Work order distribution method and device, electronic equipment and storage medium
By acquiring work orders to be dispatched and using a decision tree model for automated verification, qualified groups are selected, solving the problems of high cost and low accuracy of manual inspection in existing technologies, and achieving efficient and accurate work order dispatch.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-29
- Publication Date
- 2026-04-14
AI Technical Summary
In existing technologies, the verification of the business status of group customers relies on manual methods, which results in high costs and low accuracy, cannot effectively cover all group customers, and the verification results need further analysis and integration.
By acquiring work orders to be dispatched, candidate groups are selected based on the group status and internal operating status. A pre-built decision tree for group operating status is used for automated verification to determine the target group and dispatch work orders.
This improved the efficiency and accuracy of work order dispatch, ensuring that work orders were accurately dispatched to the groups requiring rectification, reducing the cost of manual inspection, and improving management efficiency and operational quality.
Smart Images
Figure CN121860299A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of enterprise operation status management and data analysis technology. Specifically, this invention relates to a work order dispatching method, device, electronic device, and storage medium. Background Technology
[0002] As operators expand their business scope, the number of enterprise customer files is gradually increasing. To address issues such as incomplete or inaccurate file information and non-standard operations, the "Enterprise Verification" capability is uniformly invoked in the ESMP system to verify the business status of enterprises. However, this capability cannot cover all enterprise customers, and the verification results need further analysis and integration to identify groups whose verification results, business status, and other relevant indicators do not match their actual status. Work orders are then dispatched for account managers to carry out targeted rectification.
[0003] In existing technologies, the group's operational status is usually checked manually, which is costly and inaccurate. Summary of the Invention
[0004] The technical problem to be solved by the present invention is to provide a work order dispatching method, apparatus, electronic device and storage medium, which aims to solve at least one of the above-mentioned technical problems.
[0005] Firstly, the technical solution of the present invention to solve the above-mentioned technical problems is as follows: a work order dispatching method, the method comprising: Get work orders to be dispatched; Based on the group status and internal operating status of each group in all groups, delete the groups whose group status is cancelled and whose internal operating status is that there are invalid businesses, and obtain the candidate group set. Based on the verification logic in the pre-built group operation status decision tree, each group in the candidate group set is verified to obtain the target group that passes the verification. The work orders to be dispatched will be sent to the target group.
[0006] The beneficial effects of this invention are as follows: By acquiring work orders to be dispatched and filtering them based on group status and internal operational status, groups that do not meet the requirements (i.e., groups whose status is cancelled and have invalid business) are deleted, thus obtaining a candidate group set. Next, a pre-constructed group operational status decision tree is used to verify each group in the candidate group set, ultimately determining the target groups that pass the verification and dispatching work orders to these target groups. This method can efficiently filter out groups that meet the requirements, avoiding the high cost and low accuracy problems of manual verification. Simultaneously, through the automated verification logic of the decision tree model, the efficiency and accuracy of work order dispatch are improved, ensuring that work orders are accurately dispatched to groups requiring rectification, thereby improving overall management efficiency and operational quality.
[0007] Based on the above technical solution, the present invention can be further improved as follows.
[0008] Furthermore, based on the group status and internal operating status of each group in all groups, groups with a group status of cancellation and internal operating status of invalid business are deleted, resulting in a candidate group set, including: For each group, determine whether the group status is cancelled. If the group status is cancelled, determine whether there are invalid business operations within the group. If there are invalid business operations within the group, delete the group from all groups until all groups with cancelled status and invalid business operations within the group are deleted to obtain a candidate group set.
[0009] Furthermore, the verification logic in the aforementioned group's operational status decision tree includes: checking whether the group's internal operational status is in operation through the first-level verification node, checking whether it is a group within the blacklist through the second-level verification node, and checking whether it has been continuously in arrears through the third-level verification node. Based on the verification logic in the pre-constructed group operation status decision tree, each group in the candidate group set is verified to obtain the target groups that pass the verification, including: For each candidate group in the candidate group set, check whether the candidate group's internal operating status is "in operation". If the internal operating status is "in operation", the candidate group is identified as the target group. If the internal operating status is not "in operation", check whether the candidate group is a group in the blacklist. If the candidate group is a group in the blacklist, check whether the candidate group has been continuously in arrears. If the candidate group has been continuously in arrears, the candidate group is identified as the target group.
