risk value is obtained by performing a first fusion calculation based on the first congestion factor and the second congestion factor. This includes: determining a third weighting coefficient and a fourth weighting coefficient based on the maximum difference between the temperature of each air inlet and the set temperature; and performing a first fusion calculation based on the first congestion factor, the third weighting coefficient corresponding to the first congestion factor, the second congestion factor, and the fourth weighting coefficient corresponding to the second congestion factor to obtain the congestion risk value. Specifically, the third weighting coefficient determined when the maximum difference between the temperature of each air inlet and the set temperature is less than or equal to a second preset temperature rise threshold is greater than the third weighting coefficient determined when the maximum difference between the temperature of each air inlet and the set temperature is greater than the second preset temperature rise threshold. This allows for an increase in the weight of the second congestion factor when there is a significant temperature rise, thereby improving the accuracy of the congestion risk value.

[0063] Furthermore, based on the congestion risk value, a scheduling strategy for the robot in the target cold aisle is determined and executed, including: in response to the congestion risk value being within a first risk threshold range, a first scheduling strategy is determined and executed as the scheduling strategy, the first scheduling strategy including: further determining operating parameters, occupancy parameters, environmental parameters, and cooling parameters.

[0064] In response to the congestion risk value being within the second risk threshold range, a second scheduling strategy is determined and executed as the scheduling strategy. The second scheduling strategy includes: prohibiting robots outside the target cold aisle from entering the target cold aisle, adjusting the movement path of robots within the target cold aisle, and sending a request to the refrigeration equipment to increase the first air supply speed for the air supply vents connected to the target cold aisle.

[0065] In response to the congestion risk value being within the third risk threshold range, a third scheduling strategy is determined and executed as the scheduling strategy. The third scheduling strategy includes: sending an emergency cooling request to the cooling equipment and repeatedly performing evacuation operations until the evacuation termination conditions are met.

[0066] The evacuation operation includes: obtaining a priority list of tasks performed by robots in the target cold aisle; determining low-priority tasks based on the priority list; issuing pause task instructions and evacuation instructions to robots performing low-priority tasks; re-determining operating parameters, occupancy parameters, environmental parameters, and refrigeration parameters; and updating the congestion risk value. The evacuation termination conditions include at least one of the following: the congestion risk value is less than the minimum value of the third risk threshold range; there are no low-priority tasks in the priority list; and the number of robots in the target cold aisle is less than the first robot number threshold.

[0067] In this embodiment of the application, if the congestion risk value is within the first risk threshold range, for example, CRV is less than 0.6, then the first scheduling strategy is determined and executed as the scheduling strategy. Under the first scheduling strategy, no intervention or adjustment is required. The operating parameters, occupancy parameters, environmental parameters, and cooling parameters are then determined to prepare for subsequent continuous monitoring and adjustment.

[0068] If the congestion risk value falls within the second risk threshold range (e.g., CRV greater than or equal to 0.6 and less than 0.8), then a second scheduling strategy is determined and executed. This strategy prohibits robots outside the target cold aisle from entering it. Specifically, this can be achieved by rejecting a robot's request to enter the target cold aisle. The movement paths of robots within the target cold aisle are adjusted; the specific adjustment method is not limited, as long as it contributes to restoring the heat dissipation effect. A first airflow speed increase request is sent to the cooling equipment, specifically to the air outlet connected to the target cold aisle, which is equipped with a fan. This request increases the fan speed, and the specific speed increase can be designed as needed.

