Device scheduling method, device, computer device and readable storage medium

Abstract

The present invention discloses a device scheduling method, apparatus, computer equipment and readable storage medium, the method comprising: defining device state parameters and continuously tracking and recording the actual value of each state parameter; establishing a mapping relationship between part features and state parameters of a part; defining a part feature baseline and an associated satisfaction rate function of a part based on the part features of the part; assigning a task strategy to each task; estimating the state parameters of the device after executing the task based on the current state parameters of the device and the task requirements; screening out devices whose estimated state parameters meet the mandatory strategy to obtain the remaining devices; obtaining a satisfaction rate function based on the compliance strategy; inputting the values ​​of the state parameters of the remaining devices into the satisfaction rate function to obtain the satisfaction value of each device; sorting the satisfaction values ​​of each device, and selecting the corresponding device to execute the task according to the sorted order. The present invention realizes decentralized management of equipment through the above method and extends the overall service life of core components.

Description

Technical Field

[0001] The present invention relates to the field of intelligent scheduling technology, and in particular to a device scheduling method, device, computer device and readable storage medium. Background Art

[0002] In the field of industrial machinery control, large equipment such as robotic arms, stackers, and automated guided vehicles (AGVs) are typically designed with maximum workloads in mind. However, in actual operation, the equipment's daily operating loads are often far below their theoretical upper limits. This discrepancy between design and usage has led to the widespread adoption of load balancing or task sharing strategies when performing non-device-specific tasks. For example, AGVs within the same area can interchangeably perform transport tasks, stackers can allocate tasks through cargo location scheduling, and depalletizing robots can flexibly allocate work based on host computer instructions.

[0003] While this strategy achieves fairness and efficiency in short-term task allocation, it presents a significant problem over the long term: As equipment wears out at consistent rates, the lifespan of its core components decreases with increasing synchronicity. When equipment reaches its lifespan threshold, centralized maintenance or replacement of core components is often necessary. However, core components are typically costly and require long lead times. Without adequate inventory, continuous demand can lead to equipment downtime due to spare part shortages, resulting in production interruptions and financial losses.

[0004] Currently, the industry mainly uses two methods to alleviate the above problems:

[0005] 1. By shortening the inspection interval, abnormal wear signs of core components can be detected early and stocked in advance. However, this method requires frequent disassembly inspections, increasing labor maintenance costs, and may affect production efficiency due to temporary downtime;

[0006] 2. Reduce downtime risk by increasing inventory levels of core components. However, this approach requires additional storage space, increases pressure on spare parts management and capital turnover, and may lead to waste of resources due to redundant procurement. Summary of the Invention

[0007] The purpose of the present invention is to provide an equipment scheduling method, device, computer equipment and readable storage medium, aiming to solve the problems of existing solutions such as high maintenance costs and capital turnover pressure.

[0008] In a first aspect, an embodiment of the present invention provides a device scheduling method, including:

[0009] Define equipment status parameters and continuously track and record the actual values ​​of each status parameter;

[0010] For parts of the equipment, a mapping relationship between part features and state parameters of the parts is established;

[0011] defining a part feature baseline of the part and an associated satisfaction rate function according to the part feature of the part;

[0012] Assigning a task strategy to each task, wherein each task strategy includes at least one mandatory strategy and at least one compliance strategy;

[0013] When allocating tasks, estimate the state parameters of the equipment after executing the task based on the current state parameters of the equipment and task requirements;

[0014] Screening out devices whose estimated state parameters meet the mandatory policy to obtain remaining devices;

[0015] Obtaining the corresponding part feature baseline and the associated satisfaction rate function according to the compliance strategy;

[0016] Inputting the values ​​of the state parameters of the remaining devices into the associated satisfaction rate function to obtain the satisfaction value of each device;

[0017] Sort the satisfaction values ​​of each device and select the corresponding device to perform the task according to the sorted order.

[0018] In a second aspect, an embodiment of the present invention provides a device scheduling apparatus, including:

[0019] Recording unit, used to define equipment status parameters and continuously track and record the actual values ​​of each status parameter;

[0020] An establishing unit, for establishing, for a part of the equipment, a mapping relationship between a part feature and a state parameter of the part;

[0021] A definition unit, configured to define a part feature baseline of the part and an associated satisfaction rate function according to the part feature of the part;

[0022] an allocating unit, configured to allocate task policies to each task, wherein each task policy includes at least one mandatory policy and at least one compliance policy;

[0023] The estimation unit is used to estimate the state parameters of the equipment after executing the task based on the current state parameters of the equipment and the task requirements when assigning tasks;

[0024] A screening unit, configured to screen out devices whose estimated state parameters comply with the mandatory policy, to obtain remaining devices;

[0025] An acquisition unit, configured to acquire a corresponding part feature baseline and an associated satisfaction rate function according to the compliance strategy;

[0026] an input unit, configured to input the values ​​of the state parameters of the remaining devices into an associated satisfaction rate function to obtain a satisfaction value of each device;

[0027] The selection unit is used to sort the satisfaction values ​​of each device and select the corresponding device to perform the task according to the sorted order.

[0028] In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the device scheduling method described in the first aspect when executing the computer program.

[0029] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the device scheduling method described in the first aspect above is implemented.

[0030] The present invention discloses an equipment scheduling method, apparatus, computer equipment and readable storage medium, the method comprising: defining equipment status parameters and continuously tracking and recording the actual value of each status parameter; establishing a mapping relationship between the part characteristics and status parameters of the parts of the equipment; defining the part characteristic baseline and the associated satisfaction rate function of the parts according to the part characteristics of the parts; assigning task strategies to each task, wherein each task strategy includes at least one mandatory strategy and at least one compliance strategy; when assigning tasks, estimating the state parameters of the equipment after executing the task according to the current state parameters of the equipment and the task requirements; screening out the equipment whose estimated state parameters meet the mandatory strategy to obtain the remaining equipment; obtaining the corresponding part characteristic baseline and the associated satisfaction rate function according to the compliance strategy; inputting the values ​​of the state parameters of the remaining equipment into the associated satisfaction rate function to obtain the satisfaction value of each equipment; sorting the satisfaction values ​​of each equipment, and selecting the corresponding equipment to execute the task according to the sorted order. The present invention realizes decentralized management of equipment through the above method, prolongs the overall service life of core components, and reduces the risk of downtime and spare parts costs due to centralized failures. At the same time, there is no need to increase the inspection density, so there is no need for frequent disassembly inspection, which reduces maintenance costs and improves production efficiency. In addition, there is no need to increase the number of stocks, so no additional storage space is taken up, reducing the pressure of spare parts management and capital turnover. The embodiment of the present invention also provides an equipment scheduling device, a computer-readable storage medium and a computer device, which have the above-mentioned beneficial effects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 A flowchart of the equipment scheduling method is provided;

[0033] Figure 2 The figure is a schematic block diagram of a device scheduling apparatus. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0035] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0036] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0037] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0038] See also Figure 1 This embodiment provides a device scheduling method, including:

[0039] S101: Define equipment status parameters and continuously track and record the actual values ​​of each status parameter;

[0040] In this embodiment, the state parameters related to the life of core components during operation are defined, and a continuous tracking and recording mechanism is established. The state parameters are used to measure the change of a single quantity. Taking the AGV as an example, first, based on the physical characteristics and usage scenarios of the AGV, the following state parameters are defined:

[0041] Loaded driving distance: The cumulative driving distance of the AGV when carrying cargo, in kilometers;

[0042] No-load driving distance: The cumulative driving distance of the AGV when it is not carrying any cargo, in kilometers;

[0043] Number of emergency stops: The number of emergency stops triggered by the AGV during operation due to safety or task interruption, in times;

[0044] Charging time: the cumulative time of each charging of AGV, in hours;

[0045] Number of pickup actions: the cumulative number of times the AGV performs the pickup task, in times;

[0046] Number of inventory actions: the cumulative number of times the AGV performs inventory tasks, in times;

[0047] Parking time (non-charging state): The cumulative time each time the AGV is parked, in hours.