[0010] Furthermore, for each candidate group in the candidate group set, the method also includes: if the candidate group is not a group in the blacklist, then the candidate group is determined not to be the target group.
[0011] Furthermore, for each candidate group in the candidate group set, the method also includes: If the candidate group continues to make payments, it is determined that the candidate group is not the target group.
[0012] Furthermore, the aforementioned group's operational status decision tree is constructed based on the following method: Get all sample groups, excluding groups whose status is cancelled and whose internal business has failed. Based on the number of sample groups in different first situations among all sample groups, and the number of sample groups in different first situations that have passed the final verification, the first Gini coefficient corresponding to each first situation is determined. According to the first situation corresponding to the smallest first Gini coefficient among all first Gini coefficients, the first-level verification node is determined. All first situations include potential and online status, internal group operation status, blacklisted groups and continuous non-billing status. Each sample group in the potential and online states is taken as the initial group. Based on the number of initial groups in different second cases among all initial groups, and the number of initial groups in different second cases that pass the final verification, the second Gini coefficient corresponding to each second case is determined. The second-level verification node is determined according to the second case corresponding to the smallest second Gini coefficient among all second Gini coefficients. All second cases include all first cases except for the potential and online states. Based on the third scenario, the three-level verification nodes are determined. The third scenario consists of all the first scenarios except for the first scenario corresponding to the first-level verification node and the potential and on-network states. A decision tree for the group's operational status is generated based on the first-level verification node, the second-level verification node, and the third-level verification node.
[0013] Secondly, in order to solve the above-mentioned technical problems, the present invention also provides a work order dispatching device, the device comprising: The acquisition module is used to acquire work orders to be dispatched. The candidate module is used to delete groups whose group status is cancelled and whose internal business status is invalid, based on the group status and internal business status of each group in all groups, to obtain a candidate group set. The verification module is used to verify each group in the candidate group set based on the verification logic in the pre-built group operation status decision tree, and obtain the target group that passes the verification. The dispatch module is used to dispatch work orders to the target group.
[0014] Thirdly, in order to solve the above-mentioned technical problems, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the work order dispatching method of the present application.
[0015] Fourthly, in order to solve the above-mentioned technical problems, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the work order dispatching method of the present application.
[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below.
[0018] Figure 1 A flowchart illustrating a work order dispatching method according to an embodiment of the present invention; Figure 2 A flowchart illustrating another work order dispatching method provided in an embodiment of the present invention; Figure 3 A schematic diagram of a group in a deregistered state is provided as an embodiment of the present invention; Figure 4 This is a schematic diagram of the overall Gini coefficient in a first-level classification according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the first Gini coefficient corresponding to the internal operating status of a group in a first-level classification, provided as an embodiment of the present invention; Figure 6 This is a schematic diagram of the first Gini coefficient corresponding to a blacklist group in a first-level classification, provided as an embodiment of the present invention. Figure 7 This is a schematic diagram of the first Gini coefficient corresponding to the continuously unbilled status in a first-level classification, provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the overall Gini coefficient in a second-level classification according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the second Gini coefficient corresponding to a blacklist group in a second-level classification, provided as an embodiment of the present invention. Figure 10 This is a schematic diagram of the second Gini coefficient corresponding to the continuously unbilled status in a second-level classification, provided in an embodiment of the present invention; Figure 11 This is a schematic diagram of the structure of a work order dispatching device according to an embodiment of the present invention; Figure 12 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present invention. Detailed Implementation
[0019] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.
[0020] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.
[0021] The solution provided in this invention can be applied to any application scenario that requires dispatching work orders to the group. The solution provided in this invention can be executed by any electronic device, such as a user's terminal device, including at least one of the following: smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, smart TV, or smart in-vehicle device.