[0069] If the congestion risk value falls within the third risk threshold range (e.g., CRV greater than or equal to 0.8), then the third scheduling strategy is determined and executed. This strategy involves sending an emergency cooling request to the cooling equipment. This is understandable, as data centers typically have emergency cooling equipment (i.e., backup cooling equipment). This emergency cooling equipment normally serves as a backup and does not participate in cooling; it only activates in emergencies. Upon receiving the emergency cooling request, the cooling equipment activates the emergency cooling equipment, which works in conjunction with the main cooling equipment to temporarily increase cooling capacity. The third scheduling strategy also includes repeatedly executing evacuation operations until the evacuation termination conditions are met. These evacuation operations include: obtaining a priority list of tasks performed by robots within the target cold aisle. This priority list can be predefined or configured manually and includes high-priority and low-priority tasks. Based on the priority list, low-priority tasks are determined, and a pause task command and a cold aisle withdrawal command are issued to the robots performing these low-priority tasks. Upon receiving the pause task command, the robot pauses its currently executing task. Upon receiving the cold aisle withdrawal command, the robot leaves the target cold aisle after the pause is completed. After the robot leaves (optionally, a certain interval is set to allow the parameters of the target area to stabilize), the operating parameters, occupancy parameters, environmental parameters, and cooling parameters are redefined, and the congestion risk value is updated. If the evacuation termination condition is not met after the update, i.e., the congestion risk value is still within the third risk threshold range, there are low-priority tasks in the priority list, and the number of robots is greater than or equal to the first robot quantity threshold, then the evacuation operation continues to be repeated. If the evacuation termination condition is met after the update, i.e., the congestion risk value is less than the minimum value of the third risk threshold range, and / or there are no low-priority tasks in the priority list, and / or the number of robots in the target cold aisle is less than the first robot quantity threshold, then the repeated execution of the evacuation operation stops. The first robot quantity threshold can be, for example, one, and the specific value can be designed according to the actual situation.

[0070] In this way, based on the different risk threshold ranges of the congestion risk value, different scheduling strategies are determined and executed for the robots, achieving fine-grained scheduling of robots under different risk levels. This ensures the heat dissipation effect of the target cabinet in the target area and improves the stability of the continuous operation of servers and other equipment in the target cabinet. When the congestion risk value is within the first risk threshold range, monitoring of various parameters continues to ensure that the target area is monitored in real time. When the congestion risk value is within the second risk threshold range, the number of robots in the target cold aisle is prohibited from increasing. At the same time, the robot's movement path is adjusted, and the air outlet speed is increased to reduce the impact of robot congestion on the heat dissipation effect of the target area. When the congestion risk value is within the third risk threshold range, an emergency cooling mechanism is triggered, and evacuation operations are repeatedly executed until the evacuation termination conditions are met, minimizing the impact of robots on the heat dissipation effect in the target area.

[0071] Furthermore, the motion path of the robot within the target cold aisle is adjusted, including: determining the temperature gradient field within the target cold aisle; calculating the avoidance vector based on the avoidance algorithm; calculating the thermal repulsion velocity component based on the temperature gradient field and the temperature gradient weighting coefficient; calculating the control velocity vector of the robot within the target cold aisle based on the avoidance vector and the thermal repulsion velocity component; and adjusting the motion path of the robot within the target cold aisle based on the control velocity vector.

[0072] In this embodiment, when determining the temperature gradient field of the target cold aisle, a temperature gradient field model can be optionally generated using a computer based on cabinet power consumption data, cooling air parameters, robot position heatmaps, etc. Then, a distributed temperature sensor network continuously collects discrete point temperature data of the target cold aisle, and the temperature gradient field is continuously generated according to a corresponding algorithm (e.g., inverse distance weighted interpolation, the specific algorithm can be designed according to actual needs). It is understood that the specific method of generating the temperature gradient field can be designed as needed, and the specific method is not limited here. The specific calculation method of the avoidance algorithm (in this embodiment, the avoidance algorithm adopts ORCA algorithm, i.e., Optimal Reciprocal Collision Avoidance) is not limited here and can be calculated according to the actual situation. For example, for a robot, the speed obstacle region is first calculated, and then, based on the speed obstacle region and the reciprocity principle, the speed correction amount that the robot and other robots need to bear is calculated. Then, a speed vector with the smallest deviation from the desired speed is calculated, i.e., the avoidance vector. The temperature gradient weight coefficient can be set as needed, and it represents the speed generated by a unit temperature gradient. The thermal repulsion velocity component is calculated based on the temperature gradient field and temperature gradient weighting coefficients. Then, the control velocity vector is calculated based on the avoidance vector and the thermal repulsion velocity component. (Control velocity vector) The specific calculation formula is as follows.

[0073] .