[0048] These status parameters are collected in real time by the AGV's sensors and control system and continuously tracked and recorded by the host computer. For example, when an AGV performs a transport task, its navigation system calculates the distance traveled with or without a load based on the planned path and actual trajectory, and uploads this data to the host computer in real time. Emergency stops are detected and triggered by safety sensors, while charging time and the number of retrieval and storage operations are recorded by the AGV's power management system and task execution module, respectively.

[0049] For AGVs that are not performing tasks, the actual values ​​of their status parameters are accumulated in the "No Task Record". For example, when an AGV is running in an idle state, its no-load travel distance and number of emergency stops are recorded in the parameter queue corresponding to the "No Task Record".

[0050] When an AGV performs a specific task (such as transporting goods), task-related status parameters (such as the distance traveled and the number of pickups) are recorded separately in the "Actual Value" field for that task. Upon completion of the task, the system compares the "Actual Value" with the "Planned Value" (the theoretical value estimated when the task was assigned) and updates the final value to the "No Task Record" field to ensure data continuity.

[0051] Taking a robotic arm as an example, its core components include the main motor, joint bearings, and end effector. Their wear characteristics are closely related to the following state parameters:

[0052] Movement time under load: The cumulative movement time of each joint of the robot arm when carrying cargo (unit: hours);

[0053] No-load motion time: the cumulative motion time of each joint of the robot arm in the no-load state (unit: hours);

[0054] Repeated positioning times: the cumulative number of times the robot arm completes the same trajectory action (unit: times);

[0055] Emergency stop times: the number of joint emergency stop events triggered due to safety or task interruption (unit: times);

[0056] End effector gripping times: the cumulative number of times the robot arm performs gripping actions (unit: times);

[0057] End effector release times: The cumulative number of times the robot arm performs the release action (unit: times).

[0058] Specifically, the robotic arm collects the actual values ​​of these parameters in real time through joint encoders, force sensors, and the end-effector controller. For example, when the robotic arm is performing a handling task, its joint encoders record the time it moves under load, the force sensors monitor the load status, and the end-effector controller records the number of grips and releases. In the idle state, the robotic arm's unloaded movement time is continuously accumulated through the encoder until no task is recorded. Emergency stop events are triggered and counted by safety sensors, and the data is uploaded to the host computer in real time.

[0059] Furthermore, when the robotic arm performs a specific task (such as moving goods), parameters such as the load movement time and the number of repeated positioning of the robotic arm are recorded in the "actual value" field of the task; after the task is completed, the system compares the "actual value" with the "planned value" (the theoretical value estimated when the task is assigned) and updates it to "no task record".

[0060] When the robot arm is not performing a task, its parameters such as no-load motion time and number of emergency stops are directly accumulated to the queue corresponding to "no task record".

[0061] Taking a stacker crane as an example, its core components include the main motor, horizontal guide rails, vertical lifting system, and fork extension mechanism. Their wear characteristics are related to the following state parameters:

[0062] Horizontal load travel distance: the horizontal travel distance of the stacker when carrying goods (unit: meter);

[0063] Horizontal no-load travel distance: the horizontal travel distance of the stacker crane when it is empty (unit: meter);

[0064] Vertical load travel distance: the vertical lifting distance of the stacker when carrying goods (unit: meter);

[0065] Vertical no-load travel distance: vertical lifting distance of the stacker when it is empty (unit: meter);

[0066] Horizontal load acceleration / deceleration time: the cumulative acceleration / deceleration time of the stacker crane when carrying goods in horizontal movement (unit: seconds);

[0067] Horizontal no-load acceleration / deceleration time: the cumulative acceleration / deceleration time of the stacker crane when it moves horizontally without load (unit: seconds);

[0068] Vertical load acceleration / deceleration time: the cumulative acceleration / deceleration time of the stacker crane when carrying goods (unit: seconds);

[0069] Vertical no-load acceleration / deceleration time: the cumulative acceleration / deceleration time of the vertical lifting when the stacker is empty (unit: seconds);

[0070] Number of fork picking operations: the cumulative number of times the stacker performs the picking action (unit: times);

[0071] Number of fork releases: the cumulative number of times the stacker performs the release action (unit: times);

[0072] Number of emergency stops: The number of emergency stops triggered by the stacker crane due to safety or task interruption during operation, in times;

[0073] Parking time (non-charging state): The cumulative time the stacker crane is parked each time, in hours.

[0074] Specifically, the stacker crane uses position sensors, accelerometers, and a cargo location recognition system to collect actual parameter values ​​in real time. For example, when the stacker crane is performing a pickup task, the position sensor records the horizontal and vertical distance traveled, the accelerometer monitors acceleration and deceleration time, and the cargo location recognition system records the number of fork lifts. In the idle state, the stacker crane's unloaded distance traveled and the number of emergency stops are accumulated through sensor data and added to the "no-task record."

[0075] Furthermore, when executing specific tasks (such as picking up / putting goods), parameters such as the stacker crane's horizontal / vertical travel distance, acceleration / deceleration time, etc. are recorded in the "actual value" field of the task; after the task is completed, the system compares the "actual value" with the "planned value" (estimated value based on task path planning) and updates it to "no task record".

[0076] When the stacker crane is not performing a task, its parameters such as no-load driving distance and number of emergency stops are directly accumulated to the queue corresponding to "no task record".

[0077] S102: For a part of the equipment, establishing a mapping relationship between the part characteristics and the state parameters of the part;

[0078] Specifically, the components of the equipment (mainly core components) are identified, and then the characteristics of each component are associated with its relevant status parameters.

[0079] Taking a robotic arm as an example, its core components include the main motor, spherical bearings, and end effector. The main motor's component characteristics include wear characteristics, and the corresponding status parameters include: motion time under load, motion time without load, and number of emergency stops.

[0080] Then, the main motor wear characteristics and state parameters are mapped through the weighted sum function:

[0081] Mechanical main motor wear rate = (load movement time − 500) / 100 × 0.4 + (no-load movement time − 300) / 50 × 0.3 + (number of emergency stops − 20) / 10 × 0.3

[0082] The weight coefficients (0.4, 0.3, and 0.3) reflect the contribution of each parameter to the wear of the main motor. For example, load motion time has the greatest impact on motor life and therefore has the highest weight.