[0022] This invention provides a possible implementation, such as... Figure 1 The diagram shows a flowchart of a work order dispatch method. This method can be executed by any electronic device, such as a terminal device, or jointly by a terminal device and a server. For ease of description, the method provided in this embodiment will be described below using a terminal device as the execution subject as an example. Figure 1 The flowchart shown indicates that the method may include the following steps: S10, retrieve work orders to be dispatched; S20, based on the group status and internal operating status of each group in all groups, delete the groups whose group status is cancelled and whose internal operating status is that there are invalid businesses, and obtain the candidate group set. S30, based on the verification logic in the pre-built group operation status decision tree, verifies each group in the candidate group set to obtain the target group that passes the verification; S40 dispatches work orders to the target group.
[0023] The method of this invention obtains work orders to be dispatched and filters them based on the group status and internal operational status, deleting groups that do not meet the requirements (i.e., groups whose status is cancelled and have invalid business), thus obtaining a candidate group set. Next, a pre-constructed group operational status decision tree is used to verify each group in the candidate group set, ultimately determining the target groups that pass the verification, and dispatching work orders to these target groups. This method can efficiently filter out groups that meet the requirements, avoiding the high cost and low accuracy of manual verification. At the same time, the automated verification logic of the decision tree model improves the efficiency and accuracy of work order dispatch, ensuring that work orders are accurately dispatched to groups requiring rectification, thereby improving overall management efficiency and operational quality.
[0024] The following specific embodiments further illustrate the solution of the present invention. In these embodiments, the purpose of the present invention is to provide operators with a group operation status verification method based on a decision tree model for managing a large number of group customers, and to identify groups whose group verification results, business status and other related indicators do not match the group status.
[0025] In this solution, decision tree analysis is used to construct a decision tree for the group's operational status. Decision tree analysis is a risk-based decision-making method for obtaining the optimal solution. It analyzes the probability of occurrence and the risks associated with different branch events among several alternative solutions to determine their feasibility. It can perform supervised learning based on indicators such as internal group verification results, whether there are active businesses within the group, whether the group is on a blacklist, and whether the group has not issued bills for four consecutive months, ultimately making the optimal decision and reducing manual identification costs.
[0026] Based on this, combined Figure 2 The work order dispatching method provided in this embodiment may include the following steps: S10, retrieve work orders to be dispatched; Among them, the work orders to be dispatched refer to the work orders that need to be dispatched to the target group that has passed the verification.
[0027] S20, based on the group status and internal operating status of each group in all groups, delete the groups whose group status is cancelled and whose internal operating status (also known as the business status under the group) has invalid business, and obtain the candidate group set; The group status is either cancelled or not cancelled.
[0028] The group's data is extensive. For groups in a deregistered state, there must be no active business transactions under them. Therefore, the judgment result can be directly output through two indicators: "Group Status" and "Business Status under the Group." That is, if the group status is "Deregistered" and the business status under the group is "Active (meaning there are ineffective business transactions)," it will be directly judged as failing and deleted. For "online" or "potential" groups, further judgment is needed based on indicators such as the results of internal business status verification, whether the group is on a blacklist, and whether there are continuously outstanding billings. These will be explained later.
[0029] As an example, see Figure 3 The diagram shows that there are 18,194 groups in the deregistration status, of which 13,321 are inactive (no inactive business) and 4,873 are active (inactive business exists).
[0030] Alternatively, one implementation of S20 above is as follows: For each group, determine whether the group status is cancelled. If the group status is cancelled, determine whether there are invalid business operations within the group. If there are invalid business operations within the group, delete the group from all groups until all groups with cancelled status and invalid business operations within the group are deleted to obtain a candidate group set.
[0031] S30, based on the verification logic in the pre-built group operation status decision tree, verifies each group in the candidate group set to obtain the target group that passes the verification; Optionally, the verification logic in the above-mentioned group operating status decision tree includes: checking whether the group's internal operating status is in operation through the first-level verification node, checking whether it is a group in the blacklist through the second-level verification node, and checking whether it has been continuously not billed through the third-level verification node. See Figure 2 One implementation of S30 above is as follows: For each candidate group in the candidate group set, check whether the candidate group's internal operating status is "in operation". If the internal operating status is "in operation", the candidate group is identified as the target group. If the internal operating status is not "in operation", check whether the candidate group is a group in the blacklist. If the candidate group is a group in the blacklist, check whether the candidate group has been continuously in arrears. If the candidate group has been continuously in arrears, the candidate group is identified as the target group.
