Inquiry clue evaluation method and device, electronic equipment and storage medium
By extracting features from inquiry leads and utilizing salesperson feature information, pre-evaluation and joint evaluation tasks are generated and distributed, thus solving the problem of low efficiency in inquiry lead evaluation in the existing technology and achieving efficient inquiry lead evaluation.
Patent Information
- Application Number
- CN202510842851.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-23
- Publication Date
- 2025-10-17
AI Technical Summary
The evaluation efficiency of inquiry leads in existing technologies is low and heavily dependent on the personal ability and level of salesmen, resulting in low efficiency.
By extracting features from inquiry leads, pre-evaluation tasks are generated and distributed to the salesperson evaluation unit. Effective pre-evaluation parameters are determined based on the salesperson's characteristic information. By combining the salesperson's independent evaluation with the joint evaluation of various departments, joint evaluation tasks are generated and distributed to complete the evaluation of inquiry leads.
It improves the efficiency of inquiry lead evaluation, effectively combines the independent evaluation of salesmen with the joint evaluation of various departments, and improves the accuracy and efficiency of evaluation.
Smart Images
Figure CN120806344A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of data processing, and particularly relates to a quotation lead evaluation method and device, electronic equipment and storage medium. BACKGROUND
[0002] At present, after receiving a quotation lead, a business staff member usually independently evaluates the quotation lead, or consults relevant personnel of production, procurement, finance and other departments respectively, and then evaluates the quotation lead according to the consultation result. This process seriously depends on the personal ability and level of the business staff member, and is low in efficiency.
[0003] In related technologies, some third-party platforms provide a solution for automatically processing a quotation lead. The quotation lead received is distributed to a corresponding supplier, and then quotation information is obtained from the supplier. The supplier usually directly distributes the quotation lead to a certain business staff member through manual work or a simple customer relationship management system, and the business staff member independently processes the quotation lead; or the quotation lead is directly distributed to all departments such as production and procurement, and then the departments feed back results. These processing methods still have the problem of low efficiency. SUMMARY
[0004] Therefore, the present application provides a quotation lead evaluation method and device, electronic equipment and storage medium to solve the problem of low efficiency in evaluating a quotation lead in the prior art.
[0005] In a first aspect, the present application provides a quotation lead evaluation method, comprising:
[0006] performing feature extraction on the quotation lead to obtain a feature set of the quotation lead, the feature set comprising at least one product parameter; wherein each product parameter comprises at least parameter information and a parameter type;
[0007] generating a pre-evaluation task based on the feature set, and sending the pre-evaluation task to a business staff member evaluation unit;
[0008] in response to receiving a pre-evaluation pass message, obtaining a business staff member identifier in the pre-evaluation pass message, and obtaining business staff member feature information from a business staff member information management unit based on the business staff member identifier; wherein the business staff member feature information comprises a level of the business staff member corresponding to different parameter types;
[0009] determining an effective pre-evaluation parameter in the pre-evaluation pass message based on the business staff member feature information, the effective pre-evaluation parameter being a product parameter independently evaluated by the business staff member;
[0010] determining other product parameters in the product parameters except the effective pre-evaluation parameter as joint evaluation parameters;
[0011] determine a joint evaluation task based on at least the joint evaluation parameters, and send the joint evaluation task to the joint evaluation unit, so that the joint evaluation unit completes the evaluation of the inquiry lead.
[0012] In a second aspect, the present application provides an inquiry lead evaluation device, comprising:
[0013] The feature extraction module is configured to perform feature extraction on the inquiry lead to obtain a feature set of the inquiry lead, the feature set comprising at least one product parameter; wherein each product parameter comprises at least parameter information and a parameter type;
[0014] The pre-evaluation module is configured to generate a pre-evaluation task based on the feature set, and send the pre-evaluation task to the salesperson evaluation unit;
[0015] The acquisition module is configured to, in response to receiving the pre-evaluation pass message, acquire a salesperson identifier in the pre-evaluation pass message, and acquire salesperson feature information from the salesperson information management unit based on the salesperson identifier; wherein the salesperson feature information comprises a level of the salesperson corresponding to different parameter types;
[0016] The joint evaluation module is configured to determine, based on the salesperson feature information, an effective pre-evaluation parameter in the pre-evaluation pass message, the effective pre-evaluation parameter being a product parameter evaluated independently by the salesperson;
[0017] The joint evaluation module is further configured to determine other product parameters in the product parameters, except for the effective pre-evaluation parameter, as joint evaluation parameters;
[0018] The joint evaluation module is further configured to determine a joint evaluation task based on at least the joint evaluation parameters, and send the joint evaluation task to the joint evaluation unit, so that the joint evaluation unit completes the evaluation of the inquiry lead.
