Artificial intelligence-based digital twin system

Abstract

The present invention discloses a digital twin system based on artificial intelligence, which relates to the technical field of logistics warehouse management. This system constructs a digital twin model of a designated logistics warehouse, and then analyzes the rationality of the scheduling of handling robots in the designated logistics warehouse, thereby solving the problem that the current technology analyzes the rationality of the scheduling of handling robots in a superficial and one-sided manner, realizes the intelligent management of the scheduling of handling robots, effectively ensures the rationality of the scheduling of handling robots in the logistics warehouse, ensures the uniformity of the package handling distribution of the handling robots, reduces the operating loss of the handling robots, ensures the stability of the efficiency of the handling robots and the overall operating efficiency of the logistics warehouse, thereby ensuring the smooth operation of the logistics warehouse, and also reduces the speed of damage of the handling robots and the operating cost of the logistics warehouse.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics warehouse management, and specifically to a digital twin system based on artificial intelligence. Background Art

[0002] With the continuous development of digital twin technology, digital twins have been widely used in logistics warehouses and factories. Visualizing logistics warehouses through digital twin technology can enable managers to have a clearer understanding of the operating status of logistics warehouses. The operating efficiency of logistics warehouses depends on the rational deployment of handling robots. Therefore, it is necessary to analyze and manage the rationality of the deployment of handling robots in logistics warehouses.

[0003] Current technology mainly tracks the position of handling robots in logistics warehouses and then intelligently dispatches them based on their positions. However, the rationality analysis of handling robot scheduling is still relatively superficial and one-sided. Obviously, this analysis method has at least the following problems:

[0004] 1. The rationality of handling robot scheduling affects the operation of handling robots. Current technology does not analyze the rationality of handling robot scheduling in logistics warehouses, and thus cannot guarantee the uniformity of package handling distribution by handling robots. As a result, the operating losses of handling robots vary, resulting in excessive differences in the efficiency of subsequent handling robots. This makes it impossible to guarantee the overall operating efficiency of logistics warehouses and increases the speed at which handling robots are damaged to a certain extent.

[0005] 2. The usage of the handling robot determines the upper limit of its handling capacity. The current technology only schedules the handling robot according to its location, and does not analyze the handling robot's operating capacity based on its usage time and maintenance times. As a result, it is impossible to effectively guarantee the rationality of the weight and distance of packages handled by the handling robot during subsequent scheduling. This makes it impossible to reduce the loss of the handling robot when handling packages, nor to increase its service life. In addition, it also increases the frequency of replacement of the handling robot to a certain extent, thereby increasing the operating cost of the logistics warehouse.

[0006] 3. The rationality of the package distribution of the handling robot is a reflection of the rationality of the handling robot's scheduling. The current technology does not analyze the rationality of the handling robot's package distribution based on the handling robot's operating capacity, the weight and distance of the package, and thus cannot guarantee the rationality of the handling robot's scheduling, nor can it improve the handling robot's life and operating quality, and it cannot effectively reduce the number of handling robot failures, thereby reducing the operating effect and efficiency of the logistics warehouse. Summary of the Invention

[0007] In view of this, the purpose of this application is to provide a digital twin system based on artificial intelligence to solve the problems mentioned in the background technology.

[0008] To solve the above technical problems, the present invention adopts the following technical solution: an artificial intelligence-based digital twin system, including: a warehouse model construction module, used to construct a digital twin model corresponding to a specified logistics warehouse.

[0009] The robot information acquisition module is used to obtain the basic information corresponding to each handling robot in the specified logistics warehouse and the operation information within a specified period.

[0010] The robot capability analysis module is used to analyze the basic information of each handling robot and obtain the corresponding operation capability evaluation coefficient of each handling robot within a specified period, which is recorded as i represents the number corresponding to each transport robot, i=1,2......n.

[0011] The robot operation analysis module is used to analyze the corresponding operation information and operation capability evaluation coefficient of each handling robot in a specified period, and obtain the corresponding time allocation compliance coefficient of each handling robot in a specified period, which is recorded as

[0012] The robot handling analysis module is used to analyze the handling distribution compliance coefficient of each handling robot within a specified period based on the corresponding operation information and operation capacity evaluation coefficient of each handling robot within a specified period, which is recorded as α i .