[0032] The three levels of verification nodes (Level 1, Level 2, and Level 3) indicate that verification begins at the Level 1 node, followed by verification at the Level 2 node based on the Level 1 results, and finally verification at the Level 3 node based on the Level 2 results. The Level 1 node verifies whether the group's internal operations are in progress; the Level 2 node verifies whether the group is on a blacklist; and the Level 3 node verifies whether the group has consistently failed to process payments.
[0033] Optionally, for each candidate group in the candidate group set, the method further includes: if the candidate group is not a group in the blacklist, then determine that the candidate group is not the target group.
[0034] Optionally, for each candidate group in the candidate group set, the method further includes: if the candidate group continues to make payments, then the candidate group is determined not to be the target group.
[0035] S40 dispatches work orders to the target group.
[0036] Optionally, the above group operating status decision tree is constructed based on the following method: Get all sample groups, excluding groups whose status is cancelled and whose internal business has failed. Based on the number of sample groups in different first scenarios across all sample groups, and the number of sample groups in different first scenarios that pass final verification, the first Gini coefficient corresponding to each first scenario is determined. The first-level verification node is determined based on the first scenario corresponding to the smallest first Gini coefficient among all first Gini coefficients. All first scenarios include potential and online status, internal group operating status, blacklisted groups, and continuously unbilled status. The first scenario corresponding to the first-level verification node is the internal group operating status. Each sample group in the potential and online states is used as an initial group. Based on the number of initial groups in different second cases among all initial groups, and the number of initial groups in different second cases that pass the final verification, the second Gini coefficient corresponding to each second case is determined. The second case corresponding to the smallest second Gini coefficient among all second Gini coefficients is used to determine the second verification node. All second cases include all first cases except for the potential and online states. The first case corresponding to the second verification node is the blacklist group. Based on the third scenario, a three-level verification node is determined. The third scenario includes all first scenarios except for the first scenario corresponding to the first-level verification node and the potential and online states. The first scenario corresponding to the third-level verification node is the continuous non-billing status. A decision tree for the group's operational status is generated based on the first-level verification node, the second-level verification node, and the third-level verification node.
[0037] Among them, the groups that pass the final verification are the groups that can be used as target groups.
[0038] As an example, the above group's operational status decision tree can be constructed based on the following method: For the first-level verification node: The overall Gini coefficient is determined based on the number of all sample groups, the number of sample groups in the network among all sample groups, and the number of potential sample groups. See Figure 4 There were 31,804 potential sample groups online, and 3,722 sample groups were ultimately verified.
[0039] Overall Gini coefficient = Gini(overall) = 1 - (3722 / 31804) 2 -(28082 / 31804) 2 = 0.206666893; Since no deregistered groups were included in this statistic, the internal operating status verification results of the groups can be directly classified into two categories: those with abnormal verification status and those with no verification results are grouped into one category, while those with normal verification results (in operation) are distinguished.