[0083] Furthermore, the part characteristics of the spherical bearing are: wear characteristics, and the corresponding state parameters include: the number of repeated positioning times and the rotation angle range under load.

[0084] Then, the wear characteristics and state parameters of the spherical bearing are mapped through the weighted sum function:

[0085] Spherical bearing wear rate = (number of repeated positioning times − 10,000) / 2,000 × 0.6 + (rotation angle range − 180) / 30 × 0.4

[0086] Among them, the weight coefficients (0.6, 0.4) indicate that the number of repeated positioning has a greater impact on the life of the spherical bearing.

[0087] Furthermore, the part characteristics of the end effector: wear characteristics, the corresponding state parameters include: number of clamping times, number of release times, and clamping force fluctuation range.

[0088] Then, the end effector wear characteristics and state parameters are mapped through the weighted sum function:

[0089] End effector wear rate = (number of grips − 5000) / 1000 × 0.5 + (number of releases − 5000) / 1000 × 0.3 + (gripping force fluctuation range − 10) / 5 × 0.2.

[0090] Taking the stacker crane as an example, the core components of the stacker crane include the main motor, horizontal guide rails, vertical lifting system and fork extension mechanism.

[0091] The wear level of a stacker crane's main motor is related to factors such as loaded horizontal travel distance, loaded horizontal acceleration time, loaded horizontal deceleration time, unloaded horizontal travel distance, unloaded horizontal acceleration time, unloaded horizontal deceleration time, parking time, and the number of emergency stops. Furthermore, the wear of the main motor has a weighted summation relationship with these attributes. Based on the specific capacity and parameters of the main motor, the stacker crane weight, the shelf conditions, and the cargo weight, the correlation between these parameters and the wear of the main motor is determined using big data statistics. These parameters are then used as weighted coefficients to sum up and derive a function for the wear of the main motor.

[0092] Furthermore, the component characteristics of the horizontal guide rail are: wear characteristics, and the corresponding state parameters include: horizontal no-load driving distance and horizontal emergency stop times.

[0093] Then, the horizontal guide rail wear characteristics and state parameters are mapped through the weighted sum function:

[0094] Horizontal guide rail wear rate = (no-load distance − 5000) / 1000 × 0.6 + (number of emergency stops − 10) / 5 × 0.4.

[0095] Furthermore, the part characteristics of the fork mechanism: wear characteristics, the corresponding state parameters include: number of pick-up times, number of put-down times and the fluctuation range of the telescopic stroke.

[0096] Then, the wear characteristics and state parameters of the fork mechanism are mapped through the weighted sum function:

[0097] Wear rate of the fork extension mechanism = (number of pickups − 3000) / 600 × 0.5 + (number of placements − 3000) / 600 × 0.3 + (extension stroke fluctuation range − 10) / 2 × 0.2.

[0098] Taking AGV as an example, the core components of the stacker include the main battery, drive motor and navigation system.

[0099] Part characteristics of the drive motor: wear characteristics, and the corresponding status parameters include: loaded driving distance, unloaded driving distance and number of emergency stops.

[0100] Then, the drive motor wear characteristics and state parameters are mapped through the weighted sum function:

[0101] Drive motor wear rate = (load distance − 5000) / 1000 × 0.4 + (no-load distance − 3000) / 500 × 0.3 + (number of emergency stops − 50) / 10 × 0.3;

[0102] After completing the mapping of part features and state parameters, the data collected by the host computer will simultaneously calculate the value of the corresponding device feature and append it to the corresponding feature queue for statistics.

[0103] S103: defining a part feature baseline of the part and an associated satisfaction rate function according to the part feature of the part;

[0104] In this embodiment, for parts that define equipment characteristics, a corresponding part feature baseline is defined. This baseline must include at least one part feature, but not all of the part's features. This is because some features may only be used for reference and statistical purposes and do not affect the actual lifespan of the part. Using core components of industrial equipment (such as robotic arm main motors, stacker crane main motors, and AGV main batteries) as an example, this paper details how to define a part feature baseline based on part characteristics and associate it with a corresponding satisfaction rate function, providing dynamic data support for task allocation strategies.

[0105] Based on the wear characteristics of the robot arm's main motor, the good part feature baseline, the attention part feature baseline, and the replacement part feature baseline are defined. The relevant status parameters include the movement time under load, the movement time under no load, and the number of emergency stops.

[0106] Good part feature baseline: load movement time 0-500 hours (positive sequence; this value is related to the specific calculation function), no-load movement time 0-300 hours (positive sequence), number of emergency stops 0-20 times (positive sequence);

[0107] Note the part feature baseline: load movement time 501-700 hours (positive sequence), no-load movement time 301-400 hours (positive sequence), number of emergency stops 21-40 times (positive sequence);

[0108] Baseline characteristics of parts requiring replacement: load movement time 701-800 hours (positive sequence), no-load movement time 401-500 hours (positive sequence), number of emergency stops 41-60 times (positive sequence).

[0109] In some embodiments, the main battery of the AGV is related to component characteristics such as charge and discharge cycles (a state parameter mapping related to charging, which directly affects the lifespan), high-power consumption work (a state parameter mapping related to workload, which reflects the proportion of high discharge time), and total service time (directly related to the remaining lifespan).

[0110] For the AGV main battery, multiple part feature baselines such as good, caution, and replacement are defined. Each part feature baseline is associated with the above part features, and the above values ​​and satisfaction rate calculation functions are specified respectively.

[0111] The values ​​represent the upper and lower limits and sorting of each part feature associated with this part feature baseline. For example, a good part feature baseline represents 0-200 charge-discharge cycles (in positive order), 0-20,000 high-power consumption operating values ​​(in positive order; this value is related to the specific calculation function and is for reference only), and 0-300 days of service life (in positive order). The corresponding values ​​for the note part feature baseline are 201-300 cycles (in positive order), 20,001-30,000 (in positive order), and 301-400 days (in positive order). The corresponding values ​​for the replacement part feature baseline are 301-350 cycles (in positive order), 30,001-35,000 (in positive order), and 401-500 days (in positive order).

[0112] The satisfaction rate function is a function related to the above values. The input of this function is each of the above values, and the output range is [0, 1], usually rounded to two significant digits. For example, for the AGV main battery's critical component feature baseline, the satisfaction rate function can be set as: (charge and discharge cycles - 200) / 100 * 0.5 + (high power consumption value - 20,000) / 10,000 * 0.3 + (total service time - 300) / 100 * 0.2. This function will be 0 when the value changes from good to critical, and close to 1 when it reaches the highest critical value.

[0113] S104: assigning a task strategy to each task, wherein each task strategy includes at least one mandatory strategy and at least one compliance strategy;

[0114] In this embodiment, taking the task allocation of industrial equipment (such as robotic arms, stackers, and AGVs) as an example, this paper explains in detail how to assign task strategies to different task types. Each strategy includes at least one mandatory strategy and one compliance strategy, and defines the weight values ​​between strategies and device screening rules.