[0013] The warehouse robot scheduling analysis module is used to analyze the reasonable scheduling evaluation coefficient corresponding to the handling robots in the specified logistics warehouse based on the time allocation compliance coefficient and handling allocation compliance coefficient corresponding to each handling robot within the specified period.

[0014] The warehouse robot scheduling judgment module is used to judge the rationality of the corresponding work scheduling of the handling robot in the specified logistics warehouse based on the reasonable evaluation coefficient of the corresponding work scheduling of the handling robot in the specified logistics warehouse.

[0015] The early warning terminal is used to issue early warning prompts when the work scheduling corresponding to the handling robots in the designated logistics warehouse is unreasonable.

[0016] Preferably, the basic information corresponding to each transport robot includes usage time and number of maintenance times.

[0017] The operation information of each transport robot within a specified period includes transport time, charging time, number of package transports, weight of each package transported, and distance of each package transported.

[0018] Preferably, the analysis obtains the corresponding operating capability evaluation coefficient of each handling robot within a specified period. The specific analysis process is as follows: Substitute the corresponding usage time and maintenance times of each handling robot into the calculation formula The corresponding operating capability evaluation coefficient of each handling robot in the specified cycle is obtained Where T i , Q i They represent the usage time and maintenance times of the i-th handling robot, respectively. T and Q are the reference operation time and reference maintenance times of the handling robot, respectively. ε1 and ε2 are the weight factors corresponding to the usage time and maintenance times of the handling robot, respectively.

[0019] Preferably, the analysis obtains the time allocation compliance coefficient corresponding to each transport robot in the specified period. The specific analysis process is as follows: based on the operation capability evaluation coefficient corresponding to each transport robot in the specified period, the reference operation time and reference charging time corresponding to each transport robot in the specified period are obtained, which are respectively recorded as T i ′ and cT i ′.

[0020] By calculating the formula Get the time distribution compliance coefficient of each handling robot within the specified period Where T i ″、cT i ″ respectively represent the handling time and charging time corresponding to the i-th handling robot in the specified period, γ1 and γ2 are the weight factors corresponding to the set handling time and charging time of the handling robot, respectively.

[0021] Preferably, the analysis obtains the handling distribution compliance coefficient corresponding to each handling robot in the specified period. The specific analysis process is as follows: Based on the operation capability evaluation coefficient corresponding to each handling robot in the specified period and the number of package handling in the specified period, the reasonable evaluation coefficient of the handling number distribution corresponding to each handling robot in the specified period is obtained by analysis, which is recorded as α1 i .

[0022] According to the operation capability evaluation coefficient of each handling robot in a specified period and the weight and distance of each package handled in a specified period, the reasonable evaluation coefficient of package distribution corresponding to each handling robot in a specified period is obtained by analysis, which is recorded as α2 i .

[0023] According to the calculation formula Get the handling distribution compliance coefficient α corresponding to each handling robot in the specified period i , where η1 and η2 are the weight factors corresponding to the reasonable evaluation coefficient of the set number of handling times and the reasonable evaluation coefficient of the package distribution, respectively.

[0024] Preferably, the analysis obtains a reasonable evaluation coefficient for the number of handling times corresponding to each handling robot within a specified period. The specific analysis process is as follows: Substitute the operating capacity evaluation coefficient corresponding to each handling robot within a specified period and the number of package handling times within the specified period into the calculation formula The reasonable evaluation coefficient α1 of the number of handling times corresponding to each handling robot in the specified period is obtained i , where W i ′ represents the number of package handling times of the i-th handling robot within a specified period, and λ is the correction factor corresponding to the reasonable evaluation coefficient of the set handling times.

[0025] Preferably, the analysis obtains the reasonable evaluation coefficient of the package distribution corresponding to each transport robot in the specified period. The specific analysis process is as follows: the weight of each package handled by each transport robot in the specified period is calculated by the average value to obtain the average weight of the packages handled by each transport robot in the specified period, which is recorded as

[0026] The package handling distances of each handling robot corresponding to each time in a specified period are calculated by averaging to obtain the average package handling distance of each handling robot in a specified period, which is recorded as

[0027] According to the calculation formula Get the reasonable evaluation coefficient α2 of the package distribution corresponding to each handling robot in the specified period i , where Δκ1 and Δκ2 are the set differences in the proportion of package handling weight and the proportion of package handling distance of the handling robot, respectively; μ1 and μ2 are the weight factors corresponding to the set proportion of package handling weight and the proportion of package handling distance of the handling robot, respectively.