[0040] Specifically, see Figure 5 Based on the number of operating sample groups (2640) among all sample groups, the operating Gini coefficient is determined; Operating Gini coefficient = Gini(operating) = 1 - (2640 / 2640) 2 - (0 / 2640) 2 = 0; Based on the number of non-operational sample groups that passed the final verification (1082) and the number of non-operational sample groups in all sample groups (29164), the non-operational Gini coefficient is determined; Non-operational Gini coefficient = Gini(abnormal state + no verification result) = 1 - (1082 / 29164) 2 - (28082 / 29164) 2 = 0.07144817; where 28082 is the number of groups in the non-operational sample groups that did not pass the final verification; Based on the total number of sample groups (31804), the operating Gini coefficient, the non-operating Gini coefficient, the number of operating sample groups (2640), and the number of non-operating sample groups (29164), the Gini coefficient for the internal operating status of a group is determined; Internal operating status Gini coefficient = Gini(internal operating status of the group) = (29164) 0.07144817 + 2640 0) / 31804=0.06551737; See Figure 6 Based on the number of blacklisted sample groups (1650) in all sample groups, the number of sample groups that passed the final verification (439) in all blacklisted sample groups, and the number of sample groups that failed the final verification (1211) in all blacklisted sample groups, the Gini coefficient of the first blacklisted group is determined; Gini coefficient of the first blacklisted group = Gini(yes) = 1 - (439 / 1650) 2 -(1211 / 1650) 2 = 0.39054472; Based on the number of non-blacklisted sample groups (2511) among all sample groups, the number of sample groups that passed the final verification (27643) among all non-blacklisted sample groups, and the number of non-blacklisted sample groups (30154) among all sample groups, the Gini coefficient of the second blacklisted group is determined; Gini coefficient of the second blacklisted group = Gini (no) = 1 - (2511 / 30154) 2-(27643 / 30154) 2 =0.152676439; Based on the total number of sample groups (31,804), the number of blacklisted sample groups (1,650), the number of non-blacklisted sample groups (30,154), and the Gini coefficients of the first and second blacklisted groups, the blacklist Gini coefficient is determined. Wherein, the Gini coefficient of the blacklist = Gini(blacklist group) = (30154) 0.152676439+1650 0.39054472) / 31804 = 0.165017109; See Figure 7 Based on the number of sample groups with continuous non-payment of 16,562 and the number of sample groups with continuous non-payment of 14,329 that passed the final verification, the first continuous non-payment Gini coefficient is determined. The first continuous non-payment Gini coefficient = Gini(yes) = 1 - (14,329 / 16,562). 2 - (2233 / 16562) 2 = 0.233296939; where 2233 is the number of sample groups that failed the final verification among all sample groups that have not yet been billed. Based on the number of sample groups with continuous payment (15242), the number of sample groups with continuous payment that passed final verification (1489), and the number of sample groups with continuous payment that failed final verification (13753), the second continuous non-payment Gini coefficient is determined, where the second continuous non-payment Gini coefficient = Gini(No) = 1 - (1489 / 15242) 2 - (13753 / 15242) 2 =0.17629428; Based on the number of sample groups with continuous non-payment (16,562), the first continuous non-payment Gini coefficient, the second continuous non-payment Gini coefficient, the number of sample groups with continuous payment (15,242), and the total number of sample groups (31,804), the target continuous non-payment Gini coefficient is determined. Target Gini coefficient for persistent non-payment = Gini(persistent non-payment) = (16562) 0.233296939+15242 0.17629428) / 31804 = 0.205978535; Based on the overall Gini coefficient, the Gini coefficient of the group's internal operating status, the Gini coefficient of the blacklist, and the Gini coefficient of the target's continuous non-payment, a first-level verification node is determined. Specifically, the Gini coefficient corresponding to the group's internal operating status is the smallest among the overall Gini coefficient, the Gini coefficient of the group's internal operating status, the Gini coefficient of the blacklist, and the Gini coefficient of the target's continuous non-payment. This is used as the standard for the first-level classification of the decision tree, i.e., the first-level verification node. If the result of the group's internal operating status verification is "in operation," it can be directly judged as passing.
[0041] Based on the above method for determining the primary verification node, the secondary verification node is determined. For details, please refer to [link to relevant documentation]. Figures 8 to 10 Based on 29,164 potential but non-operational sample groups in the Internet+ network, the following processing is performed on these 29,164 sample groups to determine secondary verification nodes; See Figure 8 Overall Gini coefficient: There are 29,164 sample groups including online and potential but not operating groups, of which 1,082 sample groups passed the final verification.