[0019] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above method.
[0020] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the steps of the above method.
[0021] The beneficial effects of the embodiments of the present application compared with the prior art are: the embodiments of the present application extract a feature set of the inquiry clue, generate and distribute a pre-evaluation task based on the feature set, obtain a salesperson identifier under the condition of receiving a pre-evaluation pass message, obtain salesperson feature information according to the salesperson identifier, determine an effective pre-evaluation parameter in the pre-evaluation pass message by using the salesperson feature information, and then obtain a joint evaluation parameter in the feature set except the effective pre-evaluation parameter, generate and distribute a joint evaluation task based on the joint evaluation parameter, so that a joint evaluation unit completes the evaluation of the inquiry clue, effectively combines salesperson independent evaluation and joint evaluation of each department, and improves the inquiry clue evaluation efficiency. BRIEF DESCRIPTION OF DRAWINGS
[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.
[0023] Figure 1 is a flowchart of an inquiry clue evaluation method provided by the embodiments of the present application.
[0024] Figure 2 is a flowchart of a method for processing a pre-evaluation task by a salesperson evaluation unit provided by the embodiments of the present application.
[0025] Figure 3 is a flowchart of a method for determining an effective pre-evaluation parameter in a pre-evaluation pass message based on salesperson feature information provided by the embodiments of the present application.
[0026] Figure 4 is a flowchart of a method for determining a joint evaluation task based on a joint evaluation parameter provided by the embodiments of the present application.
[0027] Figure 5 is a flowchart of a method for executing a joint evaluation task by a joint evaluation unit provided by the embodiments of the present application.
[0028] Figure 6 is a flowchart of a method for evaluating an evaluation subtask by each evaluation department provided by the embodiments of the present application.
[0029] Figure 7 is a flowchart of a method for determining a construction period of an inquiry clue based on construction period information in an evaluation result of each department provided by the embodiments of the present application.
[0030] Figure 8 is a schematic diagram of an inquiry clue evaluation device provided by the embodiments of the present application.
[0031] Figure 9 is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION
[0032] In the following description, for the purpose of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
[0033] A method and device for evaluating a quotation clue according to an embodiment of the present application will be described in detail below with reference to the accompanying drawings.
[0034] As mentioned above, the process of evaluating a quotation clue at the present stage is mostly realized by manual work, which heavily depends on the personal ability and level of the salesperson and is inefficient.
[0035] In view of this, an embodiment of the present application provides a method for evaluating a quotation clue, which extracts a feature set of the quotation clue, generates and distributes a pre-evaluation task based on the feature set, obtains a salesperson identifier under the condition of receiving a pre-evaluation pass message, obtains salesperson feature information according to the salesperson identifier, determines valid pre-evaluation parameters in the pre-evaluation pass message by using the salesperson feature information, and then obtains joint evaluation parameters in the feature set except the valid pre-evaluation parameters, generates and distributes a joint evaluation task based on the joint evaluation parameters, so as to enable a joint evaluation unit to complete the evaluation of the quotation clue, effectively combining independent evaluation by the salesperson and joint evaluation by each department, and improving the evaluation efficiency of the quotation clue.
[0036] Figure 1 is a flowchart of a method for evaluating a quotation clue provided by an embodiment of the present application. As shown in Figure 1 , the method comprises the following steps:
[0037] In step S101, feature extraction is performed on the quotation clue to obtain a feature set of the quotation clue.