[0028] Preferably, the analysis obtains the reasonable evaluation coefficient of the work scheduling corresponding to the handling robots in the specified logistics warehouse. The specific analysis process is as follows: the time allocation corresponding to each handling robot in the specified period is calculated according to the coefficient and transport distribution compliance coefficient α i Substitute into the calculation formula The reasonable evaluation coefficient β of the work scheduling corresponding to the handling robot in the specified logistics warehouse is obtained, where τ1 and τ2 are the weight factors corresponding to the set time distribution compliance coefficient and handling distribution compliance coefficient respectively, and e represents a natural constant. It is the correction factor corresponding to the set scheduling rationality assessment coefficient.

[0029] Preferably, the rationality of the work scheduling corresponding to the handling robot in the designated logistics warehouse is judged, and the specific judgment process is as follows: the reasonable evaluation coefficient of the work scheduling corresponding to the handling robot in the designated logistics warehouse is compared with the set reasonable evaluation coefficient of the work scheduling of the standard handling robot. If the reasonable evaluation coefficient of the work scheduling corresponding to the handling robot in the designated logistics warehouse is greater than or equal to the reasonable evaluation coefficient of the work scheduling of the standard handling robot, then the work scheduling corresponding to the handling robot in the designated logistics warehouse is judged to be reasonable; otherwise, the work scheduling corresponding to the handling robot in the designated logistics warehouse is judged to be unreasonable.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. The artificial intelligence-based digital twin system provided by the present invention constructs a digital twin model of a designated logistics warehouse, and then analyzes the rationality of the scheduling of handling robots in the designated logistics warehouse, thereby solving the problem of superficial and one-sided analysis of the rationality of the scheduling of handling robots in the current technology, realizing the intelligent management of the scheduling of handling robots, effectively ensuring the rationality of the scheduling of handling robots in the logistics warehouse, ensuring the uniformity of the package handling distribution of the handling robots, reducing the operating loss of the handling robots, ensuring the stability of the efficiency of the handling robots and the overall operating efficiency of the logistics warehouse, and also reducing the damage rate of the handling robots and the operating cost of the logistics warehouse.

[0032] 2. In the robot capability analysis module, the present invention analyzes the operating capability of each handling robot in a designated logistics warehouse, laying the foundation for the subsequent rationality analysis of handling robot scheduling and ensuring the stability of logistics warehouse operation.

[0033] 3. In the robot operation analysis module, the present invention analyzes the operating time and charging time of each handling robot in a designated logistics warehouse, thereby effectively ensuring the rationality of the time allocation of the handling robots, reducing the occurrence of charging congestion of the handling robots, and ensuring the stable and efficient operation of the logistics warehouse.

[0034] 4. In the robot handling analysis module of the present invention, the rationality of the handling of the root handling robot is analyzed according to the package handling weight and distance of each handling robot in the designated logistics warehouse, thereby ensuring the rationality of the scheduling of the handling robot, reducing the number of failures and operating losses of the handling robot, and improving the operating quality of the handling robot, thereby improving the operating effect and efficiency of the logistics warehouse. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only 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.

[0036] Figure 1 This is a schematic diagram of the system module structure connection of the present invention. DETAILED DESCRIPTION

[0037] 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 the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] See also Figure 1 As shown, the present invention provides a digital twin system based on artificial intelligence, including: a warehouse model construction module, a robot information acquisition module, a robot capability analysis module, a robot operation analysis module, a robot handling analysis module, a warehouse robot scheduling analysis module, a warehouse robot scheduling judgment module and an early warning terminal.

[0039] The robot information acquisition module is respectively connected to the warehouse model construction module, the robot capability analysis module, the robot operation analysis module and the robot handling analysis module; the robot capability analysis module is also connected to the robot operation analysis module and the robot handling analysis module; the warehouse robot scheduling analysis module is respectively connected to the robot operation analysis module, the robot handling analysis module and the warehouse robot scheduling judgment module; the warehouse robot scheduling judgment module is also connected to the early warning terminal.

[0040] The warehouse model construction module is used to build a digital twin model corresponding to a specified logistics warehouse.