[0074] In the formula, To avoid vectors, These are the temperature gradient weighting coefficients. For temperature gradient field, Let be the modulus of the temperature gradient field. Output the temperature gradient field modulus to the range of (0, 1).

[0075] In this way, by combining avoidance vectors and control velocity vectors, the robot's movement path can be adjusted, enabling the robot to move towards areas with lower temperatures while avoiding collisions and congestion. This reduces local heat buildup caused by the robot's operation and better mitigates the impact of the robot's operation on the heat dissipation of the target area.

[0076] Furthermore, adjusting the refrigeration equipment based on the congestion risk value includes: calculating a target opening degree based on the congestion risk value and the baseline opening degree of the refrigeration equipment's air valve, wherein the target opening degree is positively correlated with the congestion risk value; and adjusting the opening degree of the refrigeration equipment's air valve based on the target opening degree.

[0077] In this embodiment, the opening degree of the air valve of the refrigeration equipment is controlled in conjunction with the congestion risk value, wherein the target opening degree is... The specific calculation formula is as follows.

[0078] .

[0079] In the formula, The reference opening is the valve opening degree. The specific value of the reference opening degree can be set as needed. It usually refers to the valve opening degree under normal conditions. This is a proportionality coefficient, which can be set as needed, and its value is usually set to 0.2. As the congestion risk value increases, the target opening also increases accordingly, and the opening of the refrigeration equipment's air valve is adjusted based on the target opening.

[0080] In this way, by adjusting the refrigeration equipment based on the congestion risk value, the robots and refrigeration equipment can be coordinated and scheduled to better ensure the heat dissipation effect of the target area.

[0081] Furthermore, the method also includes: in response to the failure of calculating only the first congestion factor, adjusting the refrigeration equipment according to the second congestion factor; in response to the failure of calculating only the second congestion factor, determining and executing a fourth scheduling strategy as a scheduling strategy based on the first congestion factor, the fourth scheduling strategy including: calculating an avoidance vector according to an avoidance algorithm, and adjusting the movement path of the robot in the target cold aisle based on the avoidance vector.

[0082] In this embodiment, when only the first congestion factor fails to be calculated (usually due to damage to the sensor collecting parameters related to the first congestion factor), the second congestion factor is used to adjust the cooling equipment. This includes at least one of the following: sending a first request to increase the airflow speed to the cooling equipment; sending a first request to lower the set temperature to the cooling equipment; and sending an emergency cooling request to the cooling equipment. When only the second congestion factor fails to be calculated (usually due to damage to the sensor collecting parameters related to the second congestion factor), the first congestion factor is used to determine and execute a fourth scheduling strategy. This includes directly adjusting the robot's motion path using the avoidance vector calculated by the avoidance algorithm, achieving a relatively simple and quick robot avoidance scheduling.

[0083] This improves the continuous operational stability and reliability of the collaborative scheduling method, ensuring that a certain level of scheduling capability can be maintained even in situations such as partial sensor failure.

[0084] The collaborative scheduling method for robots and cooling equipment within a target area, as described in the above embodiments of this application, determines the operating parameters of the robots in the target cold aisle, the occupancy parameters of the robots in the target cold aisle, the environmental parameters of the target area, and the cooling parameters of the cooling equipment. A first congestion factor is calculated based on the occupancy parameters and operating parameters. A second congestion factor is calculated based on the operating parameters, cooling parameters, and environmental parameters. Upon successful calculation of the first and second congestion factors, a first fusion calculation is performed based on the first and second congestion factors to obtain a congestion risk value. A scheduling strategy for the robots in the target cold aisle is determined and executed based on the congestion risk value. The cooling equipment is adjusted based on the congestion risk value, thus achieving collaborative scheduling of robots and cooling equipment within the target area. This not only avoids robot congestion and ensures robot efficiency but also guarantees the heat dissipation effect of servers and other equipment within the racks. It avoids or mitigates the impact of robot heat generation and airflow obstruction on heat dissipation during operation in the target cold aisle, thereby improving the long-term stability of the data center.

[0085] Specifically, the collaborative scheduling method for robots and refrigeration equipment within the target area provided in this application brings the following beneficial effects.