[0115] Assign corresponding strategy combinations based on task types (such as "work task," "pickup task," and "new motor running-in task"). The following is an example of a typical task strategy configuration:

[0116] Task type 1: Work tasks (such as robotic arms performing transportation) use a mandatory strategy.

[0117] The mandatory strategy is implemented by satisfying all requirements: after the device completes the task, the "load movement time" and "number of emergency stops" of its main motor must both meet the "good" baseline range (load movement time 0-500 hours, number of emergency stops 0-20 times).

[0118] The use of mandatory strategies in work tasks can screen out equipment that has not reached the wear threshold, thus avoiding premature failure of core components due to task execution.

[0119] Furthermore, task type 2: picking tasks (such as stacker cranes performing storage and retrieval) adopts a mandatory strategy:

[0120] The mandatory strategy is implemented through partial satisfaction: after the equipment completes the task, the "horizontal load driving distance" or "vertical load driving distance" of its main motor must meet the "attention" baseline range (horizontal load driving distance 10001-12000 meters, vertical load driving distance 5001-6000 meters).

[0121] The mandatory strategy used in the pickup task can screen out equipment that is nearing the end of its lifespan, and prioritize tasks to accelerate its status parameters to reach the "replacement required" baseline, facilitating centralized maintenance.

[0122] Furthermore, Task Type 3: New Motor Running-in Task (such as AGV main battery verification) adopts a mandatory strategy:

[0123] The mandatory policy is implemented through partial non-satisfaction: after the device performs the task, the "number of charge and discharge cycles" or "high power consumption working time" of its main battery must not meet the "good" baseline range (0-200 charge and discharge cycles, 0-20,000 hours of high power consumption working time).

[0124] The new motor running-in task uses a mandatory strategy to screen out equipment that has entered the "caution" or "needs replacement" baseline, and prioritize tasks to verify its performance stability.

[0125] In this embodiment, mandatory policies refer to policies that must be met. There can be any number of mandatory policies. Mandatory policies are used to filter out inappropriate policies. They are equivalent to policy screening conditions. For example, a user wants equipment that is close to its design life to take on more work so that it can reach its design life, but does not want equipment that has reached its design life to be affected. In the corresponding policy, it is necessary to exclude equipment that does not meet the requirements (has reached its design life) through a combination of mandatory policies. A compliance policy refers to a policy that determines the degree of compliance of various status parameters of this equipment with the part feature baseline after completing this task. There can be any number of compliance policies.

[0126] In this embodiment, the method further includes associating each mandatory strategy with a part feature baseline of a part, and associating each compliance strategy with a part feature baseline of a part.

[0127] Each mandatory policy is only associated with one feature baseline of a part, which can ensure that there are clear entry / exit conditions when assigning tasks. For example: a fully satisfied mandatory policy can be used to restrict high-wear equipment from taking on new tasks to avoid exceeding the component life limit. A partially unsatisfied mandatory policy can be used to screen equipment that needs priority maintenance and temporarily remove it from the task pool. Each compliance policy focuses on a part feature baseline and can convert the equipment status into a comparable priority indicator by calculating the satisfaction value with weighted calculation. For example: in regular task assignment, the weight value of the motor wear baseline compliance policy can be set to 0.6, and the weight value of the battery health baseline compliance policy can be set to 0.4, so as to comprehensively evaluate the applicability of the equipment. Reverse sorting allows low-satisfaction equipment (such as new equipment with less wear) to obtain tasks first, and forward sorting does the opposite, so that it can flexibly adapt to needs such as balanced wear or giving priority to old equipment.

[0128] In this embodiment, each mandatory policy includes any of the following implementation methods:

[0129] All satisfied: After completing the task, all status parameters of the equipment meet the requirements of the corresponding part feature baseline;

[0130] Partially satisfied: After completing the task, at least one of the equipment's status parameters meets the requirements of the corresponding part feature baseline;

[0131] Partially unsatisfied: After completing the task, at least one of the status parameters of the equipment does not meet the requirements of the corresponding part feature baseline;

[0132] All unsatisfactory: After completing the task, all status parameters of the equipment do not meet the requirements of the corresponding part feature baseline.

[0133] By requiring all associated status parameters to meet baseline requirements after completing a task, the system ensures that core components remain within safe operating ranges, preventing hidden wear or failures caused by a single parameter exceeding a limit. For example, when assigning a high-load task to a stacker crane, the "all satisfied" mandatory policy can be linked to the main motor wear baseline, requiring parameters such as loaded travel distance and acceleration / deceleration time to remain within baseline thresholds. This prevents premature motor aging due to overload.

[0134] Partial fulfillment allows devices to evolve towards a specific state by ensuring that at least one parameter meets the target baseline after completing a task. For example, setting a partial fulfillment mandatory policy for an AGV with a motor replacement and linking it to the new motor's pending verification baseline prioritizes tasks, allowing the device to quickly accumulate working time and high-load duration, accelerating verification of motor performance.

[0135] Partial failure detection identifies potential failure risks and triggers maintenance procedures by detecting if at least one parameter of a device falls below its baseline after completing a task. For example, if the charging time parameter of an AGV exceeds the battery baseline threshold after completing a task, the system will use the partial failure strategy to mark the device and force it into the maintenance queue, thus avoiding interruptions caused by battery degradation.

[0136] By prohibiting equipment from executing tasks when all parameters fail to meet the baseline, this policy can forcibly exclude highly worn, aged, or faulty equipment from the task pool, preventing production accidents. For example, a general task allocation strategy using the "all fail" policy prevents new motor equipment that has not completed verification from entering the regular task pool, thus avoiding task failures due to unknown component status.

[0137] In some embodiments, a single task policy can be mapped to multiple devices, eliminating the need to specify the same task policy for each device. In the absence of specific requirements, most devices will use the same task policy. Furthermore, due to some special requirements (such as accelerating the aging of specific devices), different policies may be assigned to these specific devices. Therefore, the same task must support multiple policies, while disabling duplication of these policies across devices. This means that a device can only have at most one task policy associated with it under the same task.

[0138] In some embodiments, it further includes:

[0139] If multiple task policies are associated with the same task, a weight value is assigned to each task policy, and the mandatory policy of each task policy is used to ensure that a single device meets the conditions of only one task policy.

[0140] By assigning weights to different task strategies (e.g., "Assign more tasks to new motor equipment" with a weight of 2, "General task assignment" with a weight of 1), the system can dynamically adjust scheduling logic based on production demand. Strategies with higher weights are triggered first during task assignment, ensuring that urgent tasks (e.g., equipment run-in, verification after fault repair) or critical operations (e.g., production of high-value-added orders) are prioritized. Furthermore, by enforcing strategies that require a device to meet the conditions of only one task strategy (e.g., the "New Motor Pending Verification" strategy requires "partial satisfaction" of the baseline, while "General Task Assignment" requires "complete non-satisfaction"), the system ensures that ambiguous scenarios such as "a single device meeting multiple strategies simultaneously" are avoided during task assignment, thus avoiding scheduling errors or resource waste caused by policy conflicts.