[0041] In the above, a digital twin model corresponding to the specified logistics warehouse is constructed. The specific construction process is as follows: cameras are installed at each preset monitoring position in the specified logistics warehouse, and then the image set corresponding to the specified logistics warehouse is collected by each camera in the specified logistics warehouse, thereby constructing a digital twin model corresponding to the specified logistics warehouse.

[0042] It should be noted that real-time images of the designated logistics warehouse are collected by each camera in the designated logistics warehouse, and then transmitted to the digital twin model of the designated logistics warehouse. Through image processing and fusion, the status of the designated logistics warehouse is displayed in real time in the digital twin model of the designated logistics warehouse. The position of each handling robot in the designated logistics warehouse is obtained in real time through GPS positioning and imported into the digital twin model of the designated logistics warehouse to display the position of each handling robot in the designated logistics warehouse in real time.

[0043] The embodiment of the present invention constructs a digital twin model of a designated logistics warehouse and displays the status of the designated logistics warehouse in real time, which helps managers understand the operating status of the designated logistics warehouse and can also more intuitively understand the position of the handling robots in the designated logistics warehouse, thereby ensuring the timeliness of fault discovery and maintenance of the handling robots, and improving the overall handling efficiency of the handling robots in the warehouse.

[0044] The robot information acquisition module is used to obtain the basic information corresponding to each handling robot in the specified logistics warehouse and the operation information within a specified period.

[0045] In the above, the basic information corresponding to each transport robot includes the usage time and the number of maintenance times; the operation information of each transport robot within a specified period includes the transport time, the charging time, the number of package transports, the weight of each package transported, and the distance of each package transported.

[0046] It should be noted that the basic information corresponding to each handling robot in the designated logistics warehouse and the operation information within the specified period are obtained from the digital twin model of the designated logistics warehouse.

[0047] It should also be noted that the specific process of obtaining the weight of each package handled by each handling robot within a specified period is as follows: the package image corresponding to each package handled by each handling robot is obtained from the digital twin model of the specified logistics warehouse, and then the barcode of the package in the package image corresponding to each package handled by each handling robot is scanned to obtain the package weight corresponding to each package handled by each handling robot.

[0048] The robot capability analysis module is used to analyze the basic information of each handling robot and obtain the corresponding operation capability evaluation coefficient of each handling robot within a specified period, which is recorded as i represents the number corresponding to each transport robot, i=1,2......n.

[0049] In a specific embodiment, the operation capability evaluation coefficient corresponding to each handling robot in a specified period is obtained by analysis. The specific analysis process is as follows: Substitute the corresponding usage time and maintenance times of each handling robot into the calculation formula The corresponding operating capability evaluation coefficient of each handling robot in the specified cycle is obtained Where T i , Q i They represent the usage time and maintenance times of the i-th handling robot, respectively. T and Q are the reference operation time and reference maintenance times of the handling robot, respectively. ε1 and ε2 are the weight factors corresponding to the usage time and maintenance times of the handling robot, respectively.

[0050] The present invention analyzes the operating capabilities of each handling robot in a designated logistics warehouse in the robot capability analysis module, laying the foundation for the subsequent rationality analysis of handling robot scheduling and ensuring the stability of logistics warehouse operation.

[0051] The robot operation analysis module is used to analyze the corresponding operation information and operation capability evaluation coefficient of each handling robot in a specified period, and obtain the corresponding time allocation compliance coefficient of each handling robot in a specified period, which is recorded as

[0052] In a specific embodiment, the time allocation compliance coefficient corresponding to each transport robot in a specified period is obtained by analysis. The specific analysis process is as follows: Based on the operation capability evaluation coefficient corresponding to each transport robot in a specified period, the reference operation time and reference charging time corresponding to each transport robot in the specified period are obtained, which are respectively denoted as T i ′ and cT i ′.

[0053] In the above, the reference operation time and reference charging time corresponding to each transport robot in the specified period are obtained. The specific acquisition process is as follows: the operation capability evaluation coefficient corresponding to each transport robot in the specified period is compared with the reference operation time corresponding to the set operation capability evaluation coefficient of each transport robot to obtain the reference operation time corresponding to each transport robot in the specified period. Similarly, the reference charging time corresponding to each transport robot in the specified period is obtained by analysis.

[0054] By calculating the formula Get the time distribution compliance coefficient of each handling robot within the specified period Where T i ″、cT i ″ respectively represent the handling time and charging time corresponding to the i-th handling robot in the specified period, γ1 and γ2 are the weight factors corresponding to the set handling time and charging time of the handling robot, respectively.

