Equipment scheduling method and device, computer equipment and readable storage medium

Abstract

The invention discloses an equipment scheduling method and device, computer equipment and a readable storage medium. The method comprises the steps that equipment state parameters are defined, and actual values of the state parameters are continuously tracked and recorded; establishing a mapping relation between the part characteristics of the part and the state parameters; defining a part feature baseline of the part and an associated satisfaction rate function according to the part feature of the part; task strategies are distributed to all the tasks; according to the current state parameter of the equipment and the task demand, estimating the state parameter after the equipment executes the task; screening out the equipment of which the estimated state parameters accord with a mandatory strategy to obtain residual equipment; acquiring a satisfaction rate function according to the conformity strategy; inputting the values of the state parameters of the remaining devices into a satisfaction rate function to obtain satisfaction degree values of the devices; and sorting the satisfaction degree values of the devices, and selecting the corresponding device to execute the task according to the sequence after sorting. By means of the method, decentralized management of equipment is achieved, and the overall service life of core components is prolonged.

Description

Technical Field

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

[0002] In the field of industrial machinery control, the configuration of large devices such as robotic arms, stackers, and automated guided vehicles (AGVs) is usually designed based on the maximum working load. However, during actual operation, the daily usage load of the devices is often much lower than their theoretical upper limit. This difference between design and usage makes it common to adopt load balancing or task equalization strategies when the devices perform non-device-specific tasks. For example, AGVs in the same area can interchangeably perform transportation tasks, stackers can allocate tasks through storage location scheduling, and depalletizing robots can also flexibly allocate work according to instructions from the upper computer.

[0003] Although the above strategies can achieve fairness and efficiency in short-term task allocation, there are significant problems after long-term operation: Since the degree of equipment wear tends to be the same, the synchronization of the life consumption of their core components is relatively high. When the equipment reaches the service life threshold, it is often necessary to centrally perform maintenance or replace the core components. However, core components usually have the characteristics of high cost and long stocking cycles. If not fully prepared in advance, it is easy to cause equipment downtime waiting due to spare part shortages under continuous demand, resulting in production interruption and economic losses.

[0004] Currently, the industry mainly alleviates the above problems through two methods: 1. By shortening the inspection interval, detecting abnormal wear signs of core components early, and stocking up in advance. However, this method requires frequent disassembly and inspection, increasing the manual maintenance cost and possibly affecting production efficiency due to temporary downtime; 2. By increasing the inventory level of core components to reduce the downtime risk. But this method requires additional storage space, increasing the pressure of spare part management and capital turnover, and may cause resource waste due to redundant procurement. Summary of the Invention

[0005] The purpose of the present invention is to provide a device scheduling method, device, computer device, and readable storage medium, aiming to solve problems such as high maintenance costs and large capital turnover pressure in existing solutions.

[0006] In a first aspect, an embodiment of the present invention provides a device scheduling method, including: Defining device state parameters and continuously tracking and recording the actual values of each state parameter; Establishing a mapping relationship between the part characteristics of a part of the device and the state parameters; Defining a part characteristic baseline of the part and an associated satisfaction rate function according to the part characteristics of the part; Assign task strategies to each task, where each task strategy includes at least one mandatory strategy and at least one compliance strategy; During task assignment, estimate the state parameters of the device after task execution according to the current state parameters of the device and the task requirements; Filter out the devices whose estimated state parameters meet the mandatory strategy to obtain the remaining devices; Obtain the corresponding part feature baseline and the associated satisfaction rate function according to the compliance strategy; Input the values of the state parameters of the remaining devices into the associated satisfaction rate function to obtain the satisfaction values of each device; Sort the satisfaction values of each device and select the corresponding devices to execute the task according to the sorted order.

[0007] In a second aspect, an embodiment of the present invention provides a device scheduling device, including: A recording unit for defining device state parameters and continuously tracking and recording the actual values of each state parameter; A establishing unit for establishing a mapping relationship between the part features of the part of the device and the state parameters; A defining unit for defining the part feature baseline of the part and the associated satisfaction rate function according to the part features of the part; An assignment unit for assigning task strategies to each task, where each task strategy includes at least one mandatory strategy and at least one compliance strategy; An estimating unit for estimating the state parameters of the device after task execution according to the current state parameters of the device and the task requirements during task assignment; A screening unit for screening out the devices whose estimated state parameters meet the mandatory strategy to obtain the remaining devices; An obtaining unit for obtaining the corresponding part feature baseline and the associated satisfaction rate function according to the compliance strategy; An input unit for inputting the values of the state parameters of the remaining devices into the associated satisfaction rate function to obtain the satisfaction values of each device; A selecting unit for sorting the satisfaction values of each device and selecting the corresponding devices to execute the task according to the sorted order.

[0008] 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 on the memory and executable on the processor. When the processor executes the computer program, the device scheduling method described in the first aspect above is implemented.

[0009] Fourthly, an embodiment of the present invention further provides a computer-readable storage medium, where 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.

[0010] The present invention discloses a device scheduling method, apparatus, computer device, and readable storage medium. The method includes: defining device state parameters and continuously tracking and recording the actual values of each state parameter; establishing a mapping relationship between the part features of a part of the device and the state parameters; defining a part feature baseline of the part and an associated compliance rate function according to the part features of the part; allocating task strategies to each task, where each task strategy includes at least one mandatory strategy and at least one compliance strategy; when allocating tasks, estimating the state parameters of the device after the device executes the task according to the current state parameters of the device and the task requirements; screening out the devices whose estimated state parameters meet the mandatory strategy to obtain the remaining devices; obtaining the corresponding part feature baseline and the associated compliance rate function according to the compliance strategy; inputting the values of the state parameters of the remaining devices into the associated compliance rate function to obtain the satisfaction values of each device; sorting the satisfaction values of each device and selecting the corresponding device to execute the task according to the sorted order. Through the above method, the present invention realizes the decentralized management of devices, prolongs the overall service life of core components, reduces the shutdown risk and spare part cost caused by centralized failures. At the same time, without increasing the inspection density, it is not necessary to frequently disassemble the machine for inspection, reducing the maintenance cost and improving the production efficiency. In addition, without increasing the stock quantity, it does not occupy additional storage space, reducing the pressure of spare part management and capital turnover. An embodiment of the present invention also provides a device scheduling apparatus, a computer-readable storage medium, and a computer device, which have the above beneficial effects and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0012] Figure 1 It is a flowchart of the device scheduling method; Figure 2 It is a schematic block diagram of the device scheduling apparatus. DETAILED DESCRIPTION

[0013] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0014] It should be understood that when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0015] It should also be understood that the terms used in this specification of the present invention are merely for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in this specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0016] It should be further understood that the term " / and" used in this specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations.