[0086] 1) By calculating the first congestion factor, the degree of congestion of robots within the target cold aisle can be measured. Considering both robot space occupancy and motion turbulence, the first congestion factor more accurately describes the congestion level, thereby improving the accuracy of subsequent congestion risk values ​​and ensuring the accuracy of robot scheduling strategies and refrigeration equipment adjustments. By calculating the second congestion factor, the impact of robot congestion on the heat dissipation effect of the target cold aisle can be measured. Considering airflow obstruction, temperature rise, and heat source intensity, the second congestion factor more accurately describes the degree to which robot congestion reduces the heat dissipation effect of the target cold aisle, thereby improving the accuracy of subsequent congestion risk values ​​and ensuring the accuracy of robot scheduling strategies and refrigeration equipment adjustments. 2) Based on different risk threshold ranges for the congestion risk value, different scheduling strategies are determined and executed for the robots, achieving fine-grained scheduling of robots under different risk levels. This ensures the heat dissipation effect of the target racks in the target area and improves the stability of continuous operation of servers and other equipment in the target racks. When the congestion risk value is within the first risk threshold range, monitoring of all parameters continues to ensure that the target area is monitored in real time. When the congestion risk value is within the second risk threshold range, the number of robots in the target cold aisle is prohibited from increasing. Simultaneously, the robot's movement path is adjusted, and the airflow velocity at the air outlets is increased to reduce the impact of robot congestion on the heat dissipation effect of the target area. When the congestion risk value is within the third risk threshold range, an emergency cooling mechanism is triggered, and evacuation operations are repeatedly executed until the evacuation termination conditions are met, minimizing the impact of robots on the heat dissipation effect within the target area. 3) Combining avoidance vectors and control velocity vectors, the robot's movement path is adjusted, enabling the robot to move towards lower-temperature areas while avoiding collisions and congestion, reducing localized heat accumulation caused by robot operation and better mitigating the impact of robot operation on the heat dissipation effect of the target area. Adjusting the cooling equipment based on the congestion risk value allows for coordinated scheduling of robots and cooling equipment, better ensuring the heat dissipation effect of the target area. This improves the continuous operational stability and reliability of the coordinated scheduling method, maintaining a certain scheduling capability even in situations such as partial sensor failure.

[0087] Based on the above-described method for the coordinated scheduling of robots and refrigeration equipment within a target area, an embodiment of this application also provides a device for the coordinated scheduling of robots and refrigeration equipment within a target area. The following will be combined with... Figure 4 The device is described in detail.

[0088] Figure 4 The diagram illustrates a structural block diagram of a collaborative scheduling device for robots and refrigeration equipment within a target area according to an embodiment of this application.

[0089] like Figure 4As shown, the collaborative scheduling device 400 for robots and refrigeration equipment within the target area in this embodiment includes: a determination module 410, a calculation module 420, and an execution module 430.

[0090] The determining module 410 is used to determine the operating parameters of the robot in the target cold aisle, the occupancy parameters of the robot in the target cold aisle, the environmental parameters in the target area, and the refrigeration parameters of the refrigeration equipment. In one embodiment, the determining module 410 can be used to perform step S210 described above, which will not be repeated here.

[0091] The calculation module 420 is used to calculate a first congestion factor based on the occupancy parameters and the operating parameters, and to calculate a second congestion factor based on the operating parameters, the cooling parameters, and the environmental parameters. In one embodiment, the calculation module 420 can be used to execute step S220 described above, which will not be repeated here.

[0092] The execution module 430 is configured to, in response to the successful calculation of the first congestion factor and the second congestion factor, perform a first fusion calculation based on the first congestion factor and the second congestion factor to obtain a congestion risk value, determine and execute a scheduling strategy for the robot in the target cold aisle based on the congestion risk value, and adjust the refrigeration equipment based on the congestion risk value. In one embodiment, the execution module 430 may be used to execute step S230 described above, which will not be repeated here.