[0038] The feature set comprises at least one product parameter, and each product parameter comprises at least parameter information and a parameter type.
[0039] In step S102, a pre-evaluation task is generated based on the feature set, and the pre-evaluation task is sent to a salesperson evaluation unit.
[0040] In step S103, in response to receiving a pre-evaluation pass message, a salesperson identifier in the pre-evaluation pass message is obtained, and salesperson feature information is obtained from a salesperson information management unit based on the salesperson identifier.
[0041] The salesperson characteristic information includes the salesperson's level corresponding to different parameter types.
[0042] In step S104 , valid pre-assessment parameters in the pre-assessment pass message are determined based on the salesperson characteristic information.
[0043] Among them, the effective pre-evaluation parameters are product parameters independently evaluated by sales staff.
[0044] In step S105 , other product parameters except the valid pre-evaluation parameters among the product parameters are determined as joint evaluation parameters.
[0045] In step S106 , a joint evaluation task is determined based on at least the joint evaluation parameters, and the joint evaluation task is sent to the joint evaluation unit, so that the joint evaluation unit completes the inquiry clue evaluation.
[0046] In some embodiments of the present application, the inquiry clue method can be executed by a terminal device or a server.
[0047] In certain embodiments of the present application, after receiving an inquiry thread, feature extraction can be performed on the inquiry thread to obtain a feature set of the inquiry thread. Feature extraction can use a general large language model or a self-trained feature extraction algorithm, which is not limited here.
[0048] The extracted feature set includes at least one product parameter, each of which includes at least parameter information and parameter type. The parameter information can be the specific value specified by the inquirer for the product parameter in the inquiry thread. The parameter type can include, for example, at least one of the following: the production process corresponding to each parameter information, and the product type corresponding to each parameter information.
[0049] Taking the inquiry leads in the field of optical lenses as an example, for the components of an optical lens, the parameter information of the product parameters may include, for example, the material, surface shape, surface defects, angular accuracy, coating, etc. For the components of an optical lens, the parameter information of the product parameters may include, for example, the interface specifications, depth of field, resolution, MTF (Modulation Transfer Function), spatial spacing, center deviation, etc. Among them, optical lens elements may include lenses, prisms, aspherical mirrors, reflectors, cylindrical mirrors, etc., and optical lens components may include lenses, assemblies, etc.
[0050] On the other hand, parameter types corresponding to production processes may include, for example, procurement, processing, assembly, and cost accounting. Parameter types corresponding to product types may include, for example, security lenses, portable terminal device lenses, microscope objective lenses, industrial lighting tube lenses, and aerospace ground camera lenses.
[0051] The pre-evaluation task can be generated based on the extracted feature set and sent to the salesperson evaluation unit. In an example, a pre-evaluation task can be generated after the feature set is extracted, and the pre-evaluation task includes the feature set. In other embodiments, the pre-evaluation task can also include the inquiry lead.
[0052] After receiving the pre-evaluation task, the salesperson evaluation unit can assign the pre-evaluation task to a salesperson for evaluation, receive the pre-evaluation result fed back by the salesperson, and send the result to the terminal device or the server. If the pre-evaluation result is a pre-evaluation pass, the salesperson identifier in the pre-evaluation pass message can be obtained, and the salesperson feature information can be obtained from the salesperson information management unit based on the salesperson identifier. The salesperson feature information includes the salesperson's level corresponding to different parameter types.
[0053] The information in the salesperson information management unit can be pre-configured and updated regularly or irregularly. The feature information of each salesperson can be obtained based on the salesperson's basic situation and historical work situation. In an example, the salesperson's level corresponding to different parameter types can be determined based on the salesperson's basic situation, such as the salesperson's job level information. For example, a director-level salesperson can be set to the highest level corresponding to the cost accounting parameter type, and a junior salesperson can be set to the lowest level corresponding to the cost accounting parameter type.