[0017] Please refer to Figure 1 , this embodiment provides a device scheduling method, including: S101: Define device status parameters and continuously track and record the actual values of each status parameter; In this embodiment, status parameters related to the life of the core components during its operation are defined, and a continuous tracking and recording mechanism is established. The status parameters are used to measure the change of a single quantity. Taking the automatic guided vehicle (AGV) as an example, first, according to the physical characteristics and usage scenarios of the AGV, the following status parameters are defined: Loaded travel distance: The cumulative travel distance of the AGV when carrying goods, in kilometers; Unloaded travel distance: The cumulative travel distance of the AGV when not carrying goods, in kilometers; Emergency stop times: The number of emergency stops triggered by safety or task interruption during the operation of the AGV, in times; Charging time: The cumulative time of each charge of the AGV, in hours; Goods picking action times: The cumulative number of times the AGV performs goods picking tasks, in times; Goods storing action times: The cumulative number of times the AGV performs goods storing tasks, in times; Parking time (non - charging state): The cumulative time for each parking of the AGV, with the unit of hour.

[0018] The above - mentioned state parameters are collected in real - time through the sensors and control system of the AGV, and continuously tracked and recorded by the host computer. For example, when the AGV executes a transportation task, its navigation system calculates the loaded / unloaded driving distance based on the path planning and actual movement trajectory, and uploads the data to the host computer in real - time. Emergency stop events are detected and triggered for counting by the safety sensors, and the charging time and the number of pick - up / put - away operations are recorded by the power management system and task execution module of the AGV respectively.

[0019] For an AGV that has not executed a task, the actual values of its state parameters will be accumulated into the "no - task record". For example, when the AGV is running in the idle state, its unloaded driving distance and the number of emergency stops will be recorded into the parameter queue corresponding to the "no - task record".

[0020] When the AGV executes a specific task (such as transporting goods), the task - related state parameters (such as the loaded driving distance, the number of pick - up operations) will be separately recorded into the "actual value" field of this task. After the task is completed, the system will compare the "actual value" with the "planned value" (the theoretical value estimated during task allocation), and update the final value into the "no - task record" to ensure data continuity.

[0021] Taking the robotic arm as an example, the core components of the robotic arm include the main motor, joint bearings, end - effectors, etc. Its wear characteristics are closely related to the following state parameters: Movement time under load: The cumulative movement time of each joint of the robotic arm when carrying goods (unit: hour); Movement time without load: The cumulative movement time of each joint of the robotic arm in the unloaded state (unit: hour); Number of repeated positioning: The cumulative number of times the robotic arm completes the same trajectory action (unit: times); Number of emergency stops: The number of joint emergency stop events triggered due to safety or task interruption (unit: times); Number of end - effector pick - up operations: The cumulative number of times the robotic arm performs pick - up operations (unit: times); Number of end - effector release operations: The cumulative number of times the robotic arm performs release operations (unit: times).

[0022] Specifically, the robotic arm collects the actual values of the above parameters in real time through joint encoders, force sensors, and end effector controllers. For example, when the robotic arm performs a handling task, its joint encoder records the movement time under load, the force sensor monitors the load status, and the end effector controller records the number of pick-and-place operations; in the idle state, the unloaded movement time of the robotic arm is continuously accumulated by the encoder until no task is recorded; the emergency stop event is triggered by a safety sensor and counted, and the data is uploaded to the host computer in real time.

[0023] Furthermore, when the robotic arm performs a specific task (such as handling 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 during task allocation) and updates it to the "no task record".

[0024] When the robotic arm is not performing a task, parameters such as its unloaded movement time and the number of emergency stops are directly accumulated into the corresponding queue of the "no task record".

[0025] Taking the stacker crane as an example, the core components of the stacker crane include the main motor, horizontal guide rails, vertical lifting system, fork extension mechanism, etc., and its wear characteristics are related to the following state parameters: Horizontal load travel distance: The horizontal movement distance of the stacker crane when carrying goods (unit: meter); Horizontal unloaded travel distance: The horizontal movement distance of the stacker crane when unloaded (unit: meter); Vertical load travel distance: The vertical lifting distance of the stacker crane when carrying goods (unit: meter); Vertical unloaded travel distance: The vertical lifting distance of the stacker crane when unloaded (unit: meter); Horizontal load acceleration / deceleration time: The cumulative acceleration / deceleration time of the stacker crane when moving horizontally while carrying goods (unit: second); Horizontal unloaded acceleration / deceleration time: The cumulative acceleration / deceleration time of the stacker crane when moving horizontally while unloaded (unit: second); Vertical load acceleration / deceleration time: The cumulative acceleration / deceleration time of the stacker crane when lifting vertically while carrying goods (unit: second); Vertical unloaded acceleration / deceleration time: The cumulative acceleration / deceleration time of the stacker crane when lifting vertically while unloaded (unit: second); Fork extension pick-up times: The cumulative number of pick-up operations performed by the stacker crane (unit: times); Fork extension drop-off times: The cumulative number of drop-off operations performed by the stacker crane (unit: times); Emergency stop times: The number of emergency stops triggered by safety or task interruption during the operation of the stacker crane, in units of times; Parking time (non - charging state): The cumulative time for each parking of the stacker, with the unit of hours.

[0026] Specifically, the stacker collects the actual values of parameters in real - time through position sensors, accelerometers, and goods location identification systems. For example: when the stacker performs a goods - picking task, the position sensor records the traveling distance in the horizontal / vertical directions, the accelerometer monitors the acceleration / deceleration time, and the goods location identification system records the number of times of fork extension for picking goods; in the idle state, the unloaded traveling distance and the number of emergency stops of the stacker are accumulated to the "no - task record" through sensor data.

[0027] Furthermore, when performing a specific task (such as picking / delivering goods), parameters such as the horizontal / vertical traveling distance and acceleration / deceleration time of the stacker 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 estimated value based on task path planning) and updates it to the "no - task record".

[0028] When the stacker does not perform a task, parameters such as its unloaded traveling distance and the number of emergency stops are directly accumulated to the corresponding queue of the "no - task record".

[0029] S102: For the parts of the device, establish the mapping relationship between the part features and state parameters of the parts; Specifically, identify the components of the device (mainly the core components), and then associate each component feature with its related state parameters.

[0030] Taking the robotic arm as an example, the core components of the robotic arm include the main motor, joint bearings, and end - effectors. The part feature of the main motor: wear feature, and the corresponding state parameters include: movement time under load, movement time without load, and the number of emergency stops.