[0141] In some embodiments, the weight values ​​of each task strategy are automatically adjusted using a preset algorithm based on the actual values ​​of each state parameter, specifically including:

[0142] When the average wear of a certain type of core component in the device cluster exceeds the preset threshold, the policy weight of the high-load tasks associated with the component is automatically reduced, and the policy weight corresponding to the low-wear devices is increased;

[0143] When the urgency of a task exceeds the threshold, the weight of the quick response strategy is temporarily increased, and the equipment closest to the task point or with the most stable status is prioritized for scheduling;

[0144] When a low electricity price period is detected, the weight of the energy-saving strategy is automatically increased. The strategy associates the status parameters related to the energy consumption of the equipment (such as no-load driving energy consumption and standby power) through the compliance strategy, and prioritizes the scheduling of low-energy consumption equipment in reverse order.

[0145] By monitoring the average wear of core components in a cluster (such as the average load-carrying distance of AGV motors and the average number of charge and discharge cycles of stacker crane main batteries), the system automatically reduces the weight of high-load task strategies when these exceed preset thresholds, forcing tasks to be allocated to less-wearable equipment. For example, if the average wear of AGV motors in a workshop reaches 70% of the baseline value, the system automatically reduces the weight of the "high-load task strategy" and increases the weight of the "low-load equipment priority strategy," thus preventing all equipment from entering the high-wear range.

[0146] The system converts task urgency (such as the number of days remaining to order delivery and the cost of production line downtime) into a weighted gain factor, and uses a preset algorithm (such as an exponential function) to increase the weight of the "rapid response strategy" in real time. For example, if a rush order has less than 24 hours remaining to deliver, the corresponding strategy weight will increase from the default of 50 to 100, forcing the host computer to prioritize the equipment closest to the task point (regardless of its wear and tear), ensuring timely completion of the task and avoiding the risk of default.

[0147] Furthermore, the dynamic weight value adjustment algorithm includes:

[0148] Based on the standard deviation of the wear of the core components of the equipment cluster, when the standard deviation is less than the threshold, the task strategy weights are evenly distributed; when the standard deviation exceeds the threshold, the weights of the corresponding strategies for low-wear equipment are increased in reverse order of wear, and the weight adjustment range is proportional to the standard deviation.

[0149] The standard deviation is used to quantify the discrete degree of wear of the core components of the equipment cluster (such as the standard deviation of the loaded driving distance of the AGV motor). When the standard deviation is less than the threshold, the wear distribution is determined to be uniform. At this time, the task strategy weights are evenly distributed to avoid scheduling fluctuations caused by excessive intervention. For example, when the standard deviation of the "loaded horizontal driving distance" of the stacking crane cluster is less than 100 meters, the system maintains consistent task weights for each device to ensure stable production. At the same time, a preset standard deviation threshold (such as 20% of the mean wear value) is used as the critical point for strategy switching: when the standard deviation ≤ the threshold, the weights are evenly distributed, maintaining the traditional "task equalization" strategy and avoiding unnecessary adjustments to normal wear conditions; when the standard deviation > the threshold, the wear leveling strategy is activated, and the task allocation is forcibly intervened through weight adjustment.

[0150] In some specific embodiments, several AGVs have had their motors replaced. The status parameters for these devices include 0 hours of high-load operation, 0 hours of low-load operation, and 0 hours of total operation. The characteristic operating time of the motor components includes, but is not limited to, the three aforementioned status parameters. Engineers need to observe these motors operating for at least 50 hours and at least 10 hours of high-load operation. To expedite this process, a setting is required, hoping that the host computer will allocate more tasks to these devices in the short term.

[0151] First, define a part characteristic baseline, "New Motor to be Verified": This refers to the motor's part characteristics. The operating hours are [0, 50] hours (in positive order), and the high-load operating hours are [0, 10] hours (in positive order). The satisfaction rate function is: operating hours / 100 + high-load operating hours / 20.

[0152] Next, define the task strategy "Assign more tasks to new motor devices": its enforcement strategy is set to "New motor to be verified" and the passing method is partially satisfied; the compliance strategy is set to "New motor to be verified" and the weight is 1 (because there is only one compliance strategy, the weight is meaningless); the sorting method is reverse order (the higher the compliance, the lower the priority); and map this device to the AGV with the replaced motor.

[0153] At the same time, for the existing task strategy "General Task Assignment" (the default assignment strategy), its mandatory strategy adds a new motor for verification, with the pass method set to "All Not Satisfied". This task strategy is mapped to all AGVs by default and does not require adjustment.

[0154] Then, ensure that both the "Assign More Tasks to New Motor Devices" and "General Task Assignment" task policies are associated with the work task. The former has a weight of 2, and the latter has a weight of 1. Although the two task policies now overlap in the associated devices, due to the mandatory policy settings, the mandatory policy for the "Assign More Tasks to New Motor Devices" task policy passes as partially satisfied, while the mandatory policy for the "General Task Assignment" task policy passes as completely unsatisfied. Therefore, it's impossible for a device to meet the mandatory policy requirements of both task policies simultaneously, and this setting is effectively enforced (i.e., there won't be a situation where the selected device can't determine which task policy it meets).

[0155] S105: When assigning tasks, estimate the state parameters of the device after executing the task based on the current state parameters of the device and the task requirements;

[0156] Specifically, after the system is started, it will continuously collect data statistics for each status parameter of each device. This process can be divided into two scenarios:

[0157] If the device is executing a task and there is a record of the task in the system, the system will accumulate and record the state parameter changes (such as driving distance, working time, etc.) generated during the task execution into the "Actual Value" field of the task;

[0158] If a device doesn't execute a task or the corresponding task record doesn't exist in the system, the changes in the status parameter are accumulated in the device's "No Task Record." For example, the "Number of Inventory Actions" for stacker crane No. 1 will have a separate "No Task Record." Separate records are created for each task in the system, containing both the "Actual Value" (the actual data after the task is executed) and the "Planned Value" (the estimated data when the task was assigned).

[0159] When a task needs to be assigned, it is processed according to the following logic:

[0160] Based on the relevant attributes of the task (such as the stacker crane's current position, target location, and task type), the system calculates the changes in various state parameters that may result from executing the task. For example, by analyzing the stacker crane's trajectory, load, and operating movements, it estimates the changes in parameters such as "loaded horizontal travel distance" and "high-load operating time." These estimates are written into the task's "Planned Value" field and serve as a basis for subsequent policy selection.

[0161] Then, the system finds the corresponding task strategy based on the task type and traverses all devices mapped by the task strategy. For each device, the system calculates the total value of its current status parameters. The specific rules are as follows:

[0162] The current status parameter value of each device is the sum of two parts of data:

[0163] 1) Value without task record: that is, the value of the state parameter change accumulated by the device during the period when no task is executed;

[0164] 2) Maximum value of task records: For all tasks executed by the device, the system takes the larger of the "actual value" and the "planned value" in each task record and adds them together. This design ensures that even if a task is not fully executed, policy decisions can be made based on the estimated value.

