Multi-tenant task scheduling method, device, equipment and storage medium

Abstract

The present invention provides a multi-tenant task scheduling method, device, equipment and storage medium, and relates to the technical field of SAAS online car-hailing task scheduling. The present invention can play a decentralized role by deploying multiple platform nodes to form a platform network, which can not only reduce the risk of single point failure and data tampering, but also be more conducive to processing the data of multiple tenants at the same time, avoiding processing delays, and using platform nodes to analyze and process the scheduling information of each task scheduling of the tenant, so as to identify bad tenants with long-term and multiple timeouts, facilitate the processing of bad tenants, and avoid damaging the overall image of the company.

Description

Technical Field

[0001] The present invention relates to the technical field of SAAS online car-hailing task scheduling, and specifically to a multi-tenant task scheduling method, device, equipment and storage medium. Background Art

[0002] SAAS is a cloud computing-based software delivery model that provides software as a service to users over the Internet. At present, the commonly used scheduling method for online ride-hailing is to build a server background based on SAAS to exchange data and schedule tasks. However, this single-body architecture has certain risks and requires backup servers to avoid system paralysis caused by failures. If attacked, it is easy to cause data tampering. Moreover, during peak vehicle usage periods, the server needs to process a large amount of scheduling information at the same time, which greatly increases the response time of the system. This not only weakens the user experience, but also makes it difficult to distinguish between bad tenants who deliberately drive around and cause timeouts and normal tenants who cause timeouts due to system delays, which is not conducive to maintaining the company's image.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-tenant task scheduling method, device, equipment and storage medium to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The multi-tenant task scheduling method includes the following steps:

[0007] S1: Collect location information of multiple groups of tenants, and deploy multiple platform nodes according to the location information to build a platform network;

[0008] S2: Collect the scheduling information and execution information of tenants, and use the platform nodes to execute and verify the scheduling information;

[0009] S3: Use the platform node to judge the tenant's execution information, generate the judgment result, and use the platform network to store and disclose the judgment result;

[0010] S4: Generate the tenant's dishonesty index according to the determination result, and determine the tenant's dishonesty index to screen out bad tenants.

[0011] Preferably, a consensus algorithm is deployed between the platform nodes, the location information includes vehicle trajectories and map coordinates, which are used to detect vehicle density in different areas, the scheduling information includes identity information, request information and end information, and the execution information includes task start time and task end time.

[0012] Preferably, the logic for executing and verifying the scheduling information is:

[0013] The tenant sends the identity information and request information to the platform node, and the platform node first verifies the tenant's identity information;

[0014] After the identity authentication is passed, the platform node executes the received request information and dispatches the task to the tenant;

[0015] After the tenant task scheduling is completed, the end information will be sent to the platform node. The scheduling information will be written into the platform node and published to the entire platform network.

[0016] Preferably, the logic for generating the determination result is:

[0017] Number N different areas and calculate the vehicle density δ according to the vehicle density δ i , determine the degree of congestion in the area, and generate the congestion coefficient ε according to the degree of congestion i , the subscript i indicates the number of the region;

[0018] According to the tenant's task start time Tk, task end time Tj, congestion coefficient ε i Generate judgment result A j , the subscript j represents the number of the task scheduling, and the calculation formula is:

[0019]

[0020] Where △T represents the preset time threshold.

[0021] Preferably, the congestion coefficient ε i The generation logic is:

[0022] According to the vehicle density δ in different areas i Generate average vehicle density The calculation formula is:

[0023]

[0024] Based on the average vehicle density Generate the congestion coefficient ε of the corresponding area i ,

[0025]

[0026] Where θ1 ,θ 2 ,θ 3 is the preset congestion constant, and n is the preset fluctuation coefficient.

[0027] Preferably, the judgment logic for the tenant's dishonesty index is:

[0028] According to the judgment result A of the tenant's M-time task scheduling j Generate a dishonesty index The calculation formula is:

[0029]

[0030] When the dishonesty index , the tenant is considered to be a bad tenant, where Ay is the default threshold.

[0031] A multi-tenant task scheduling device, the task scheduling device is used to execute the above-mentioned task scheduling method, including:

[0032] A first data collection module, used to collect location information of tenants;

[0033] A second data collection module, the second data collection module is communicatively connected to the first data collection module and is used to collect scheduling information and execution information of the tenant;

[0034] A first determination module, the first determination module is communicatively connected to the second data acquisition module, and is used to determine the execution information of the tenant;

[0035] A second determination module, which is in communication with the first determination module and is used to determine the tenant's dishonesty index;

[0036] A storage module is communicatively connected with the first determination module and the second determination module, and is used to store the scheduling information, the execution information and the determination result.