[0031] Then map the wear feature and state parameters of the main motor through a weighted summation function: Wear rate of the mechanical main motor = (Movement time under load - 500) / 100×0.4+(Movement time without load - 300) / 50×0.3+(Number of emergency stops - 20) / 10×0.3 Among them, the weight coefficients (0.4, 0.3, 0.3) reflect the contribution ratio of each parameter to the wear of the main motor. For example, the movement time under load has the greatest impact on the motor life, so it has the highest weight.

[0032] Furthermore, the part feature of the joint bearing: wear feature, and the corresponding state parameters include: number of repeated positioning times, rotation angle range under load.

[0033] Then map the wear feature and state parameters of the joint bearing through a weighted summation function: Wear rate of the spherical plain bearing = (Number of repeated positioning - 10000) / 2000 × 0.6 + (Range of rotation angle - 180) / 30 × 0.4 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 plain bearing.

[0034] Furthermore, the part characteristics of the end effector: wear characteristics, and the corresponding state parameters include: number of picking operations, number of releasing operations, and range of clamping force fluctuation.

[0035] Then, map the wear characteristics and state parameters of the end effector through a weighted summation function: Wear rate of the end effector = (Number of picking operations - 5000) / 1000 × 0.5 + (Number of releasing operations - 5000) / 1000 × 0.3 + (Range of clamping force fluctuation - 10) / 5 × 0.2.

[0036] Taking the stacker as an example, the core components of the stacker include the main motor, horizontal guide rail, vertical lifting system, and telescopic fork mechanism.

[0037] The wear degree of the main motor of the stacker is related to the horizontal traveling distance with load, horizontal acceleration time with load, horizontal deceleration time with load, horizontal traveling distance without load, horizontal acceleration time without load, horizontal deceleration time without load, parking time, and number of emergency stops, etc. And the wear condition of the main motor has a certain weighted summation relationship with these attributes. That is, according to the specific capabilities and parameters of the main motor, the weight of the stacker, the situation of the shelf, the weight of the goods, etc., and based on big data statistics, determine the relevance of the above parameters to the wear condition of the main motor respectively, and use them as weighted coefficients to sum up to obtain the wear condition function of the main motor.

[0038] Furthermore, the part characteristics of the horizontal guide rail: wear characteristics, and the corresponding state parameters include: horizontal traveling distance without load and number of horizontal emergency stops.

[0039] Then, map the wear characteristics and state parameters of the horizontal guide rail through a weighted summation function: Wear rate of the horizontal guide rail = (Distance without load - 5000) / 1000 × 0.6 + (Number of emergency stops - 10) / 5 × 0.4.

[0040] Furthermore, the part characteristics of the telescopic fork mechanism: wear characteristics, and the corresponding state parameters include: number of goods picking operations, number of goods placing operations, and range of telescopic stroke fluctuation.

[0041] Then, map the wear characteristics and state parameters of the telescopic fork mechanism through a weighted summation function: Fork extension mechanism wear rate = (number of pick-up operations - 3000) / 600 × 0.5 + (number of drop-off operations - 3000) / 600 × 0.3 + (fork extension stroke fluctuation range - 10) / 2 × 0.2.

[0042] Taking the AGV as an example, the core components of the stacker include the main battery, drive motor, and navigation system.

[0043] Part characteristics of the drive motor: wear characteristics, and the corresponding state parameters include: loaded travel distance, unloaded travel distance, and number of emergency stops.

[0044] Then, map the wear characteristics and state parameters of the drive motor through a weighted summation function: Drive motor wear rate = (loaded distance - 5000) / 1000 × 0.4 + (unloaded distance - 3000) / 500 × 0.3 + (number of emergency stops - 50) / 10 × 0.3; After mapping the part characteristics and state parameters, the data collected by the host computer will be used to calculate the corresponding values of the device characteristics and appended to the corresponding feature queue for statistics.

[0045] S103: Define the part characteristic baseline and the associated satisfaction rate function of the part according to the part characteristics of the part; In this embodiment, for the parts that define the device characteristics, the corresponding part characteristic baselines should be defined. The baseline needs to include at least one part characteristic, but it is not required to include all the characteristics of this part, because some characteristics may only be used for reference and statistics and do not affect the actual life of the part. Taking the core components of industrial equipment (such as the main motor of the robotic arm, the main motor of the stacker, the main battery of the AGV) as an example, it is detailed how to define the part characteristic baseline according to the part characteristics and associate the corresponding satisfaction rate function to provide dynamic data support for the task allocation strategy.

[0046] Define the good part characteristic baseline, attention part characteristic baseline, and part characteristic baseline to be replaced according to the wear characteristics of the main motor of the robotic arm. The relevant state parameters include the movement time under load, the movement time without load, and the number of emergency stops.

[0047] Good part characteristic baseline: Load movement time 0 - 500 hours (in ascending order; this value is related to the specific calculation function), unloaded movement time 0 - 300 hours (in ascending order), number of emergency stops 0 - 20 times (in ascending order); Attention part characteristic baseline: Load movement time 501 - 700 hours (in ascending order), unloaded movement time 301 - 400 hours (in ascending order), number of emergency stops 21 - 40 times (in ascending order); Parts feature baseline for replacement required: Load movement time 701 - 800 hours (in ascending order), no-load movement time 401 - 500 hours (in ascending order), emergency stop count 41 - 60 times (in ascending order).

[0048] In some embodiments, the main battery of the AGV is related to parts features such as charge-discharge cycles (mapping of state parameters related to charging, directly affecting the lifespan), high-power consumption operations (mapping of state parameters related to workload, reflecting the proportion of high-discharge time), and total service time (directly related to the remaining lifespan value).

[0049] Define multiple parts feature baselines such as good, attention required, and replacement required for the AGV main battery, associate the above-mentioned parts features with each parts feature baseline, and respectively specify the above values and the satisfaction rate calculation function.

[0050] The value represents the upper and lower limits and sorting method of the values of each parts feature related to this parts feature baseline. For example, the good parts feature baseline indicates charge-discharge cycles of 0 - 200 times (in ascending order), high-power consumption operation value of 0 - 20000 (in ascending order; this value is related to the specific calculation function for reference only), and total service time of 0 - 300 days (in ascending order); the attention required parts feature baseline corresponds to 201 - 300 times (in ascending order), 20001 - 30000 (in ascending order), and 301 - 400 days (in ascending order); the replacement required parts feature baseline corresponds to 301 - 350 times (in ascending order), 30001 - 35000 (in ascending order), and 401 - 500 days (in ascending order).