[0055] It should be noted that when the handling robot is not scheduled reasonably, it will cause the handling robot to carry continuously, and the long-term handling of the handling robot will cause the internal parts to heat up, thereby affecting the speed of the handling robot and increasing the wear and tear of the internal parts of the handling robot. Therefore, it is necessary to analyze the operating time of the handling robot within a specified cycle; when the temperature is high, the power consumption of the handling robot is accelerated, and the charging time of the handling robot that consumes power too quickly will increase, but long-term charging will occupy the charging position, thereby affecting the charging time of other handling robots. Therefore, it is necessary to analyze the charging time of the handling robot within a specified cycle.

[0056] The present invention analyzes the operating time and charging time of each handling robot in a designated logistics warehouse in the robot operation analysis module, thereby effectively ensuring the rationality of the time allocation of the handling robots, reducing the occurrence of charging congestion of the handling robots, and ensuring the stable and efficient operation of the logistics warehouse.

[0057] The robot handling analysis module is used to analyze the handling distribution compliance coefficient of each handling robot within a specified period based on the corresponding operation information and operation capacity evaluation coefficient of each handling robot within a specified period, which is recorded as α i .

[0058] In a specific embodiment, the handling distribution compliance coefficient corresponding to each handling robot in a specified period is obtained by analysis. The specific analysis process is as follows: Based on the operation capability evaluation coefficient corresponding to each handling robot in a specified period and the number of package handling times in a specified period, the reasonable evaluation coefficient of the handling number distribution corresponding to each handling robot in a specified period is obtained by analysis, which is recorded as α1 i .

[0059] According to the operation capability evaluation coefficient of each handling robot in a specified period and the weight and distance of each package handled in a specified period, the reasonable evaluation coefficient of package distribution corresponding to each handling robot in a specified period is obtained by analysis, which is recorded as α2 i .

[0060] According to the calculation formula Get the handling distribution compliance coefficient α corresponding to each handling robot in the specified period i , where η1 and η2 are the weight factors corresponding to the reasonable evaluation coefficient of the set number of handling times and the reasonable evaluation coefficient of the package distribution, respectively.

[0061] In another specific embodiment, the reasonable evaluation coefficient of the number of handling times corresponding to each handling robot in a specified period is obtained by analysis. The specific analysis process is as follows: the operation capacity evaluation coefficient corresponding to each handling robot in a specified period and the number of package handling times in a specified period are substituted into the calculation formula The reasonable evaluation coefficient α1 of the number of handling times corresponding to each handling robot in the specified period is obtained i , where W i ′ represents the number of package handling times of the i-th handling robot within a specified period, and λ is the correction factor corresponding to the reasonable evaluation coefficient of the set handling times.

[0062] In another specific embodiment, the reasonable evaluation coefficient of the package distribution corresponding to each transport robot in a specified period is obtained by analysis. The specific analysis process is as follows: the weight of each package handled by each transport robot in the specified period is calculated by averaging to obtain the average weight of the packages handled by each transport robot in the specified period, which is recorded as

[0063] The package handling distances of each handling robot corresponding to each time in a specified period are calculated by averaging to obtain the average package handling distance of each handling robot in a specified period, which is recorded as

[0064] According to the calculation formula Get the reasonable evaluation coefficient α2 of the package distribution corresponding to each handling robot in the specified period i , where Δκ1 and Δκ2 are the set differences in the proportion of package handling weight and the proportion of package handling distance of the handling robot, respectively; μ1 and μ2 are the weight factors corresponding to the set proportion of package handling weight and the proportion of package handling distance of the handling robot, respectively.

[0065] In the robot handling analysis module, the present invention analyzes the rationality of the handling of the root handling robots according to the package handling weight and distance of each handling robot in the specified logistics warehouse, thereby ensuring the rationality of the scheduling of the handling robots, reducing the number of failures and operating losses of the handling robots, and improving the operating quality of the handling robots, thereby improving the operating effect and efficiency of the logistics warehouse.

[0066] The warehouse robot scheduling analysis module is used to analyze the reasonable scheduling evaluation coefficient corresponding to the handling robots in the specified logistics warehouse based on the time allocation compliance coefficient and handling allocation compliance coefficient corresponding to each handling robot within the specified period.