[0054] A feature matching algorithm can also be pre-trained to match the historical work situation data of different salespersons with different levels of different parameter types to obtain the optimal matching effect. Then, the historical work situation data of the salesperson whose feature information needs to be set can be input into the feature matching algorithm to obtain the salesperson's level corresponding to different parameter types. If the salesperson's historical work situation data is empty, the salesperson's level corresponding to each parameter type can be set to the lowest level.
[0055] In some embodiments of the present application, the valid pre-evaluation parameters in the pre-evaluation pass message can be determined based on the determined salesperson feature information. The valid pre-evaluation parameters are product parameters evaluated by the salesperson independently. That is, the pre-evaluation pass information fed back by the salesperson includes the salesperson's evaluation results of at least part of the product parameters. To improve the evaluation accuracy, the salesperson's evaluation of these product parameters can be further screened based on the salesperson's feature information, and the salesperson's evaluation of the product parameters that can be trusted is selected as the valid pre-evaluation parameters.
[0056] In some embodiments of the present application, other product parameters in the product parameters except the effective pre-evaluation parameters can also be determined as joint evaluation parameters, and a joint evaluation task can be determined based on the joint evaluation parameters.
[0057] The salesperson evaluation unit, the joint evaluation unit and the salesperson information management unit can be configured in the terminal device or the server, or can be configured in other terminal devices or servers, and can be in communicable connection with the terminal device or the server and exchange information.
[0058] According to the technical scheme provided by the embodiments of the present application, by extracting the feature set of the inquiry clues, generating and distributing the pre-evaluation task based on the feature set, obtaining the salesperson identifier under the condition of receiving the pre-evaluation pass message, obtaining the salesperson feature information according to the salesperson identifier, determining the effective pre-evaluation parameters in the pre-evaluation pass message by using the salesperson feature information, and then obtaining the joint evaluation parameters in the feature set except the effective pre-evaluation parameters, generating and distributing the joint evaluation task based on the joint evaluation parameters, so that the joint evaluation unit completes the evaluation of the inquiry clues, effectively combining the independent evaluation of the salesperson and the joint evaluation of each department, and improving the evaluation efficiency of the inquiry clues.
[0059] In some embodiments of the present application, the feature set can also include at least one of order amount information, customer identifier information and product identifier information.
[0060] The customer identifier information is the customer identifier of an existing customer, which can be determined by querying the inquiry party information in the customer information management system. If the inquiry party information matches the target customer information in the customer information management system, the customer identifier information of the target customer can be determined as the customer identifier information of the inquiry clues. Otherwise, if the inquiry party information does not match all the customer information in the customer information management system, the customer identifier information in the feature set can be set as null.
[0061] The product identifier information is standard product information, which includes general standard products and produced customized products. The product identifier information can be determined by querying the product name or product parameters in the inquiry clues in the product information management system. If the product name or product parameters match the target product information in the product information management system, the product identifier information of the target product can be determined as the product identifier information of the inquiry clues. Otherwise, if the product name or product parameters do not match all the product information in the product information management system, the product identifier information in the feature set can be set as null.
[0062] The order amount information can be directly recorded in the inquiry clue, or can be estimated from the product information in the inquiry clue. The estimation can be a rough calculation, for example, by obtaining the product name or product parameter in the inquiry clue to determine the corresponding standard product, or determining a similar standard product when there is no corresponding standard product, and then multiplying the standard unit price of the standard product by the quantity that the inquiring party hopes to purchase as the estimated order amount.
[0063] Figure 2 is a flowchart of a method for processing a pre-evaluation task by a salesperson evaluation unit provided in an embodiment of the present application. As shown in Figure 2 the method comprises the following steps:
[0064] In step S201, in response to determining that the customer identification information is included in the feature set, the target salesperson is determined based on the customer identification information.
[0065] In step S202, in response to determining that the customer identification information is not included in the feature set, and at least one of the order amount information and the product identification information is included, the target salesperson is determined based on a preset mapping relationship.
[0066] The preset mapping relationship includes a mapping relationship among the order amount, the product identification, and the salesperson.
[0067] In step S203, the pre-evaluation task is sent to the target salesperson, and the pre-evaluation result of the inquiry clue by the target salesperson is obtained.