[0051] 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 retaining two significant figures. For example, for the attention required parts feature baseline of the AGV main battery, the satisfaction rate function can be set as: (charge-discharge cycles - 200) / 100 * 0.5 + (high-power consumption operation value - 20000) / 10000 * 0.3 + (total service time - 300) / 100 * 0.2. This function will be 0 when entering the attention required state from good and approach 1 when reaching the maximum value of attention required.

[0052] S104: Allocate task strategies for each task, where each task strategy includes at least one mandatory strategy and at least one compliance strategy; In this embodiment, taking the task allocation of industrial equipment (such as robotic arms, stackers, AGVs) as an example, it details how to allocate task strategies for different task types. Each strategy includes at least one mandatory strategy and one compliance strategy, and defines the weight values between strategies and the device screening rules.

[0053] Allocate corresponding strategy combinations according to the task type (such as "work task", "pickup task", "new motor running-in task"). The following is a typical task strategy configuration example: Task type 1: Work tasks (such as a robotic arm performing handling) adopt a mandatory strategy.

[0054] The mandatory strategy is implemented by full satisfaction: after the device executes the task, the "load movement time" and "emergency stop times" of its main motor need to simultaneously meet the "good" baseline range (load movement time 0 - 500 hours, emergency stop times 0 - 20 times).

[0055] Adopting a mandatory strategy for work tasks can screen out devices that have not reached the wear threshold, avoiding premature failure of core components due to task execution.

[0056] Furthermore, task type 2: Pickup tasks (such as a stacker performing storage and retrieval in storage locations) adopt a mandatory strategy: The mandatory strategy is implemented by partial satisfaction: after the device executes the task, either the "horizontal load travel distance" or the "vertical load travel distance" of its main motor needs to meet the "attention" baseline range (horizontal load travel distance 10001 - 12000 meters, vertical load travel distance 5001 - 6000 meters).

[0057] Adopting a mandatory strategy for pickup tasks can screen out devices approaching the end of their lifespan, and prioritize task allocation to accelerate their state parameters to reach the "needs replacement" baseline for centralized maintenance.

[0058] Furthermore, task type 3: New motor running-in tasks (such as AGV main battery verification) adopt a mandatory strategy: The mandatory strategy is implemented by partial non - satisfaction: after the device executes the task, either the "charge - discharge cycle times" or the "high - power consumption working time" of its main battery needs to not meet the "good" baseline range (charge - discharge cycles 0 - 200 times, high - power consumption working time 0 - 20000 hours).

[0059] Adopting a mandatory strategy for new motor running - in tasks can screen out devices that have entered the "attention" or "needs replacement" baseline, and prioritize task allocation to verify their performance stability.

[0060] In this embodiment, the mandatory strategy refers to a strategy that must be satisfied. There can be any number of mandatory strategies. The mandatory strategy is used to filter out inappropriate strategies, which is equivalent to the screening condition of the strategy. For example, if the user hopes that devices approaching the design lifespan undertake more work to reach the design lifespan, and does not want devices that have already reached the design lifespan to be affected, in the corresponding strategy, it is necessary to exclude devices that do not meet the requirements (devices that have already reached the design lifespan) through a combination of mandatory strategies. The compliance strategy refers to a strategy for judging the degree of compliance of the various state parameters of this device after completing this task with the part feature baseline. There can be any number of compliance strategies.

[0061] In this embodiment, it further includes: associating each mandatory policy with a part feature baseline of a part, and associating each compliance policy with a part feature baseline of a part.

[0062] Each mandatory policy is only associated with a feature baseline of one part, which can ensure clear admission / exit conditions during task assignment. For example, all mandatory policies being satisfied can be used to restrict high-wear equipment from taking on new tasks to avoid exceeding the component life limit. Partially not satisfying the mandatory policies can be used to screen equipment that needs to be maintained first 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 through weighting. For example, in regular task assignment, the weighting value of the motor wear baseline compliance policy can be set to 0.6, and the weighting value of the battery health baseline compliance policy can be set to 0.4 to comprehensively evaluate the equipment applicability. Reverse sorting can enable low-satisfaction equipment (such as new equipment with less wear) to obtain tasks first, while forward sorting is the opposite, so as to be able to flexibly adapt to requirements such as balanced wear or preferentially using old equipment.

[0063] In this embodiment, each mandatory policy includes any of the following implementation methods: All satisfied: All state parameters of the equipment after completing the task meet the requirements of the corresponding part feature baseline; Partially satisfied: At least one of the state parameters of the equipment after completing the task meets the requirements of the corresponding part feature baseline; Partially not satisfied: At least one of the state parameters of the equipment after completing the task does not meet the requirements of the corresponding part feature baseline; All not satisfied: All state parameters of the equipment after completing the task do not meet the requirements of the corresponding part feature baseline.

[0064] All satisfied passing requires that all associated state parameters of the equipment after completing the task meet the baseline requirements, which can ensure that the core components are always in the safe working range and avoid potential wear or failures caused by a single parameter exceeding the limit. For example, when assigning a high-load task to a stacker, by associating the main motor wear baseline with all satisfied mandatory policies, it is required that parameters such as its load travel distance, acceleration / deceleration time, etc. do not exceed the baseline threshold to prevent the motor from aging prematurely due to overload.

[0065] Partially satisfied passing allows at least one parameter of the equipment to meet the target baseline after task execution, which can specifically guide the equipment to evolve towards a specific state. For example, for an AGV with a replaced motor, setting a partially satisfied mandatory policy associated with the new motor to be verified baseline enables the equipment to take on tasks first, quickly accumulate working hours and high-load durations, and accelerate the verification of whether the motor performance meets the standard.

[0066] If some parameters do not meet the baseline after the task execution of the detection device, potential failure risks can be identified in a timely manner, triggering the maintenance process. For example, if the charging time parameter of the AGV exceeds the battery baseline threshold after completing the task, the system marks the device through the partial non-compliance strategy and forces it to enter the maintenance queue, thus avoiding job interruption caused by battery performance degradation.

[0067] If all parameters do not meet the baseline after the task execution of the device is prohibited, highly worn, aged, or faulty devices can be forced out of the task pool to prevent production accidents. For example, the general task allocation strategy uses the all non-compliance enforcement strategy to prevent new motor devices that have not completed verification from entering the regular task pool, avoiding task failures caused by unknown component states.