[0042] Overall Gini coefficient = Gini(overall) = 1 - (28082 / 29164)2 - (1082 / 29164)2 = 0.20776677; See Figure 9 Gini coefficient of blacklisted groups: Gini(is) = 1 - (439 / 1521) 2 - (439 / 1521) 2 = 0.952725413; Gini (No) = 1 - (27643 / 27643) 2 - (0 / 27643) 2 =0; Gini (Blacklist Group) = (27643 0+1521 0.952725413) / 29164 = 0.049687812; See Figure 10 Gini coefficient that has not been paid out: Gini(is) = 1 - (14329 / 15411) 2 -(1082 / 15411) 2 = 0.144632088; Gini (No) = 1 - (13753 / 13753) 2 -(0 / 13753) 2 =0; Gini (not yet billed) = (15411 0.144632088+13753 0) / 31804 = 0.076427277; In summary, the blacklist group has the lowest Gini coefficient, serving as the standard for secondary classification in the decision tree, i.e., the secondary verification node. The "non-blacklist" group can be directly judged as failing. Therefore, whether or not payments have been continuously delayed can serve as the final classification criterion in the decision tree, i.e., the tertiary verification node.
[0043] The Gini coefficient in decision trees is an indicator used to measure the impurity or disorder of a dataset. It is calculated based on the probability that two randomly drawn samples will have inconsistent class labels. The Gini value is a way to measure dataset impurity; it represents the probability that two randomly drawn samples from the dataset will have inconsistent class labels. The smaller the Gini value, the higher the purity of the dataset, meaning a higher proportion of samples in the dataset belong to the same class. The formula for calculating the Gini value (which is existing technology and will not be explained in detail in this solution) is: The Gini coefficient gain measures the reduction in impurity of a dataset after splitting it based on a specific attribute. In decision tree construction, we aim to find the splitting method that minimizes the Gini coefficient, resulting in purer child nodes. The Gini gain is calculated based on the change in Gini values before and after the split, using a weighted average to account for the impact of different branches on the overall Gini coefficient.
[0044] The key technical point of this invention is: 1. Clean the original data, removing duplicate, erroneous, and irrelevant data to ensure data accuracy and consistency. The "cancellation" of a group mainly depends on the business status of the group. This part of the data cannot be used in the algorithm's prediction, otherwise it will affect the accuracy of the prediction results. 2. Select the features most relevant to the group's operating status from among many features to improve the accuracy and efficiency of the model; 3. To avoid overfitting, cross-validation is used to determine the optimal tree structure.
[0045] The method provided by this invention can be used to predict the current operating status of a group, mainly including: Decision trees are generated based on existing data through supervised learning, providing identification standards for subsequent group status verification. It provides automatic identification capabilities for verifying the group's compliance, reducing the cost and efficiency of manual identification.
[0046] Based on and Figure 1 Based on the same principle as the method shown, this embodiment of the invention also provides a work order dispatching device 20, such as... Figure 11 As shown, the work order dispatching device 20 may include an acquisition module 210, a candidate module 220, a verification module 230, and a dispatching module 240, wherein: Module 210 is used to obtain work orders to be dispatched; Candidate module 220 is used to delete groups whose group status is cancelled and whose internal business status is invalid, based on the group status and internal business status of each group in all groups, to obtain a candidate group set. The verification module 230 is used to verify each group in the candidate group set based on the verification logic in the pre-built group operation status decision tree, and obtain the target group that passes the verification. The dispatch module 240 is used to dispatch work orders to the target group.
[0047] Optionally, when the candidate module 220 deletes groups whose group status is cancelled and whose internal operating status is invalid, based on the group status and internal operating status of each group in all groups, to obtain the candidate group set, it is specifically used for: For each group, determine whether the group status is cancelled. If the group status is cancelled, determine whether there are invalid business operations within the group. If there are invalid business operations within the group, delete the group from all groups until all groups with cancelled status and invalid business operations within the group are deleted to obtain a candidate group set.
[0048] Optionally, the verification logic in the above-mentioned group operating status decision tree includes: checking whether the group's internal operating status is in operation through the first-level verification node, checking whether it is a group in the blacklist through the second-level verification node, and checking whether it has been continuously not billed through the third-level verification node. The aforementioned verification module 230, based on the verification logic in the pre-built group operation status decision tree, verifies each group in the candidate group set and obtains the target group that passes the verification. Specifically, it is used for: For each candidate group in the candidate group set, check whether the candidate group's internal operating status is "in operation". If the internal operating status is "in operation", the candidate group is identified as the target group. If the internal operating status is not "in operation", check whether the candidate group is a group in the blacklist. If the candidate group is a group in the blacklist, check whether the candidate group has been continuously in arrears. If the candidate group has been continuously in arrears, the candidate group is identified as the target group.