[0067] In a specific embodiment, the reasonable evaluation coefficient of the work scheduling corresponding to the handling robots in the specified logistics warehouse is obtained by analysis. The specific analysis process is as follows: the time allocation corresponding to each handling robot in the specified period is calculated according to the coefficient and transport distribution compliance coefficient α i Substitute into the calculation formula The reasonable evaluation coefficient β of the work scheduling corresponding to the handling robot in the specified logistics warehouse is obtained, where τ1 and τ2 are the weight factors corresponding to the set time distribution compliance coefficient and handling distribution compliance coefficient respectively, and e represents a natural constant. It is the correction factor corresponding to the set scheduling rationality assessment coefficient.

[0068] The warehouse robot scheduling judgment module is used to judge the rationality of the corresponding work scheduling of the handling robot in the specified logistics warehouse based on the reasonable evaluation coefficient of the corresponding work scheduling of the handling robot in the specified logistics warehouse.

[0069] In another specific embodiment, the rationality of the work scheduling corresponding to the handling robot in the designated logistics warehouse is judged, and the specific judgment process is as follows: the reasonable evaluation coefficient of the work scheduling corresponding to the handling robot in the designated logistics warehouse is compared with the set reasonable evaluation coefficient of the work scheduling of the standard handling robot. If the reasonable evaluation coefficient of the work scheduling corresponding to the handling robot in the designated logistics warehouse is greater than or equal to the reasonable evaluation coefficient of the work scheduling of the standard handling robot, then the work scheduling corresponding to the handling robot in the designated logistics warehouse is judged to be reasonable; otherwise, the work scheduling corresponding to the handling robot in the designated logistics warehouse is judged to be unreasonable.

[0070] The early warning terminal is used to issue early warning prompts when the work scheduling corresponding to the handling robots in the designated logistics warehouse is unreasonable.

[0071] The embodiment of the present invention constructs a digital twin model of a designated logistics warehouse, and then analyzes the rationality of the scheduling of handling robots in the designated logistics warehouse, thereby solving the problem that the current technology analyzes the rationality of the scheduling of handling robots in a superficial and one-sided manner, realizes the intelligent management of the scheduling of handling robots, effectively ensures the rationality of the scheduling of handling robots in the logistics warehouse, ensures the uniformity of the package handling distribution of the handling robots, reduces the operating loss of the handling robots, ensures the stability of the efficiency of the handling robots and the overall operating efficiency of the logistics warehouse, and also reduces the speed of damage of the handling robots and the operating cost of the logistics warehouse.

[0072] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. The digital twin system based on artificial intelligence is characterized by: include: Warehouse model construction module, used to build a digital twin model corresponding to a specified logistics warehouse; The robot information acquisition module is used to obtain the basic information corresponding to each handling robot in the specified logistics warehouse and the operation information within a specified period; The robot capability analysis module is used to analyze the basic information of each handling robot and obtain the corresponding operation capability evaluation coefficient of each handling robot within a specified period, which is recorded as , i represents the number corresponding to each handling robot, ; The robot operation analysis module is used to analyze the corresponding operation information and operation capability evaluation coefficient of each handling robot in a specified period, and obtain the corresponding time allocation compliance coefficient of each handling robot in a specified period, which is recorded as ; The robot handling analysis module is used to analyze the handling allocation compliance coefficient of each handling robot within a specified period based on the corresponding operation information and operation capability evaluation coefficient of each handling robot within a specified period, which is recorded as ; The warehouse robot scheduling analysis module is used to analyze the reasonable scheduling evaluation coefficients of the handling robots in a specified logistics warehouse based on the time allocation compliance coefficient and handling allocation compliance coefficient of each handling robot within a specified period; The warehouse robot scheduling judgment module is used to judge the rationality of the corresponding work scheduling of the handling robots in the specified logistics warehouse based on the reasonable evaluation coefficient of the corresponding work scheduling of the handling robots in the specified logistics warehouse; The early warning terminal is used to issue early warning prompts when the work scheduling corresponding to the handling robots in the designated logistics warehouse is unreasonable.

2. The artificial intelligence-based digital twin system according to claim 1, characterized in that: The basic information corresponding to each transport robot includes usage time and number of maintenance times; The operation information of each transport robot within a specified period includes transport time, charging time, number of package transports, weight of each package transported, and distance of each package transported.