[0068] In some embodiments, a task strategy can be mapped to multiple devices, thereby avoiding the situation of formulating the same task strategy for each of the same devices separately. Without special requirements, most devices will use the same task strategy. Secondly, due to some special requirements (such as accelerating the aging of specific devices), different strategies will be given to these special devices. Therefore, the same task needs to support multiple strategies, and at the same time, duplication of these strategies on devices is not allowed, that is, a device can have at most one task strategy associated with it under the same task.

[0069] In some embodiments, it further includes: When multiple task strategies are associated with the same task, weight values are assigned to each task strategy, and the enforcement strategy of each task strategy is used to ensure that a single device only meets the conditions of one task strategy.

[0070] By assigning weight values to different task strategies (such as weight 2 for "allocating more tasks to new motor devices" and weight 1 for "general task allocation"), the system can dynamically adjust the scheduling logic according to production requirements. The high-weight strategy is triggered first during task allocation to ensure the priority execution of urgent tasks (such as equipment running-in and verification after fault repair) or key operations (such as production of high-value-added orders). At the same time, the enforcement strategy requires that a device can only meet the conditions of one task strategy (for example, the "new motor to be verified" strategy requires "partial compliance" with the baseline, while "general task allocation" requires "all non-compliance"), ensuring that there will be no ambiguous scenario where "one device meets multiple strategies at the same time" during task allocation in the system, and avoiding scheduling errors or resource waste caused by strategy conflicts.

[0071] In some embodiments, the weight values of each task strategy are automatically adjusted according to the actual values of each state parameter through a preset algorithm, specifically including: When the average wear degree of a certain type of core component in the equipment cluster exceeds the preset threshold, automatically reduce the policy weight of high-load tasks associated with this component, and increase the policy weight corresponding to low-wear devices; When the task urgency level is higher than the threshold, temporarily increase the weight of the quick response policy, and preferentially schedule the device closest to the task point or in the most stable state; When a low valley electricity price period is detected, automatically increase the weight of the energy-saving policy. This policy associates state parameters related to equipment energy consumption (such as no-load driving energy consumption, standby power) through a compliance policy, and preferentially schedules low-energy-consuming devices in reverse order.

[0072] By monitoring the average wear degree of the core components of the equipment cluster (such as the average load driving distance of the AGV motor, the average number of charge and discharge cycles of the main battery of the stacker), when it exceeds the preset threshold, automatically reduce the policy weight of high-load tasks, and force the tasks to be allocated to low-wear devices. For example, if the average wear degree of the AGV motor in a certain workshop reaches 70% of the baseline value, the system automatically reduces the weight of the "high-load task policy" and increases the weight of the "low-load device priority policy", avoiding all devices from concentrating in the high-wear interval.

[0073] Convert the task urgency level (such as the remaining days of the order delivery period, the production line downtime loss cost) into a weight gain factor, and use a preset algorithm (such as an exponential function) to increase the weight of the "quick response policy" in real time. For example, if the remaining delivery period of an urgent order is less than 24 hours, the corresponding policy weight increases from the default 50 to 100, forcing the host computer to preferentially schedule the device closest to the task point (regardless of its wear degree), ensuring that the task is completed on time and avoiding the risk of default.

[0074] Furthermore, the adjustment algorithm for dynamic weight values includes: Based on the standard deviation of the wear degree of the core components of the equipment cluster, when the standard deviation is less than the threshold, evenly distribute the task policy weights; when the standard deviation exceeds the threshold, increase the weights of the policies corresponding to low-wear devices in reverse order of wear degree, and the weight adjustment amplitude is proportional to the standard deviation.

[0075] Quantify the dispersion degree of the wear of the core components of the equipment cluster using the standard deviation (such as the standard deviation of the load driving distance of the AGV motor). When the standard deviation is less than the threshold, it is determined that the wear distribution is uniform. At this time, the task policy 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 stacker crane group is less than 100 meters, the system maintains the same task weights for each device to ensure stable production. At the same time, a preset standard deviation threshold (such as 20% of the average wear degree) is used as the critical point for policy switching: when the standard deviation ≤ threshold, the weights are evenly distributed, and the traditional policy of "equal task distribution" is maintained, avoiding unnecessary adjustments to the normal wear state; when the standard deviation > threshold, the wear balance policy is started, and the task allocation is forcibly intervened by adjusting the weights.

[0076] In some specific embodiments, several AGVs have their motors replaced. At this time, among the state parameters of these devices, the high-load working time of the motor is 0 hours, the low-load working time is 0 hours, and the total working time is 0 hours. The characteristic working time of the motor parts includes, but is not limited to, the above three state parameters. The engineer needs to observe this motor for at least 50 hours and have at least 10 hours of high-load work. To accelerate this process, settings need to be made at this time, hoping that the host computer will assign more tasks to these devices in the short term.

[0077] First, define the part characteristic baseline "new motor to be verified": regarding the part characteristics of the motor. The working time is in the range of [0, 50] hours (in ascending order), and the high-load working time is in the range of [0, 10] hours (in ascending order). The satisfaction rate function is: working time / 100 + high-load working time / 20.

[0078] Next, define the task policy "allocate more tasks to new motor devices": its forced policy is set to new motor to be verified, the passing method is partial satisfaction; the compliance policy is set to new motor to be verified, and the weight is 1 (since there is only one compliance policy, the weight is meaningless); the sorting method is reverse order (the higher the compliance, the lower the priority); and this device is mapped to the AGV with the motor replaced.

[0079] At the same time, for the original task policy "general task allocation" (i.e., the default allocation policy): its forced policy adds new motor to be verified, and the passing method is all non-satisfaction. This task policy is default mapped to all AGVs without adjustment.

[0080] Then ensure that both task strategies of "Allocate More Tasks to New Motor Equipment" and "General Task Allocation" are associated with work tasks. The former has a weight of 2 and the latter has a weight of 1. At this time, although there is an overlap in the associated equipment for the two task strategies, due to the setting of the mandatory strategy, the passing method of the mandatory strategy for the task strategy of "Allocate More Tasks to New Motor Equipment" is partially satisfied, and the passing method of the mandatory strategy for the task strategy of "General Task Allocation" is not satisfied at all. Therefore, it is impossible for a certain device to simultaneously meet the requirements of the mandatory strategies of these two task strategies. So this setting can be effectively executed (that is, the situation where the selected device cannot confirm which task strategy it conforms to will not occur).