[0093] According to an embodiment of this application, the collaborative scheduling device 400 for robots and refrigeration equipment within the target area further includes an emergency execution module (not shown in the figure). In one embodiment, the emergency execution module is configured to, in response to the failure of only the calculation of the first congestion factor, adjust the refrigeration equipment according to the second congestion factor; and, in response to the failure of only the calculation of the second congestion factor, determine and execute a fourth scheduling strategy as the scheduling strategy based on the first congestion factor, the fourth scheduling strategy including: calculating an avoidance vector according to an avoidance algorithm, and adjusting the movement path of the robot within the target cold aisle based on the avoidance vector.

[0094] According to embodiments of this application, any multiple modules among the determining module 410, computing module 420, execution module 430, and emergency execution module can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this application, at least one of the determining module 410, computing module 420, execution module 430, and emergency execution module can be at least partially implemented as hardware circuitry, such as field-programmable gate arrays, programmable logic arrays, systems-on-a-chip, systems-on-a-substrate, systems-on-package, application-specific integrated circuits, or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the determining module 410, computing module 420, execution module 430, and emergency execution module can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0095] Based on the aforementioned collaborative scheduling device 400 for robots and refrigeration equipment within a target area, embodiments of this application also provide a collaborative scheduling system for robots and refrigeration equipment within a target area. The following will be combined with... Figure 5 The system is described in detail.

[0096] Figure 5 The diagram illustrates a structural block diagram of a collaborative scheduling system for robots and refrigeration equipment within a target area according to an embodiment of this application.

[0097] like Figure 5 As shown, the system 500 of this embodiment includes: a first determining unit 510, a second determining unit 520, a first calculating unit 530, a second calculating unit 540, a decision-making unit 550, a first execution unit 560, and a second execution unit 570.

[0098] The first determining unit 510 and the second determining unit 520 are used to determine the operating parameters of the robot in the target cold aisle, the occupancy parameters of the robot in the target cold aisle, the environmental parameters in the target area, and the refrigeration parameters of the refrigeration equipment. In one embodiment, the determining module 410 in the device 400 includes the first determining unit 510 and the second determining unit 520, which can be used to perform some of the operations of step S210 described above, and will not be repeated here.

[0099] The first calculation unit 530 and the second calculation unit 540 are used to calculate a first congestion factor based on the occupancy parameter and the operating parameter, and to calculate a second congestion factor based on the operating parameter, the cooling parameter, and the environmental parameter. In one embodiment, the calculation module 420 in the device 400 includes the first calculation unit 530 and the second calculation unit 540, which can be used to perform some of the operations of step S220 described above, and will not be repeated here.

[0100] The decision-making unit 550, the first execution unit 560, and the second execution unit 570 are used to respond to the successful calculation of the first congestion factor and the second congestion factor, perform a first fusion calculation based on the first congestion factor and the second congestion factor to obtain a congestion risk value, determine and execute a scheduling strategy for the robot in the target cold aisle based on the congestion risk value, and adjust the refrigeration equipment based on the congestion risk value. In one embodiment, the execution module 430 in the device 400 includes the decision-making unit 550, the first execution unit 560, and the second execution unit 570, which can be used to perform some of the operations in step S230 described above, and will not be repeated here.

[0101] According to embodiments of this application, at least one of the first determining unit 510, the second determining unit 520, the first computing unit 530, the second computing unit 540, the decision-making unit 550, the first execution unit 560, and the second execution unit 570 can be at least partially implemented as hardware circuits, such as field-programmable gate arrays, programmable logic arrays, systems-on-a-chip, systems-on-a-substrate, systems-on-package, application-specific integrated circuits, or any other reasonable means of integrating or packaging circuits, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the first determining unit 510, the second determining unit 520, the first computing unit 530, the second computing unit 540, the decision-making unit 550, the first execution unit 560, and the second execution unit 570 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0102] Figure 6 A block diagram of an electronic device suitable for implementing a collaborative scheduling method for robots and refrigeration equipment within a target area, according to an embodiment of this application, is shown schematically.

[0103] like Figure 6As shown, an electronic device 1200 according to an embodiment of this application includes a processor 1201, which can perform various appropriate actions and processes according to a program stored in a read-only memory 1202 or a program loaded from a storage portion 1208 into a random access memory 1203. The processor 1201 may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a dedicated microprocessor. The processor 1201 may also include onboard memory for caching purposes. The processor 1201 may include a single processing unit or multiple processing units for executing different steps of the method flow according to an embodiment of this application.