[0165] After obtaining the current state parameter value of each device, the state parameter of each device after executing the task is estimated based on the current state parameter value of each device and the assigned task requirements.

[0166] S106: Filter out devices whose estimated state parameters meet the mandatory policy to obtain remaining devices;

[0167] Specifically, the estimated state parameters of each device after executing the task are substituted into each mandatory policy for inspection. If the inspection fails, the device is abandoned, thereby obtaining the remaining devices.

[0168] In some embodiments, filtering out devices whose estimated state parameters meet the mandatory policy, and obtaining the remaining devices includes:

[0169] If no device meets the mandatory policy after screening, the implementation method of the mandatory policy is changed, and devices whose estimated state parameters meet the changed mandatory policy are re-screened to obtain the remaining devices.

[0170] When the original mandatory policy results in unavailable equipment, automatic policy switching can prevent task queue backlogs or production interruptions caused by temporarily substandard equipment status. Flexible policy adjustments allow the system to dynamically balance equipment maintenance needs with the urgency of task delivery, avoiding inefficient resource utilization caused by overly conservative policy settings.

[0171] In some specific embodiments, when a pickup task request arrives, the system estimates that executing this task will increase the working time of each stacker crane by 0.5 hours, the high-load working time by 0.2 hours, and the low-load working time by 0.3 hours.

[0172] Then, the estimated state parameters of each stacker after the task is executed are calculated (for example, the current working time of stacker A = 50 hours, which becomes 50.5 hours after the task is executed; the high-load working time = 10 hours, which becomes 10.2 hours after the task is executed).

[0173] Then, based on the partial fulfillment rule, devices with at least one parameter that does not exceed the baseline upper limit are screened. Assume that the estimated status parameters of the three new motor stackers all exceed all baseline upper limits (for example, operating hours = 50.5 hours, high-load operating hours = 10.2 hours). The screening fails, and no device meets the policy.

[0174] Then change the mandatory policy passing mode from partial satisfaction to partial non-satisfaction, that is, filter out devices with at least one parameter exceeding the baseline upper limit.

[0175] S107: Obtaining the corresponding part feature baseline and the associated satisfaction rate function according to the compliance strategy;

[0176] Since each conformity strategy is associated with a part feature baseline of a part, the corresponding part feature baseline and the associated satisfaction rate function can be obtained according to the conformity strategy.

[0177] S108: Inputting the values ​​of the state parameters of the remaining devices into the associated satisfaction rate function to obtain the satisfaction value of each device;

[0178] Specifically, the values ​​of the state parameters of the remaining devices are input into the associated satisfaction rate function to obtain the satisfaction value of each device, including:

[0179] Obtain the actual value of the associated state parameter according to the part feature baseline corresponding to the compliance strategy;

[0180] Determining whether the actual value of the status parameter of the device exceeds a predetermined range;

[0181] If the actual value of the state parameter of the device exceeds the predetermined range, the value in the predetermined range closest to the actual value is input into the satisfaction rate function;

[0182] If the actual value of the state parameter of the device does not exceed the predetermined range, the actual value is input into the satisfaction rate function.

[0183] If, after executing this task, the expected high-power consumption value is 20005, which exceeds the upper limit of the "good" high-power consumption value (20000), the upper limit (20000) will be used to calculate the satisfaction rate. If, after executing this task, the charge and discharge cycles are 100, which is within the "good" charge and discharge cycle range, the value (100) will be used to calculate the satisfaction rate. This design prevents the satisfaction rate function from calculating the degree of compliance outside its value range [0, 1], ensuring the continuity of the calculation process.

[0184] S109: Sort the satisfaction values ​​of the devices, and select the corresponding device to execute the task according to the sorted order.

[0185] Specifically, the system returns the policy satisfaction value of each device that has not been abandoned to the host computer for reference in device selection. If the host computer has no other sorting strategies to consider, it can directly use the sorting method specified by the task strategy (such as from largest to smallest or from smallest to largest) to sort these policy satisfaction values ​​and select the optimal device. If the host computer needs to consider other conditions (such as proximity allocation), it sends these policy satisfaction values ​​and the sorting method specified by the task strategy to the host computer for comprehensive sorting.

[0186] In some specific embodiments, when a new "work task" Task 001 request arrives:

[0187] First, make an estimate. It is estimated that Task 001 will result in +0.1 hours of work time, +0.05 hours of high-load work time, +0.05 hours of low-load work time, +1 pick-up action, +1 put-down action, etc. Create a record for Task 001 and record the above values ​​in the "Planned Value" of this task.

[0188] Next, we examine the task policies mapped to "Work Tasks" and find "Assign More Tasks to New Motor Devices" (weighted 2) and "General Task Assignment" (weighted 1). We perform a mandatory policy check on all devices in both task policies and find that in "Assign More Tasks to New Motor Devices," AGVs with recently replaced motors meet the requirements; in "General Task Assignment," all other AGVs meet the requirements. We calculate the satisfaction values ​​of each device in the corresponding task policy and find that in "Assign More Tasks to New Motor Devices," AGVs with newer motors have lower satisfaction values. Since this task policy sorts in reverse order, devices with lower satisfaction values ​​are ranked first. The situation in "General Task Assignment" is omitted.

[0189] If this strategy alone is used for allocation, the higher weight given to "assigning more tasks to devices with newer motors" will result in direct assignments to the devices ranked highest in the strategy. In this example, this would be the AGV with the newest motor. However, in practice, the host computer typically considers additional factors (such as device status and relative distance), including the weights of the task strategies and the ranking of the devices within each strategy. Ultimately, AGVs with newer motors are more likely to receive tasks.

[0190] Next, sorting the satisfaction values ​​of the devices and selecting the corresponding devices to perform the tasks according to the sorted order includes: accumulating the actual values ​​of the state parameters of the task records into the no-task record of the device and deleting the task record.

[0191] The actual values ​​of task records (such as the load travel distance of AGVs and the number of inventory movements of stackers) are accumulated in real time to the equipment's non-task records to ensure that the equipment status parameters always reflect the total wear and tear over the entire life cycle, avoiding baseline matching errors caused by unupdated task data.

[0192] In some embodiments, sensors are used to collect real-time data on vibration frequency, temperature change curves, and current fluctuations during the operation of industrial equipment, wherein:

[0193] The vibration frequency is measured by a piezoelectric or MEMS sensor with a range of 0-50Hz and a sampling frequency of 1Hz to 1kHz;

[0194] The temperature change curve is recorded by a digital temperature sensor or fiber Bragg grating sensor with an accuracy of ±0.5°C and a sampling interval of 1 minute;

[0195] Current fluctuations are detected by Hall effect sensors or current transformers with a range of 0-100A and a resolution of 0.1A;

[0196] Next, at least one part feature baseline is defined for the core component of the device. Each baseline includes upper and lower limits for a corresponding non-traditional parameter, and a satisfaction rate function is defined associated with the baseline. The satisfaction rate function is used to quantify the degree of match between the non-traditional parameter and the baseline.