[0081] S105: During task allocation, estimate the status parameters of the device after task execution according to the current status parameters of the device and the task requirements; Specifically, after the system starts, continuous data statistics will be carried out on each status parameter of each device. This process is divided into two scenarios: If the device is executing a certain task and there is a record of this task in the system, the system will accumulate and record the changed values of the status parameters (such as travel distance, working time, etc.) generated during the task execution in the "actual value" field of this task; If the device is not executing a task or there is no corresponding task record in the system, the changed values of the status parameters will be accumulated in the "no task record" of the device. For example, the "inventory operation times" of Stacker No. 1 will have an independent no task record, and a record will be established separately for each task in the system, including "actual value" (the real data after task execution) and "planned value" (the estimated data during task allocation).

[0082] When a task needs to be allocated, it is processed according to the following logic: According to the relevant attributes of the task (such as the current position of the stacker, the target cargo location, the task type, etc.), calculate the possible changes in each status parameter caused by executing this task. For example, by analyzing the movement trajectory, load condition and operation actions of the stacker, estimate the changed values of parameters such as "travel distance with load horizontally" and "working time with high load". These estimated values will be written into the "planned value" field of the task as the basis for subsequent policy screening.

[0083] Then find the corresponding task strategy according to the task type and traverse all the devices mapped by this task strategy. For each device, the system will calculate the total value of its current status parameters, and the specific rules are as follows: The current status parameter value of each device is accumulated by two parts of data: 1) The value of the no task record: that is, the changed value of the status parameter accumulated by the device during the period when it is not executing a task; 2) Maximum value of task records: For all tasks executed by the device, the system takes the larger value between the "actual value" and the "planned value" in each task record and accumulates them. This design ensures that even if the task is not fully completed, policy judgments can be made based on the estimated values.

[0084] After obtaining the current state parameter values of each device, then estimate the state parameters of each device after task execution according to the current state parameter values of each device and the allocated task requirements.

[0085] S106: Screen out the devices whose estimated state parameters meet the mandatory policy to obtain the remaining devices; Specifically, substitute the estimated state parameters of each device after task execution into each mandatory policy for inspection. If the inspection fails, discard this device, thereby obtaining the remaining devices.

[0086] In some embodiments, screening out the devices whose estimated state parameters meet the mandatory policy to obtain the remaining devices includes: If there are no devices that meet the mandatory policy after screening, then change the implementation method of the mandatory policy and re-screen out the devices whose estimated state parameters meet the changed mandatory policy to obtain the remaining devices.

[0087] When the original mandatory policy results in no available devices, by automatically switching the policy implementation method, it is possible to avoid the backlog of the task queue or production interruption caused by the temporarily non-compliant device status. Through the flexible adjustment of the policy implementation method, the system can dynamically balance between the device maintenance requirements and the urgency of task delivery, avoiding low resource utilization caused by overly conservative policy settings.

[0088] In some specific embodiments, when a pick-up task request arrives, the system estimates that executing this task will increase the working time of each stacker by 0.5 hours, the high-load working time by 0.2 hours, and the low-load working time by 0.3 hours.

[0089] Then calculate the estimated state parameters of each stacker after task execution (for example, the current working time of stacker A = 50 hours, which becomes 50.5 hours after task execution; the high-load working time = 10 hours, which becomes 10.2 hours after task execution).

[0090] Subsequently, according to the partial satisfaction rule, screen out the devices with at least one parameter not exceeding the baseline upper limit. Assume that the estimated state parameters of 3 new motor stackers all exceed all baseline upper limits (such as working time = 50.5 hours, high-load working time = 10.2 hours), then the screening fails and no device meets the policy.

[0091] Then change the forced policy from partially satisfied to partially dissatisfied by screening out devices with at least one parameter exceeding the upper baseline limit.

[0092] S107: Obtain the corresponding part feature baseline and associated satisfaction rate function according to the compliance policy; Since each compliance policy is associated with a part feature baseline of a part, the corresponding part feature baseline and associated satisfaction rate function can be obtained according to the compliance policy.

[0093] S108: Input the values of the status parameters of the remaining devices into the associated satisfaction rate function to obtain the satisfaction values of each device; Specifically, inputting the values of the status parameters of the remaining devices into the associated satisfaction rate function to obtain the satisfaction values of each device includes: Obtain the actual values of the associated status parameters according to the part feature baseline corresponding to the compliance policy; Judge whether the actual values of the status parameters of the device exceed the predetermined range; If the actual value of the status parameter of the device exceeds the predetermined range, input the value closest to the actual value within the predetermined range into the satisfaction rate function; If the actual value of the status parameter of the device does not exceed the predetermined range, input the actual value into the satisfaction rate function.

[0094] If after executing this task, the expected high power consumption working value is 20005, which exceeds the upper limit (20000) of the "good" high power consumption working value, the upper limit value (20000) will be used for calculating the satisfaction rate; if after executing this task, the charge-discharge cycle is 100, within the "good" charge-discharge cycle range, then its value (100) will be used for calculating the satisfaction rate. This design is used to prevent the compliance calculated by the satisfaction rate function from exceeding its value range [0, 1], ensuring the continuity of the calculation process.

[0095] S109: Sort the satisfaction values of each device and select the corresponding device to execute the task according to the sorted order.

[0096] Specifically, the system returns the policy satisfaction values of each non-abandoned device to the host computer for reference in selecting devices. If the host computer has no other sorting strategies to consider, it can directly execute the sorting method specified by the task policy (such as from large to small or from small to large) to sort these policy satisfaction values, so as to select the optimal device. If the host computer needs to consider other conditions simultaneously (such as proximity allocation), then these policy satisfaction values and the sorting method specified by the task policy are sent to the host computer for comprehensive sorting by the host computer.

[0097] In some specific embodiments, when a new "work task" task 001 request arrives: First, an estimation is performed. The estimation believes that task 001 will result in an increase in working hours by 0.1 hour, high-load working hours by 0.05 hour, low-load working hours by 0.05 hour, pick-up actions by 1, put-down actions by 1, etc. A record of task 001 is established, and the above values are recorded in the "planned values" of this task.

[0098] Next, check the task policies mapped by the "work task", and find "allocate more tasks to the new motor equipment" (weight 2) and "general task allocation" (weight 1). Perform corresponding mandatory policy checks on all devices of the two task policies. It is found that among "allocate more tasks to the new motor equipment", the AGV with the recently replaced motor meets the requirements; among "general task allocation", other AGVs meet the requirements. For the devices that meet the requirements, calculate their satisfaction values in the corresponding task policies respectively. It is found that among "allocate more tasks to the new motor equipment", the newer the motor of the AGV, the smaller its satisfaction value. And since the sorting method of this task policy is in reverse order, the one with a smaller satisfaction value will be ranked in the front. The situation in "general task allocation" is omitted.