[0104] Random access memory 1203 stores various programs and data required for the operation of electronic device 1200. Processor 1201, read-only memory 1202, and random access memory 1203 are interconnected via bus 1204. Processor 1201 executes various steps of the method flow according to embodiments of this application by executing programs in read-only memory 1202 and / or random access memory 1203. It should be noted that the programs may also be stored in one or more memories other than read-only memory 1202 and random access memory 1203. Processor 1201 may also execute various steps of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0105] According to embodiments of this application, the electronic device 1200 may further include an input / output interface 1205, which is also connected to the bus 1204. The electronic device 1200 may also include one or more of the following components connected to the input / output interface 1205: an input section 1206 including a keyboard, mouse, etc.; an output section 1207 including a cathode ray tube, liquid crystal display, etc., and a speaker, etc.; a storage section 1208 including a hard disk, etc.; and a communication section 1209 including a network interface card, such as a local area network card, modem, etc. The communication section 1209 performs communication processing via a network such as the Internet. A drive 1210 is also connected to the input / output interface 1205 as needed. A removable medium 1211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 1210 as needed so that computer programs read from it can be installed into the storage section 1208 as needed.

[0106] Embodiments of this application also provide a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0107] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include the read-only memory 1202, and / or random access memory 1203, and / or one or more memories other than read-only memory 1202 and random access memory 1203 described above.

[0108] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this application.

[0109] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 1209, and / or installed from the removable medium 1211. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0110] In embodiments of this application, the computer program can be downloaded and installed from a network via communication section 1209, and / or installed from removable medium 1211. When the computer program is executed by processor 1201, it performs the functions defined in the system of this application embodiment. According to embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0111] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0112] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0113] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

Claims

1. A method for the coordinated scheduling of robots and refrigeration equipment within a target area, characterized in that, The target area includes a target cold aisle and target cabinets affected by the target cold aisle. The target cold aisle is used to communicate with the air outlet of the refrigeration equipment and also to accommodate a robot. The method includes: Determine the operating parameters of the robot in the target cold aisle, the occupancy parameters of the robot in the target cold aisle, the environmental parameters in the target area, and the refrigeration parameters of the refrigeration equipment; A first congestion factor is calculated based on the occupancy parameter and the operating parameter, and a second congestion factor is calculated based on the operating parameter, the cooling parameter, and the environmental parameter. In response to the successful calculation of the first congestion factor and the second congestion factor, a first fusion calculation is performed based on the first congestion factor and the second congestion factor to obtain a congestion risk value. Based on the congestion risk value, a scheduling strategy for the robot in the target cold aisle is determined and executed, and the refrigeration equipment is adjusted based on the congestion risk value.

2. The method according to claim 1, characterized in that, The occupancy parameters include the projected area of ​​the robot on the ground of the target cold aisle and the number of robot bodies in the target cold aisle; the operating parameters include the moving speed of the robot in the target cold aisle. The calculation of the first congestion factor based on the occupancy parameters and the operating parameters includes: The space occupancy rate of the robot in the target cold aisle is calculated based on the projected area and the floor area of ​​the target cold aisle. The speed dispersion of the robot within the target cold aisle is calculated based on the moving speed and the number of robots, and the motion disorder of the robot within the target cold aisle is calculated based on the speed dispersion and a first preset speed threshold. The first congestion factor is obtained by performing a second fusion calculation based on the space occupancy rate and the motion disorder.

3. The method according to claim 2, characterized in that, The operating parameters also include the robot's operating power; the environmental parameters include the actual airflow velocity at the air inlet of the target cabinet and the air inlet temperature of the target cabinet; the cooling parameters include the set temperature of the cooling equipment; and the calculation of the second congestion factor based on the operating parameters, the cooling parameters, and the environmental parameters includes: The airflow blockage degree is calculated based on the actual air intake velocity, the preset air intake velocity, and the attenuation coefficient, wherein the attenuation coefficient is calculated based on the number of units and the cross-sectional area of ​​the target cold aisle. The degree of temperature rise is calculated based on the air inlet temperature, the set temperature, and the first preset temperature rise threshold. The heat source intensity is calculated based on the operating power and the first energy conversion coefficient. The second congestion factor is calculated based on the airflow obstruction degree, the temperature rise degree, and the heat source intensity.