[0068] In some embodiments of the present application, when processing the pre-evaluation task, the salesperson evaluation unit can first determine whether the customer identification information is included in the feature set. If so, the target salesperson can be determined based on the customer identification information. That is, if it is determined that the customer identification information is included in the feature set, it is determined that the inquiring party of the inquiry clue is an existing customer, and at this time, the salesperson responsible for the existing customer can be directly determined as the target salesperson.
[0069] On the other hand, if it is determined that the customer identification information is not included in the feature set, but at least one of the order amount information and the product identification information is included, the target salesperson can be determined based on a preset mapping relationship, which includes a mapping relationship among the order amount, the product identification, and the salesperson.
[0070] That is, a mapping relationship table can be preset, which includes at least a salesperson column, a product identification column, and an order amount column. Different salespersons can be configured with the product identification and order amount that they can handle. Based on the mapping relationship table, the salesperson who can handle the product corresponding to the order amount and the product identification can be determined. If multiple salespersons are determined, the target salesperson can be further determined according to the salesperson idle situation, etc.
[0071] After the target salesperson is determined, the salesperson evaluation unit can send the pre-evaluation task to the target salesperson and obtain the pre-evaluation result of the target salesperson on the inquiry clue. If the pre-evaluation result is not passed, a pre-evaluation not passed message is sent to the terminal device or the server, the evaluation is terminated, and the inquiry clue and the pre-evaluation not passed message are saved to the inquiry clue management unit. The pre-evaluation not passed message at least includes a not passed reason.
[0072] If the pre-evaluation result is passed, a pre-evaluation passed message is sent to the terminal device or the server. The pre-evaluation passed message includes the evaluated product parameter information, and the evaluated product parameter is at least one product parameter in the feature set.
[0073] Figure 3 is a flowchart of a method for determining effective pre-evaluation parameters in a pre-evaluation passed message based on salesperson feature information provided by an embodiment of the present application. As shown in Figure 3 the method includes the following steps:
[0074] In step S301, the target product type of each product parameter in the evaluated product parameter information is obtained.
[0075] In step S302, the level of the salesperson corresponding to each target product type is queried in the salesperson feature information.
[0076] In step S303, the product parameter corresponding to the target product type whose salesperson level is greater than the preset level threshold is determined as the effective pre-evaluation parameter.
[0077] In some embodiments of the present application, when determining the effective pre-evaluation parameter, the target product type of each product parameter in the evaluated product parameter information can be obtained first. For example, if the feature set includes product parameters 1 to N, N is a positive integer greater than 1, and the salesperson has evaluated product parameters P to Q, i.e., the pre-evaluation passed message includes product parameters P to Q, P and Q are positive integers less than or equal to N, and Q is greater than or equal to P. At this time, the product type corresponding to each product parameter P to Q can be determined as the target product type.
[0078] On the other hand, the level of the salesperson corresponding to each target product type can be queried in the salesperson feature information. As described above, the salesperson feature information includes the level of the salesperson corresponding to different parameter types, so the level of the salesperson corresponding to the product type of product parameters P to Q can be queried. Then, the product parameter corresponding to the target product type whose salesperson level is greater than the preset level threshold is determined as the effective pre-evaluation parameter.
[0079] For example, if the salesperson's level for product type X is a first level value, and the preset level threshold value for product type X is a second level value, where product type X is a product type corresponding to product parameter Y, X is a positive integer, and Y is a positive integer greater than or equal to P and less than or equal to Q. If the first level value is greater than the second level value, the product parameter Y can be considered as an effective pre-evaluation parameter. On the contrary, if the first level value is less than or equal to the second level value, the product parameter Y can be considered as an ineffective pre-evaluation parameter.
[0080] In this way, the salesperson's independent evaluation results can be further screened based on the salesperson's pre-evaluation information (i.e., feature information), reducing the risk of unreliable evaluation results caused by low salesperson independent evaluation accuracy.
[0081] Figure 4 is a flowchart of a method for determining a joint evaluation task based on joint evaluation parameters provided by an embodiment of the present application. As shown in Figure 4 the method includes the following steps:
[0082] In step S401, the parameter types of the product parameters in the joint evaluation parameters are obtained.