[0049] Optionally, for each candidate group in the candidate group set, the device further includes: a first judgment module, used to determine that the candidate group is not the target group when the candidate group is not a group in the blacklist.
[0050] Optionally, for each candidate group in the candidate group set, the device further includes: a first judgment module, used to determine that a candidate group is not the target group when the candidate group continues to make payments.
[0051] Optionally, the above group operating status decision tree is constructed based on the following method: Get all sample groups, excluding groups whose status is cancelled and whose internal business has failed. Based on the number of sample groups in different first situations among all sample groups, and the number of sample groups in different first situations that have passed the final verification, the first Gini coefficient corresponding to each first situation is determined. According to the first situation corresponding to the smallest first Gini coefficient among all first Gini coefficients, the first-level verification node is determined. All first situations include potential and online status, internal group operation status, blacklisted groups and continuous non-billing status. Each sample group in the potential and online states is taken as the initial group. Based on the number of initial groups in different second cases among all initial groups, and the number of initial groups in different second cases that pass the final verification, the second Gini coefficient corresponding to each second case is determined. The second-level verification node is determined according to the second case corresponding to the smallest second Gini coefficient among all second Gini coefficients. All second cases include all first cases except for the potential and online states. Based on the third scenario, the three-level verification nodes are determined. The third scenario consists of all the first scenarios except for the first scenario corresponding to the first-level verification node and the potential and on-network states. A decision tree for the group's operational status is generated based on the first-level verification node, the second-level verification node, and the third-level verification node.
[0052] The work order dispatching device of this invention can execute the work order dispatching method provided in this invention, and their implementation principles are similar. The actions performed by each module and unit in the work order dispatching device in each embodiment of this invention correspond to the steps in the work order dispatching method in each embodiment of this invention. For detailed functional descriptions of each module of the work order dispatching device, please refer to the descriptions in the corresponding work order dispatching methods shown above, which will not be repeated here.
[0053] The aforementioned work order dispatching device can be a computer program (including program code) running on a computer device, such as an application software; the device can be used to execute the corresponding steps in the method provided in the embodiments of the present invention.
[0054] In some embodiments, the work order dispatching device provided in this invention can be implemented using a combination of hardware and software. As an example, the work order dispatching device provided in this invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the work order dispatching method provided in this invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.
[0055] In other embodiments, the work order dispatching device provided in this invention can be implemented in software. Figure 11 A work order dispatching device stored in a memory is shown. It can be software in the form of programs and plug-ins, and includes a series of modules, including an acquisition module 210, a candidate module 220, a verification module 230, and a dispatching module 240, for implementing the work order dispatching method provided in the embodiments of the present invention.
[0056] The modules described in the embodiments of the present invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.
[0057] Based on the same principles as the methods shown in the embodiments of the present invention, the embodiments of the present invention also provide an electronic device, which may include, but is not limited to: a processor and a memory; the memory for storing computer programs; and the processor for executing the methods shown in any embodiment of the present invention by invoking the computer programs.
[0058] In one alternative embodiment, an electronic device is provided, such as Figure 12 As shown, Figure 12The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.
[0059] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
[0060] Bus 4002 may include a pathway for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 12 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.
[0061] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.
[0062] The memory 4003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.
[0063] Among these, electronic devices can also be terminal devices. Figure 12 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.
[0064] This invention provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.
[0065] According to another aspect of the present invention, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various embodiments described above.
[0066] Computer program code for performing the operations of this invention can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, and conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0067] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0068] The computer-readable storage medium provided in this invention can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0069] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.
[0070] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.