3. The artificial intelligence-based digital twin system according to claim 2, characterized in that: The analysis obtains the corresponding operating capability evaluation coefficient of each handling robot within a specified period. The specific analysis process is as follows: Substitute the usage time and maintenance times of each handling robot into the calculation formula The corresponding operating capability evaluation coefficient of each handling robot in the specified cycle is obtained ,in 、 They represent the usage time and maintenance times of the i-th handling robot, respectively. T and Q are the reference operation time and reference maintenance times of the handling robot, respectively. 、 They are the weight factors corresponding to the set usage time and maintenance times of the handling robot.

4. The artificial intelligence-based digital twin system according to claim 2, characterized in that: The analysis obtains the time allocation compliance coefficient corresponding to each handling robot within a specified period. The specific analysis process is as follows: Based on the operation capability evaluation coefficient of each handling robot in a specified period, the reference operation time and reference charging time of each handling robot in a specified period are obtained, which are recorded as and ; By calculating the formula , get the time distribution compliance coefficient of each handling robot in the specified period ,in 、 They represent the handling time and charging time of the i-th handling robot in a specified period, 、 They are the weight factors corresponding to the set handling time and charging time of the handling robot.

5. The artificial intelligence-based digital twin system according to claim 2, characterized in that: The analysis obtains the handling distribution compliance coefficient corresponding to each handling robot within a specified period. The specific analysis process is as follows: Based on the operation capability evaluation coefficient of each handling robot in a specified period and the number of package handling times in a specified period, the reasonable evaluation coefficient of the number of handling times corresponding to each handling robot in a specified period is obtained by analysis, which is recorded as ; According to the operation capability evaluation coefficient of each handling robot in a specified period and the weight and distance of each package handled in a specified period, the reasonable evaluation coefficient of package distribution corresponding to each handling robot in a specified period is obtained by analysis, which is recorded as ; According to the calculation formula , get the handling distribution compliance coefficient of each handling robot in the specified period ,in 、 Assign reasonable assessment coefficients to the set number of transports and weight factors corresponding to the reasonable assessment coefficients to the packages.

6. The artificial intelligence-based digital twin system according to claim 5, characterized in that: The analysis yields the reasonable evaluation coefficient of package distribution corresponding to each handling robot within a specified period. The specific analysis process is as follows: The weight of each package handled by each handling robot in a specified period is calculated by averaging to obtain the average weight of the packages handled by each handling robot in a specified period, which is recorded as ; The package handling distances of each handling robot corresponding to each time in a specified period are calculated by averaging to obtain the average package handling distance of each handling robot in a specified period, which is recorded as ; According to the calculation formula , get the reasonable evaluation coefficient of package distribution corresponding to each handling robot in the specified period ,in 、 They are the weight ratio difference and distance ratio difference of the packages handled by the set handling robots, 、 They are the weight factors corresponding to the proportion of package handling weight and the proportion of package handling distance of the set handling robot.

7. The artificial intelligence-based digital twin system according to claim 1, characterized in that: The analysis obtains the reasonable evaluation coefficient of the work scheduling corresponding to the handling robots in the specified logistics warehouse. The specific analysis process is as follows: the time allocation corresponding to each handling robot in the specified period meets the coefficient and transport distribution compliance coefficient Substitute into the calculation formula In the calculation, the reasonable evaluation coefficient of the work scheduling corresponding to the handling robot in the specified logistics warehouse is obtained. ,in 、 They are the weight factors corresponding to the set time allocation compliance coefficient and transportation allocation compliance coefficient, and e represents a natural constant. It is the correction factor corresponding to the set scheduling rationality assessment coefficient.

8. The artificial intelligence-based digital twin system according to claim 1, wherein: The rationality of the work scheduling corresponding to the handling robot in the designated logistics warehouse is judged, and the specific judgment process is as follows: the reasonable evaluation coefficient of the work scheduling corresponding to the handling robot in the designated logistics warehouse is compared with the set reasonable evaluation coefficient of the work scheduling of the standard handling robot. If the reasonable evaluation coefficient of the work scheduling corresponding to the handling robot in the designated logistics warehouse is greater than or equal to the reasonable evaluation coefficient of the work scheduling of the standard handling robot, then the work scheduling corresponding to the handling robot in the designated logistics warehouse is judged to be reasonable; otherwise, the work scheduling corresponding to the handling robot in the designated logistics warehouse is judged to be unreasonable.

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