[0037] A multi-tenant task scheduling device, the task scheduling device comprising:

[0038] Storage media for storing computer programs;

[0039] A processor is used to execute the computer program to implement the above method.

[0040] A multi-tenant task scheduling storage medium, wherein the storage medium is used to store a computer program, and the computer program implements the above method when executed by a processor.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention can play a decentralized role by deploying multiple platform nodes to form a platform network, which can not only reduce the risk of single point failure and data tampering, but also be more conducive to processing data of multiple tenants at the same time and avoid processing delays. At the same time, the platform nodes are used to analyze and process the scheduling information of each task scheduling of the tenant, which can identify bad tenants with long-term and multiple timeouts, facilitate the processing of bad tenants, and avoid damaging the company's overall image. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the process of the present invention;

[0043] Figure 2 It is a schematic diagram of the module structure of the present invention. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0045] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0046] Example:

[0047] See also Figure 1 - Figure 2 , the present invention provides a technical solution:

[0048] The multi-tenant task scheduling method includes the following steps:

[0049] S1: Collect location information of multiple groups of tenants, and deploy multiple platform nodes based on the location information, build a platform network based on the SAAS platform, and deploy a consensus algorithm between the platform nodes. By deploying the consensus algorithm, data exchange between the platform nodes can be realized to improve the security and reliability of the platform network. The location information includes vehicle trajectories and map coordinates, which are used to detect vehicle density in different areas. By setting up multiple platform nodes, a decentralized effect can be achieved, reducing the risk of single point failures and data tampering, and it can also be more conducive to processing data of multiple tenants at the same time to avoid processing delays.

[0050] S2: Collect the scheduling information and execution information of the tenant. The scheduling information includes identity information, request information and end information. The execution information includes the task start time and task end time. The tenant sends the identity information and request information to the platform node. Each platform node is preset with the tenant's identity information. The platform node first verifies the tenant's identity information. After the identity authentication is passed, the platform node executes the received request information and issues task scheduling to the tenant. After the tenant task scheduling is completed, the end information is sent to the platform node. The scheduling information is written into the platform node and announced to the entire platform network through communication between each platform node. The platform node is used to realize the execution, verification and storage of the scheduling information, which is convenient for subsequent viewing and review of the scheduling information.

[0051] S3: Use the platform node to judge the tenant's execution information and generate a judgment result. The judgment result is generated by numbering N different areas and dividing them into groups according to the vehicle density δ i , determine the degree of congestion in the area, and generate the congestion coefficient ε according to the degree of congestion i , the subscript i indicates the number of the region;

[0052] According to the tenant's task start time Tk, task end time Tj, congestion coefficient ε i Generate judgment result A j , the subscript j represents the number of the task scheduling, and the calculation formula is:

[0053]

[0054] Where △T represents the preset time threshold. It can be seen that when the congestion coefficient ε i When a task is scheduled, the longer it takes, the more likely it is that the result A j The larger the value of A j >1 indicates timeout, congestion coefficient ε i The generation logic is:

[0055] According to the vehicle density δ in different areas i Generate average vehicle density The calculation formula is:

[0056]

[0057] Based on the average vehicle density Generate the congestion coefficient ε of the corresponding area i ,

[0058]

[0059] Where θ 1 ,θ 2 ,θ 3 is the preset congestion constant, and θ 1 <θ 2 <θ 3 , n is the preset fluctuation coefficient, when the vehicle density δ i Less than average vehicle density When the congestion coefficient ε i When the vehicle density δ i When it increases, the corresponding congestion coefficient ε i The value will also become larger, and the time required for a task scheduling will also become longer, realizing dynamic adjustment to avoid system misjudgment due to traffic congestion. Finally, the platform network will be used to store and publish the judgment results.

[0060] S4: Generate the tenant's dishonesty index based on the determination result, and determine the tenant's dishonesty index. The determination logic for the tenant's dishonesty index is:

[0061] According to the judgment result A of the tenant's M-time task scheduling j Generate a dishonesty index The calculation formula is:

[0062]

[0063] Dishonesty Index The larger the value, the more times and duration the tenant has exceeded the limit. , the tenant is considered to be a bad tenant, and the bad tenants are screened, where Ay is the default threshold.