[0197] Then, multiple task policies are defined, each of which is associated with at least one mandatory policy and / or compliance policy;

[0198] Then, based on the degree of matching between the non-traditional parameters and the part feature baseline, the weight value of the task strategy is dynamically adjusted, where:

[0199] When the vibration frequency exceeds the preset threshold (for example, >40Hz), the slope of the temperature change curve exceeds the safe range (for example, ΔT / Δt>5°C / min), or the current fluctuation amplitude exceeds the baseline upper limit (for example, >90% of the rated current), the weight value of the corresponding task strategy of the device is reduced to reduce the frequency of task allocation;

[0200] When all non-traditional parameters are within the baseline range, the weight value of the task strategy corresponding to the device is increased to increase the frequency of task allocation.

[0201] Define the baseline range and satisfaction function for each non-traditional parameter, for example:

[0202] Vibration frequency baseline: normal range 0-40Hz, satisfaction rate function is:

[0203] Vibration satisfaction rate = (actual frequency − baseline lower limit) / (baseline upper limit − baseline lower limit) × weight coefficient;

[0204] Temperature change curve baseline: safety slope ≤ 5℃ / min, satisfaction rate function is:

[0205] Temperature satisfaction rate = (actual slope − baseline lower limit) / (baseline upper limit − baseline lower limit) × weight coefficient;

[0206] Current fluctuation baseline: The stable range is ±10% of the rated current, and the satisfaction rate function is:

[0207] Current satisfaction rate = (actual fluctuation amplitude − baseline lower limit) / (baseline upper limit − baseline lower limit) × weight coefficient;

[0208] Among them, the weight coefficient is allocated according to the impact of each parameter on the health of the equipment.

[0209] A clear baseline range is defined for each non-traditional parameter (e.g., vibration 0-40Hz, temperature slope ≤5°C / min). A linear normalization formula (e.g., vibration satisfaction rate = actual frequency / 40 × weight coefficient) is used to convert parameter values ​​into quantitative indicators in the [0, 1] range, facilitating automatic comparison and ranking by the system. For example, when a device's vibration frequency is 30Hz, the satisfaction rate is 0.75, indicating that its vibration condition is in the good range (the weight coefficient can be set to 0.8). However, at a frequency of 45Hz, the satisfaction rate is 1.125 (exceeding the upper limit), triggering an alert. Furthermore, trend analysis of non-traditional parameters (e.g., the increase in vibration spectrum energy over time) can proactively identify potential faults and reduce the proportion of reactive maintenance from 70% to below 30%. For example, when the vibration frequency of an AGV drive motor in a factory gradually increased from 20Hz to 35Hz (approaching the 40Hz threshold), the system triggered a predictive maintenance strategy two months in advance, scheduling bearing replacements and avoiding unexpected production line downtime (a single downtime loss of approximately 50,000 yuan).

[0210] The device scheduling method of this embodiment can be used in the following scenarios:

[0211] When certain devices are approaching their inspection time, by adjusting the task strategies mapped to these devices, these devices can be made to take on more tasks, thereby increasing the effectiveness of the inspection.

[0212] After routine maintenance of certain equipment is completed, by adjusting the task strategies mapped to these equipment in a short period of time, these equipment can take on more tasks to check their status after maintenance and accelerate the running-in process.

[0213] Under normal circumstances, the task strategies of some equipment can be adjusted to make them take on more tasks to prevent the core parts of all equipment from being damaged at the same time.

[0214] This embodiment achieves decentralized equipment management through the above-mentioned implementations, extending the overall service life of core components and reducing the risk of downtime and spare parts costs due to centralized failures. Furthermore, there is no need to increase inspection frequency, eliminating the need for frequent disassembly inspections, reducing maintenance costs and improving production efficiency. Furthermore, there is no need to increase inventory, eliminating the need for additional storage space and reducing the pressure on spare parts management and capital turnover.

[0215] See also Figure 2 This embodiment provides a device scheduling apparatus 200, including:

[0216] Recording unit 201, used to define device status parameters and continuously track and record the actual values ​​of each status parameter;

[0217] An establishing unit 202 is used to establish a mapping relationship between a part feature and a state parameter of a part of the equipment;

[0218] A definition unit 203, configured to define a part feature baseline of the part and an associated satisfaction rate function according to the part feature of the part;

[0219] an allocating unit 204, configured to allocate task policies to each task, wherein each task policy includes at least one mandatory policy and at least one compliance policy;

[0220] The estimation unit 205 is used to estimate the state parameters of the device after executing the task based on the current state parameters of the device and the task requirements when the task is assigned;

[0221] A screening unit 206 is configured to screen out devices whose estimated state parameters comply with the mandatory policy, thereby obtaining remaining devices;

[0222] An acquisition unit 207 is configured to acquire a corresponding part feature baseline and an associated satisfaction rate function according to the compliance strategy;

[0223] An input unit 208 is configured to input the values ​​of the state parameters of the remaining devices into an associated satisfaction rate function to obtain a satisfaction value of each device;

[0224] The selection unit 209 is configured to sort the satisfaction values ​​of the devices and select the corresponding device to perform the task according to the sorted order.

[0225] Furthermore, it also includes:

[0226] The association unit is used to associate each enforcement strategy with a part feature baseline of a part, and to associate each compliance strategy with a part feature baseline of a part.

[0227] Furthermore, each mandatory policy includes any of the following implementation methods:

[0228] All satisfied: After completing the task, all status parameters of the equipment meet the requirements of the corresponding part feature baseline;

[0229] Partially satisfied: After completing the task, at least one of the equipment's status parameters meets the requirements of the corresponding part feature baseline;

[0230] Partially unsatisfied: After completing the task, at least one of the status parameters of the equipment does not meet the requirements of the corresponding part feature baseline;

[0231] All unsatisfactory: After completing the task, all status parameters of the equipment do not meet the requirements of the corresponding part feature baseline.

[0232] Furthermore, the input unit 208 includes:

[0233] The actual value acquisition subunit is used to obtain the actual value of the associated state parameter according to the part feature baseline corresponding to the compliance strategy;

[0234] a judging subunit, configured to judge whether an actual value of a status parameter of the device exceeds a predetermined range;

[0235] a first input subunit, configured to input a value in the predetermined range closest to the actual value into the satisfaction rate function if the actual value of the state parameter of the device exceeds a predetermined range;

[0236] The second input subunit is configured to input the actual value into the satisfaction rate function if the actual value of the state parameter of the device does not exceed a predetermined range.

[0237] Furthermore, the selection unit 209 includes:

[0238] The accumulation subunit is used to accumulate the actual values ​​of various status parameters of the task record into the no-task record of the device.

[0239] Furthermore, it also includes:

[0240] The weight value allocation unit is used to allocate a weight value to each task policy when the same task is associated with multiple task policies, and ensure that a single device only meets the conditions of one task policy through the mandatory policy of each task policy.