[0099] If only this policy is used for allocation, due to the higher weight of "allocate more tasks to the new motor equipment", it will be directly allocated to the device ranked in the front in this task policy. In this example, it is the AGV with the newest motor. In actual use, usually the upper computer also needs to consider more factors (such as device status, relative distance, etc.), and take into account the weights of the task policies and the device sorting in each task policy. Eventually, the newer the motor of the AGV, the greater the possibility of getting the task.

[0100] Next, sort the satisfaction values of each device, and select the corresponding device to execute the task according to the sorted order. After that, it includes: accumulating the actual values of each status parameter of the task record into the task-free record of the device, and deleting this task record.

[0101] Accumulate the actual values of the task record (such as the load travel distance of the AGV, the number of inventory actions of the stacker, etc.) into the task-free record of the device in real time to ensure that the device status parameters always reflect the total wear amount of the entire life cycle and avoid baseline matching errors caused by unupdated task data.

[0102] In some embodiments, the vibration frequency, temperature change curve, and current fluctuation data during the operation of industrial equipment are collected in real time by sensors, where: The vibration frequency is measured by a piezoelectric or MEMS sensor, with a range of 0 - 50 Hz and a sampling frequency of 1 Hz to 1 kHz; The temperature change curve is recorded by a digital temperature sensor or a fiber Bragg grating sensor, with an accuracy of ±0.5°C and a sampling interval of 1 minute; The current fluctuation is detected by a Hall effect sensor or a current transformer, with a range of 0 - 100A and a resolution of 0.1A; Next, at least one part feature baseline is defined for the core components of the device. Each baseline contains the upper and lower limits of the corresponding non - traditional parameters, and a satisfaction rate function associated with the baseline is defined. The satisfaction rate function is used to quantify the matching degree between the non - traditional parameters and the baseline; Then, multiple task strategies are defined, and each task strategy is associated with at least one mandatory strategy and / or compliance strategy; Furthermore, according to the matching degree between the non - traditional parameters and the part feature baseline, the weight value of the task strategy is dynamically adjusted, where: When the vibration frequency exceeds the preset threshold (e.g., >40Hz), the slope of the temperature change curve exceeds the safe range (e.g., ΔT / Δt > 5°C / min), or the amplitude of the current fluctuation exceeds the upper limit of the baseline (e.g., >90% of the rated current), the weight value of the corresponding task strategy of the device is reduced to reduce the task allocation frequency; When the non - traditional parameters are all within the baseline range, the weight value of the corresponding task strategy of the device is increased to increase the task allocation frequency.

[0103] Define the baseline range and satisfaction rate function for each non - traditional parameter, for example: Vibration frequency baseline: normal range 0 - 40Hz, and the satisfaction rate function is: Vibration satisfaction rate = (actual frequency - lower limit of the baseline) / (upper limit of the baseline - lower limit of the baseline) × weight coefficient; Temperature change curve baseline: safe slope ≤ 5°C / min, and the satisfaction rate function is: Temperature satisfaction rate = (actual slope - lower limit of the baseline) / (upper limit of the baseline - lower limit of the baseline) × weight coefficient; Current fluctuation baseline: stable range is ±10% of the rated current, and the satisfaction rate function is: Current satisfaction rate = (actual fluctuation amplitude - lower limit of the baseline) / (upper limit of the baseline - lower limit of the baseline) × weight coefficient; Among them, the weight coefficient is allocated according to the influence degree of each parameter on the device health.

[0104] Define a clear baseline range for each non - traditional parameter (such as vibration 0 - 40 Hz, temperature slope ≤ 5℃ / min). Convert the parameter value into a quantization index in the range of [0, 1] through a linear normalization formula (such as vibration satisfaction rate = actual frequency / 40 × weight coefficient), which is convenient for the system to automatically compare and sort. For example, when the vibration frequency of a certain device is 30 Hz, the satisfaction rate is 0.75, indicating that its vibration state is in a good range (the weight coefficient can be set to 0.8), while when the frequency is 45 Hz, the satisfaction rate is 1.125 (exceeding the upper limit), triggering an early warning. At the same time, through the trend analysis of non - traditional parameters (such as the increasing of vibration spectrum energy over time), potential faults can be identified in advance, reducing the proportion of passive maintenance from 70% to less than 30%. For example, the vibration frequency of the AGV drive motor in a certain factory gradually rises from 20 Hz to 35 Hz (close to the 40 Hz threshold), and the system triggers a pre - maintenance strategy 2 months in advance, arranging for bearing replacement to avoid production line downtime caused by sudden failures (the loss of a single downtime is about 50,000 yuan).

[0105] The device scheduling method of this embodiment can be used in the following scenarios: When some devices are approaching the inspection time, by adjusting the task strategies mapped to these devices, these devices can be made to undertake more tasks to enhance the inspection effect.

[0106] After routine maintenance of some devices is completed, by adjusting the task strategies mapped to these devices in a short time, these devices can be made to undertake more tasks to check the state after maintenance and accelerate the running - in process.

[0107] In the daily state, the task strategies of some devices can be adjusted to make them undertake more tasks to prevent the centralized damage of the core parts of all devices.

[0108] Through the above - mentioned embodiments, this embodiment realizes the decentralized management of devices, extends the overall service life of core components, reduces the risk of downtime and spare - part costs caused by centralized failures. At the same time, without increasing the inspection density, there is no need for frequent disassembly and detection, reducing the maintenance cost and improving the production efficiency. In addition, without increasing the inventory quantity, it does not occupy additional storage space, reducing the pressure of spare - part management and capital turnover.

[0109] Please refer to Figure 2 , this embodiment provides a device scheduling device 200, including: A recording unit 201, used to define device state parameters and continuously track and record the actual values of each state parameter; A establishing unit 202, used to establish the mapping relationship between the part features of the parts of the device and the state parameters; A defining unit 203, used to define the part feature baseline of the part and the associated satisfaction rate function according to the part features of the part; An allocation unit 204 for allocating task policies to each task, where each task policy includes at least one mandatory policy and at least one compliance policy; An estimation unit 205 for estimating the state parameters of the device after task execution according to the current state parameters of the device and the task requirements during task allocation; A screening unit 206 for screening out the devices whose state parameters after estimation meet the mandatory policy to obtain the remaining devices; An acquisition unit 207 for acquiring the corresponding part feature baseline and the associated satisfaction rate function according to the compliance policy; An input unit 208 for inputting the values of the state parameters of the remaining devices into the associated satisfaction rate function to obtain the satisfaction values of each device; A selection unit 209 for sorting the satisfaction values of each device and selecting the corresponding device to execute the task according to the sorted order.