4. The method according to claim 3, characterized in that, The step of determining and executing a scheduling strategy for robots within the target cold aisle based on the congestion risk value includes: In response to the congestion risk value being within a first risk threshold range, a first scheduling strategy is determined and executed as the scheduling strategy, the first scheduling strategy including: continuing to determine the operating parameters, the placeholder parameters, the environmental parameters, and the cooling parameters; In response to the congestion risk value being within the second risk threshold range, a second scheduling strategy is determined and executed as the scheduling strategy. The second scheduling strategy includes: prohibiting robots outside the target cold aisle from entering the target cold aisle, adjusting the movement path of robots within the target cold aisle, and sending a first air supply speed increase request to the refrigeration equipment for the air supply outlet connected to the target cold aisle. In response to the congestion risk value being within the third risk threshold range, a third scheduling strategy is determined and executed as the scheduling strategy. The third scheduling strategy includes: sending an emergency cooling request to the cooling equipment and repeatedly performing evacuation operations until the evacuation termination conditions are met. The evacuation operation includes: obtaining a priority list of tasks performed by robots in the target cold aisle; determining low-priority tasks based on the priority list; issuing a pause task command and a withdrawal command from the cold aisle to the robots performing the low-priority tasks; re-determining the operating parameters, the occupancy parameters, the environmental parameters, and the refrigeration parameters; and updating the congestion risk value. The evacuation termination conditions include at least one of the following: the congestion risk value is less than the minimum value of the third risk threshold range; the low-priority task does not exist in the priority list; and the number of robots in the target cold aisle is less than the first robot number threshold.

5. The method according to claim 4, characterized in that, The adjustment of the robot's movement path within the target cold aisle includes: Determine the temperature gradient field within the target cold aisle; The avoidance vector is calculated based on the avoidance algorithm; The thermal repulsion velocity component is calculated based on the temperature gradient field and the temperature gradient weighting coefficient. The control velocity vector of the robot in the target cold aisle is calculated based on the avoidance vector and the thermal repulsion velocity component, and the motion path of the robot in the target cold aisle is adjusted based on the control velocity vector.

6. The method according to claim 4, characterized in that, Adjusting the refrigeration equipment based on the congestion risk value includes: The target opening is calculated based on the congestion risk value and the baseline opening of the air valve of the refrigeration equipment, and the target opening is positively correlated with the congestion risk value. Adjust the opening degree of the air valve of the refrigeration equipment based on the target opening degree.

7. The method according to claim 6, characterized in that, The method further includes: In response to the failure of calculating only the first congestion factor, the refrigeration equipment is adjusted according to the second congestion factor; In response to the failure of calculating only the second congestion factor, a fourth scheduling strategy is determined and executed based on the first congestion factor. The fourth scheduling strategy includes: calculating an avoidance vector based on an avoidance algorithm, and adjusting the movement path of the robot in the target cold aisle based on the avoidance vector.

8. A collaborative scheduling device for robots and refrigeration equipment within a target area, characterized in that, The target area includes a target cold aisle and target cabinets affected by the target cold aisle. The target cold aisle is used to communicate with the air outlet of the refrigeration equipment and also to accommodate a robot. The device includes: The determination module is used to determine the operating parameters of the robot in the target cold aisle, the occupancy parameters of the robot in the target cold aisle, the environmental parameters in the target area, and the refrigeration parameters of the refrigeration equipment; The calculation module is used to calculate a first congestion factor based on the occupancy parameter and the operating parameter, and to calculate a second congestion factor based on the operating parameter, the cooling parameter and the environmental parameter; The execution module is configured to, in response to the successful calculation of the first congestion factor and the second congestion factor, perform a first fusion calculation based on the first congestion factor and the second congestion factor to obtain a congestion risk value, determine and execute a scheduling strategy for the robot in the target cold aisle based on the congestion risk value, and adjust the refrigeration equipment based on the congestion risk value.

9. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 7.

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