[0241] Furthermore, the screening unit 206 includes:

[0242] The changing subunit is configured to change the implementation mode of the mandatory policy if no device is found to be in compliance with the mandatory policy after screening, and to re-screen devices whose estimated state parameters are in compliance with the modified mandatory policy to obtain the remaining devices.

[0243] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-mentioned devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0244] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When executed, the computer program can implement the methods provided in the above embodiments. The storage medium may include a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, among other media capable of storing program code.

[0245] The present invention further provides a computer device that may include a memory and a processor. The memory stores a computer program, and the processor, when invoking the computer program in the memory, can implement the method provided in the above embodiment. Of course, the computer device may also include various network interfaces, a power supply, and other components.

[0246] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

[0247] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprising" or any other variations thereof are intended to cover non-exclusive.

[0248] Inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a..." does not preclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

Claims

1. A device scheduling method, characterized in that: include: Define equipment status parameters and continuously track and record the actual values ​​of each status parameter; For parts of the equipment, a mapping relationship between part features and state parameters of the parts is established; defining a part feature baseline of the part and an associated satisfaction rate function according to the part feature of the part; Assigning a task strategy to each task, wherein each task strategy includes at least one mandatory strategy and at least one compliance strategy; When allocating tasks, estimate the state parameters of the equipment after executing the task based on the current state parameters of the equipment and task requirements; Screening out devices whose estimated state parameters meet the mandatory policy to obtain remaining devices; Obtaining the corresponding part feature baseline and the associated satisfaction rate function according to the compliance strategy; Inputting the values ​​of the state parameters of the remaining devices into the associated satisfaction rate function to obtain the satisfaction value of each device; Sort the satisfaction values ​​of each device and select the corresponding device to perform the task according to the sorted order; The method further includes: associating each mandatory strategy with a part feature baseline of a part, and associating each conformance strategy with a part feature baseline of a part; Each mandatory policy includes any of the following implementation methods: All satisfied: After completing the task, all status parameters of the equipment meet the requirements of the corresponding part feature baseline; Partially satisfied: After completing the task, at least one of the equipment's status parameters meets the requirements of the corresponding part feature baseline; Partially unsatisfied: After completing the task, at least one of the status parameters of the equipment does not meet the requirements of the corresponding part feature baseline; All unsatisfactory: After completing the task, all status parameters of the equipment do not meet the requirements of the corresponding part feature baseline; Inputting the values ​​of the state parameters of the remaining devices into the associated satisfaction rate function to obtain the satisfaction value of each device includes: Obtain the actual value of the associated state parameter according to the part feature baseline corresponding to the compliance strategy; Determining whether the actual value of the status parameter of the device exceeds a predetermined range; If the actual value of the state parameter of the device exceeds the predetermined range, the value in the predetermined range closest to the actual value is input into the satisfaction rate function; If the actual value of the state parameter of the device does not exceed the predetermined range, the actual value is input into the satisfaction rate function; Also includes: If multiple task policies are associated with the same task, a weight value is assigned to each task policy, and the mandatory policy of each task policy is used to ensure that a single device meets the conditions of only one task policy; Define the status parameters related to the life of core components during equipment operation. Status parameters are used to measure changes in a single quantity. Mapping wear characteristics and state parameters through a weighted sum function; Define good parts feature baseline, attention parts feature baseline and replacement parts feature baseline according to wear characteristics; Among them, the part feature is the wear feature; The satisfaction rate function is a weighted function of the value of each part feature relative to the baseline representing the part feature, the input of the function is the value of each part feature, and the output range is [0, 1].

2. The device scheduling method according to claim 1, characterized in that: The device whose estimated state parameters meet the mandatory policy is screened out to obtain the remaining devices, including: If no device is found to be in compliance with the mandatory policy after screening, the implementation method of the mandatory policy is changed, and devices whose estimated state parameters are in compliance with the changed mandatory policy are re-screened to obtain the remaining devices.

3. The device scheduling method according to claim 1, characterized in that: The process of sorting the satisfaction values ​​of the devices and selecting the corresponding devices to perform the tasks according to the sorted order includes: accumulating the actual values ​​of the status parameters of the task records into the no-task records of the devices.

4. A device scheduling device, characterized in that: include: Recording unit, used to define equipment status parameters and continuously track and record the actual values ​​of each status parameter; An establishing unit, for establishing, for a part of the equipment, a mapping relationship between a part feature and a state parameter of the part; A definition unit, configured to define a part feature baseline of the part and an associated satisfaction rate function according to the part feature of the part; an allocating unit, configured to allocate task policies to each task, wherein each task policy includes at least one mandatory policy and at least one compliance policy; The estimation unit is used to estimate the state parameters of the equipment after executing the task based on the current state parameters of the equipment and the task requirements when assigning tasks; A screening unit, configured to screen out devices whose estimated state parameters comply with the mandatory policy, to obtain remaining devices; An acquisition unit, configured to acquire a corresponding part feature baseline and an associated satisfaction rate function according to the compliance strategy; an input unit, configured to input the values ​​of the state parameters of the remaining devices into an associated satisfaction rate function to obtain a satisfaction value of each device; A selection unit is used to sort the satisfaction values ​​of each device and select the corresponding device to perform the task according to the sorted order; Also includes: An association unit is used to associate each mandatory policy with a part feature baseline of a part, and to associate each compliance policy with a part feature baseline of a part; Each mandatory policy includes any of the following implementation methods: All satisfied: After completing the task, all status parameters of the equipment meet the requirements of the corresponding part feature baseline; Partially satisfied: After completing the task, at least one of the equipment's status parameters meets the requirements of the corresponding part feature baseline; Partially unsatisfied: After completing the task, at least one of the status parameters of the equipment does not meet the requirements of the corresponding part feature baseline; All unsatisfactory: After completing the task, all status parameters of the equipment do not meet the requirements of the corresponding part feature baseline; The input unit includes: The actual value acquisition subunit is used to obtain the actual value of the associated state parameter according to the part feature baseline corresponding to the compliance strategy; a judging subunit, configured to judge whether an actual value of a status parameter of the device exceeds a predetermined range; a first input subunit, configured to input a value in the predetermined range closest to the actual value into the satisfaction rate function if the actual value of the state parameter of the device exceeds a predetermined range; a second input subunit, configured to input the actual value into the satisfaction rate function if the actual value of the state parameter of the device does not exceed a predetermined range; Also includes: A weight value assignment unit is used to assign a weight value to each task policy when multiple task policies are associated with the same task, and to ensure that a single device only meets the conditions of one task policy through the mandatory policy of each task policy; Define the status parameters related to the life of core components during equipment operation. Status parameters are used to measure changes in a single quantity. Mapping wear characteristics and state parameters through a weighted sum function; Define good parts feature baseline, attention parts feature baseline and replacement parts feature baseline according to wear characteristics; Among them, the part feature is the wear feature; The satisfaction rate function is a weighted function of the value of each part feature relative to the baseline representing the part feature, the input of the function is the value of each part feature, and the output range is [0, 1].

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the device scheduling method according to any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to perform the device scheduling method according to any one of claims 1 to 3.

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