[0083] In step S402, at least one evaluation department is determined according to the parameter types.
[0084] In step S403, a joint evaluation task is determined, which includes at least the determined evaluation departments and a feature set.
[0085] In some embodiments of the present application, when determining the joint evaluation task, the parameter types of the product parameters in the joint evaluation parameters can be determined first, and then at least one evaluation department can be determined according to the parameter types.
[0086] If the parameter types include parameter types corresponding to production processes, the evaluation departments can be determined directly based on the production process information. On the other hand, if the parameter types do not include parameter types corresponding to production processes, but include parameter types corresponding to product types, the standard production processes of the product types can be obtained, and the evaluation departments can be determined based on the standard production processes.
[0087] After at least one evaluation department is determined, a joint evaluation task can be generated, which can include the determined evaluation departments and a feature set of the inquiry clues. In some embodiments, the joint evaluation task can also include the inquiry clues.
[0088] Figure 5 is a flowchart of a method for a joint evaluation unit to execute a joint evaluation task provided by an embodiment of the present application. As shown in Figure 5 the method includes the following steps:
[0089] In step S501, the joint evaluation parameter is determined for each product parameter corresponding to the process.
[0090] In step S502, the pre-trained path optimization algorithm is used to optimize each process to obtain at least one production scheme.
[0091] In step S503, the evaluation department corresponding to each process in the production scheme is determined to obtain the evaluation sub-tasks of each evaluation department.
[0092] Each evaluation sub-task includes the product parameter to be evaluated in the sub-task.
[0093] In step S504, the evaluation sub-tasks are sent to the corresponding evaluation departments for evaluation to obtain the evaluation results of each department.
[0094] Each department's evaluation result includes production parameter feasibility information and duration information.
[0095] In step S505, in response to determining that the production parameters in each department evaluation result are feasible, the duration of the inquiry clue is determined based on the duration information in each department evaluation result, and the evaluation result is generated.
[0096] In some embodiments of the present application, the joint evaluation unit can first determine the joint evaluation parameter corresponding to each product parameter in the process, and then use the pre-trained path optimization algorithm to optimize each process to obtain at least one production scheme. The pre-trained path optimization algorithm can be trained according to the historical production data of the evaluation party, which will not be described here.
[0097] Next, the evaluation department corresponding to each process in the production scheme can be determined, for example, the evaluation department corresponding to the procurement process is the procurement department, the evaluation department corresponding to the component production process is each component production department, and the evaluation department corresponding to the assembly process is the assembly department.
[0098] The joint evaluation unit can generate corresponding evaluation sub-tasks for each evaluation department, and each evaluation sub-task includes the product parameter to be evaluated in the sub-task. For example, the evaluation sub-task of the procurement department can include the parameters of the products to be purchased, the evaluation sub-tasks of each component production department can include the product parameters to be produced by the department, and the evaluation sub-tasks of the assembly department can include the assembly product parameters to be achieved, including assembly quality parameters and assembly deadline parameters.
[0099] The joint evaluation unit can send the evaluation sub-tasks to the corresponding evaluation departments for evaluation to obtain evaluation results of each department. Each department can use knowledge graph, feature matching, or other methods for evaluation, which is not limited here. The evaluation result of each department includes information of whether the production parameters are feasible and information of the construction period.
[0100] If it is determined that the production parameters in the evaluation results of each department are feasible, the construction period of the inquiry clue is determined based on the construction period information in the evaluation results of each department, and then the evaluation result is obtained. On the one hand, if the determined construction period can meet the customer's expected construction period requirement in the inquiry clue, it can be determined that the evaluation is passed. On the other hand, if the determined construction period cannot meet the customer's expected construction period requirement in the inquiry clue, it can be determined that the evaluation is not passed.
[0101] In some embodiments of the present application, each evaluation sub-task can further include the expected construction period and the expected cost of the corresponding process of the department.