Claims
1. A work order dispatching method, characterized in that, include: Get work orders to be dispatched; Based on the group status and internal operating status of each group in all groups, delete the groups whose group status is cancelled and whose internal operating status is that there are invalid businesses, and obtain the candidate group set. Based on the verification logic in the pre-constructed group operation status decision tree, each group in the candidate group set is verified to obtain the target group that passes the verification. The work order to be dispatched is sent to the target group.
2. The method according to claim 1, characterized in that, Based on the group status and internal operating status of each group in all groups, groups with a group status of cancellation and internal operating status of invalid business are deleted, resulting in a candidate group set, including: For each group, determine whether the group status is cancelled. If the group status is cancelled, determine whether there are invalid business operations within the group. If there are invalid business operations within the group, delete the group from all groups until all groups with cancelled status and invalid business operations within the group are deleted to obtain a candidate group set.
3. The method according to claim 1, characterized in that, The verification logic in the group's operational status decision tree includes: checking whether the group's internal operational status is in operation through the first-level verification node, checking whether it is a group in the blacklist through the second-level verification node, and checking whether it has been continuously in arrears through the third-level verification node. Based on the verification logic in the pre-constructed group operation status decision tree, each group in the candidate group set is verified to obtain the target groups that pass the verification, including: For each candidate group in the candidate group set, check whether the internal operating status of the candidate group is "in operation". If the internal operating status of the group is "in operation", then the candidate group is determined as the target group. If the internal operating status of the group is not "in operation", then check whether the candidate group is a group in the blacklist. If the candidate group is a group in the blacklist, then check whether the candidate group has been continuously without payment. If the candidate group has been continuously without payment, then the candidate group is determined as the target group.
4. The method according to claim 3, characterized in that, For each candidate group in the candidate group set, the method further includes: if the candidate group is not a group in the blacklist, then determining that the candidate group is not the target group.
5. The method according to claim 3, characterized in that, For each candidate group in the candidate group set, the method further includes: if the candidate group continues to make payments, then determining that the candidate group is not the target group.
6. The method according to any one of claims 1 to 5, characterized in that, The group's operational status decision tree is constructed based on the following method: Get all sample groups, excluding groups whose status is cancelled and whose internal business has failed. Based on the number of sample groups in different first situations among all sample groups, and the number of sample groups in different first situations that have passed the final verification, the first Gini coefficient corresponding to each first situation is determined. According to the first situation corresponding to the smallest first Gini coefficient among all first Gini coefficients, the first-level verification node is determined. All first situations include potential and online status, internal group operation status, blacklisted groups and continuous non-billing status. Each sample group in the potential and online states is taken as the initial group. Based on the number of initial groups in different second cases among all initial groups, and the number of initial groups in different second cases that pass the final verification, the second Gini coefficient corresponding to each second case is determined. The second-level verification node is determined according to the second case corresponding to the smallest second Gini coefficient among all second Gini coefficients. All second cases include all first cases except for the potential and online states. Based on the third scenario, a three-level verification node is determined. The third scenario is all the first scenarios except for the first scenario corresponding to the first-level verification node and the potential and on-network states. Based on the first-level verification node, the second-level verification node, and the third-level verification node, a decision tree for the group's operating status is generated.
7. A work order dispatching device, characterized in that, include: The acquisition module is used to acquire work orders to be dispatched. The candidate module is used to delete groups whose group status is cancelled and whose internal business status is invalid, based on the group status and internal business status of each group in all groups, to obtain a set of candidate groups. The verification module is used to verify each group in the candidate group set based on the verification logic in the pre-built group operation status decision tree, and obtain the target group that passes the verification. The dispatch module is used to dispatch the work order to be dispatched to the target group.
8. The apparatus according to claim 7, characterized in that, When the candidate module deletes groups whose status is cancelled and whose internal operating status is invalid, based on the group status and internal operating status of each group in all groups, and obtains the candidate group set, it is specifically used for: For each group, determine whether the group status is cancelled. If the group status is cancelled, determine whether there are invalid business operations within the group. If there are invalid business operations within the group, delete the group from all groups until all groups with cancelled status and invalid business operations within the group are deleted to obtain a candidate group set.
9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1-6.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1-6.