[0110] Furthermore, it further includes: An association unit for associating one part feature baseline of a part with each mandatory policy and one part feature baseline of a part with each compliance policy.

[0111] Furthermore, each mandatory policy includes any one of the following implementation methods: Fully satisfied: All state parameters of the device after completing the task meet the requirements of the corresponding part feature baseline; Partially satisfied: At least one of the state parameters of the device after completing the task meets the requirements of the corresponding part feature baseline; Partially dissatisfied: At least one of the state parameters of the device after completing the task does not meet the requirements of the corresponding part feature baseline; Fully dissatisfied: All state parameters of the device after completing the task do not meet the requirements of the corresponding part feature baseline.

[0112] Furthermore, the input unit 208 includes: An actual value acquisition subunit for acquiring the actual values of the associated state parameters according to the part feature baseline corresponding to the compliance policy; A judgment subunit for judging whether the actual value of the state parameter of the device exceeds the predetermined range; A first input subunit for inputting the value closest to the actual value within the predetermined range into the satisfaction rate function if the actual value of the state parameter of the device exceeds the predetermined range; A second input subunit for inputting the actual value into the satisfaction rate function if the actual value of the state parameter of the device does not exceed the predetermined range.

[0113] Further, the selection unit 209 includes: An accumulation subunit, configured to accumulate the actual values of the respective status parameters of the task record to the taskless record of the device.

[0114] Further, it further includes: A weight value assignment unit, configured to, when multiple task policies are associated with the same task, assign weight values to each task policy, and ensure that only one task policy is met for a single device through the mandatory policy of each task policy.

[0115] Further, the screening unit 206 includes: A change subunit, configured to, if there is no device that meets the mandatory policy after screening, change the implementation manner of the mandatory policy, and re-screen the devices whose estimated status parameters meet the changed mandatory policy to obtain the remaining devices.

[0116] Those skilled in the art can clearly understand that for the sake of convenience and brevity of description, the specific working processes of the above-mentioned device and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0117] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the method provided by the above-mentioned embodiments can be implemented. The storage medium may include: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc that can store program codes.

[0118] The present invention also provides a computer device, which may include a memory and a processor. When the processor calls the computer program stored in the memory, the method provided by the above-mentioned embodiments can be implemented. Of course, the computer device may further include various network interfaces, power supplies and other components.

[0119] The various embodiments in the specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among 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 description in the method part. It should be noted that for those of ordinary skill in the art in the technical field of the present invention, 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 protection scope of the claims of the present invention.

[0120] It should also be noted that in this specification, relational terms such as first and second are only used 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 "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion.

[0121] Including, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent in such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

Claims

1. A device scheduling method, characterized in that, Including: Defining device status parameters and continuously tracking and recording the actual values of each status parameter; Establishing a mapping relationship between the part features of the parts of the device and the status parameters; Defining the part feature baseline of the part and the associated compliance rate function according to the part features of the part; Allocating task strategies for each task, where each task strategy includes at least one mandatory strategy and at least one compliance strategy; When allocating tasks, estimating the status parameters of the device after the device executes the task according to the current status parameters of the device and the task requirements; Filtering out the devices whose estimated status parameters meet the mandatory strategy to obtain the remaining devices; Obtaining the corresponding part feature baseline and the associated compliance rate function according to the compliance strategy; Inputting the values of the status parameters of the remaining devices into the associated compliance rate function to obtain the satisfaction values of each device; Sorting the satisfaction values of each device and selecting the corresponding devices to execute the task according to the sorted order.

2. The device scheduling method according to claim 1, wherein Also including: 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.

3. The device scheduling method according to claim 1, wherein Each mandatory strategy includes any one of the following implementation methods: Fully satisfied: All status parameters of the device after completing the task meet the requirements of the corresponding part feature baseline; Partially satisfied: At least one of the status parameters of the device after completing the task meets the requirements of the corresponding part feature baseline; Partially not satisfied: At least one of the status parameters of the device after completing the task does not meet the requirements of the corresponding part feature baseline; Fully not satisfied: All status parameters of the device after completing the task do not meet the requirements of the corresponding part feature baseline.

4. The device scheduling method according to claim 1, wherein, The filtering out the devices whose estimated status parameters meet the mandatory strategy to obtain the remaining devices includes: If there are no devices that meet the mandatory strategy after filtering out, changing the implementation method of the mandatory strategy and re-filtering out the devices whose estimated status parameters meet the changed mandatory strategy to obtain the remaining devices.

5. The device scheduling method according to claim 1, wherein The inputting the values of the status parameters of the remaining devices into the associated compliance rate function to obtain the satisfaction values of each device includes: Obtaining the actual values of the associated status parameters according to the part feature baseline corresponding to the compliance strategy; Judging whether the actual value of the status parameter of the device exceeds the predetermined range; If the actual value of the status parameter of the device exceeds the predetermined range, inputting the value closest to the actual value within the predetermined range into the compliance rate function; If the actual value of the status parameter of the device does not exceed the predetermined range, inputting the actual value into the compliance rate function.

6. The device scheduling method according to claim 1, wherein After sorting the satisfaction values of each device and selecting the corresponding devices to execute the task according to the sorted order: Accumulating the actual values of each status parameter recorded in the task into the record without tasks of the device.

7. The device scheduling method according to claim 1, wherein Also including: When a task is associated with multiple task strategies, assigning weight values to each task strategy and ensuring that a single device only meets the conditions of one task strategy through the mandatory strategy of each task strategy.

8. A device scheduling apparatus, characterized in that, Including: A recording unit for defining device status parameters and continuously tracking and recording the actual values of each status parameter; A building unit, configured to establish a mapping relationship between the part features and the state parameters of a part of a device; A defining unit, configured to define a part feature baseline of the part and an associated satisfaction rate function according to the part features of the part; An allocation unit, configured to allocate task strategies for each task, where each task strategy includes at least one mandatory strategy and at least one compliance strategy; An estimating unit, configured to estimate the state parameters of the device after the task is executed according to the current state parameters of the device and the task requirements when the task is allocated; A screening unit, configured to screen out the devices whose estimated state parameters comply with the mandatory strategy to obtain the remaining devices; An obtaining unit, configured to obtain the corresponding part feature baseline and the 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 the associated satisfaction rate function to obtain the satisfaction values of each device; A selection unit, configured to sort the satisfaction values of each device and select the corresponding devices to execute the task according to the sorted order.

9. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the device scheduling method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the processor is caused to execute the device scheduling method according to any one of claims 1 to 7.

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