[0064] A multi-tenant task scheduling device, the task scheduling device is used to execute the above-mentioned task scheduling method, including:

[0065] The first data collection module uses a car GPS module to collect the location information of the tenant.

[0066] The second data collection module adopts a small vehicle-mounted computer, and the second data collection module is connected to the first data collection module for collecting the scheduling information and execution information of the tenant;

[0067] A first determination module, the first determination module is communicatively connected to the second data acquisition module, and is used to determine the execution information of the tenant;

[0068] A second determination module, wherein both the first determination module and the second determination module are preset software modules of the platform node, and the second determination module is communicatively connected with the first determination module and is used to determine the tenant's dishonesty index;

[0069] A storage module is communicatively connected with the first determination module and the second determination module, and is used to store the scheduling information, the execution information and the determination result.

[0070] This embodiment also provides a multi-tenant task scheduling device, and the task scheduling device includes:

[0071] Storage media for storing computer programs;

[0072] A processor is used to execute the computer program to implement the above method.

[0073] This embodiment also provides a multi-tenant task scheduling storage medium, where the storage medium is used to store a computer program, and the computer program implements the above method when executed by a processor.

[0074] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0075] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by computer software, electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0076] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0077] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A multi-tenant task scheduling method, characterized in that: The following steps are involved: S1: Collect location information of multiple groups of tenants, and deploy multiple platform nodes according to the location information to build a platform network; S2: Collect the scheduling information and execution information of tenants, and use the platform nodes to execute and verify the scheduling information; The logic for executing and verifying the scheduling information is as follows: The tenant sends the identity information and request information to the platform node, and the platform node first verifies the tenant's identity information; After the identity authentication is passed, the platform node executes the received request information and dispatches the task to the tenant; After the tenant task scheduling is completed, the end information is sent to the platform node. The scheduling information is written into the platform node and published to the entire platform network. S3: Use the platform node to judge the tenant's execution information, generate a judgment result, and use the platform network to store and disclose the judgment result; The generation logic of the determination result is: Number N different areas and calculate the vehicle density δ according to the vehicle density δ i , determine the degree of congestion in the area, and generate the congestion coefficient ε according to the degree of congestion i , the subscript i indicates the number of the region; According to the tenant's task start time Tk, task end time Tj, congestion coefficient ε i Generate judgment result A j , the subscript j represents the number of the task scheduling, and the calculation formula is: Where △T represents the preset time threshold; The congestion coefficient ε i The generation logic is: According to the vehicle density δ in different areas i Generate average vehicle density The calculation formula is: Based on the average vehicle density Generate the congestion coefficient ε of the corresponding area i , Where θ1, θ2, θ3 are preset congestion constants, and n is the preset fluctuation coefficient; The judgment logic for the tenant's dishonesty index is as follows: According to the judgment result A of the tenant's M-time task scheduling j Generate a dishonesty index The calculation formula is: When the dishonesty index When , the tenant is considered to be a bad tenant, where Ay is the default threshold; S4: Generate the tenant's dishonesty index according to the determination result, and determine the tenant's dishonesty index to screen out bad tenants.

2. The multi-tenant task scheduling method according to claim 1, characterized in that: A consensus algorithm is deployed between the platform nodes. The location information includes vehicle trajectories and map coordinates, which are used to detect vehicle density in different areas. The scheduling information includes identity information, request information and end information. The execution information includes task start time and task end time.

3. A multi-tenant task scheduling device, characterized in that: The task scheduling device is used to execute the task scheduling method according to any one of claims 1 to 2, comprising: A first data collection module, used to collect location information of tenants; A second data collection module, the second data collection module is communicatively connected to the first data collection module and is used to collect scheduling information and execution information of the tenant; A first determination module, the first determination module is communicatively connected to the second data acquisition module, and is used to determine the execution information of the tenant; A second determination module, which is in communication with the first determination module and is used to determine the tenant's dishonesty index; A storage module is communicatively connected with the first determination module and the second determination module, and is used to store the scheduling information, the execution information and the determination result.

4. Multi-tenant task scheduling equipment, characterized by: The task scheduling device comprises: Storage media for storing computer programs; A processor, configured to execute the computer program to implement the method according to any one of claims 1 to 2.

5. Multi-tenant task scheduling storage medium, characterized by: The storage medium is used to store a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

Images