[0102] Figure 6 is a flowchart of a method for evaluating the evaluation sub-tasks by the evaluation departments according to an embodiment of the present application. As shown in Figure 6 the method includes the following steps:
[0103] In step S601, the production capacity information, the current production situation and the planned production situation of the evaluation party are obtained.
[0104] In step S602, a pre-trained evaluation algorithm is called, the product parameters to be evaluated, the expected construction period and the expected cost of each sub-task are taken as inputs, and the production capacity information, the current production situation and the planned production situation are taken as constraint conditions to obtain the evaluation result of each sub-task.
[0105] In some embodiments of the present application, when evaluating the evaluation sub-tasks, each evaluation department can first obtain the production capacity information, the current production situation and the planned production situation of the evaluation party. Then, a pre-trained evaluation algorithm is called, the product parameters to be evaluated, the expected construction period and the expected cost of each sub-task are taken as inputs, and the production capacity information, the current production situation and the planned production situation are taken as constraint conditions to obtain the evaluation result of each sub-task.
[0106] The evaluation algorithm can be trained using the historical production data of the evaluation party. For example, the historical order data of the evaluation party can be obtained, the product parameters in the historical order data are taken as inputs, and the real production capacity information, the current production situation and the planned production situation when each historical order is executed are taken as basic constraint conditions. The feasible production capacity information, the current production situation and the planned production situation combination that meets different construction period requirements and / or different cost requirements are trained to obtain the trained evaluation algorithm.
[0107] In the evaluation of each subtask using the evaluation algorithm, the parameters to be evaluated of each subtask, the expected duration and the expected cost can be input, and the production capacity information, the current production situation and the planned production situation of the evaluation party are input as constraint conditions. If the evaluation algorithm judges that the production capacity information, the current production situation and the planned production situation of the evaluation party are within the feasible combination range, it can be determined that the evaluation is passed. Otherwise, the evaluation is not passed.
[0108] Figure 7 is a flowchart of a method for determining the duration of an inquiry clue based on the duration information in the evaluation results of each department provided by the embodiments of the present application. As shown in Figure 7 , the method comprises the following steps:
[0109] In step S701, in response to determining that the durations in at least two evaluation results conflict, a pre-trained path optimization algorithm is called to optimize each process again with the duration conflict information as a constraint condition to obtain at least one updated production plan.
[0110] In step S702, based on the updated production plan, an updated evaluation subtask is determined, and a pre-trained evaluation algorithm is called to evaluate the updated evaluation subtask to obtain an updated evaluation result.
[0111] In step S703, the duration of the inquiry clue is determined based on at least the updated evaluation result.
[0112] In some embodiments of the present application, when determining the duration of the inquiry clue based on the duration information in the evaluation results of each department, it can be first determined whether the durations in the evaluation results of each department conflict. If it is determined that the durations in at least two evaluation results conflict, a pre-trained path optimization algorithm can be called to optimize each process again with the duration conflict information as a constraint condition to obtain at least one updated production plan.
[0113] Next, based on the updated production plan, an updated evaluation subtask is determined, and a pre-trained evaluation algorithm is called to evaluate the updated evaluation subtask to obtain an updated evaluation result, and the duration of the inquiry clue is determined based on the updated evaluation result. If there is still a duration conflict problem in the updated evaluation result, it can be determined that the evaluation is not passed.
[0114] The technical solution of the embodiments of the present application can divide the evaluation of inquiry clues into a salesman independent evaluation stage and a joint evaluation stage of each department, wherein the independent evaluation results of the salesman need to be screened twice, and some evaluation parameters that are not well grasped are automatically included in the joint evaluation category. The joint evaluation department is automatically matched by the production plan obtained by path optimization of the process corresponding to the product parameters, reducing manual operation and improving evaluation efficiency.
[0115] All the optional technical solutions described above can be combined to form optional embodiments of the present application, which will not be described one by one here.
[0116] The following is an embodiment of the device of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.
[0117] Figure 8 is a schematic diagram of an inquiry clue evaluation device provided by an embodiment of the present application. As shown in Figure 8 , the device comprises:
[0118] The feature extraction module 801 is configured to perform feature extraction on the inquiry clue to obtain a feature set of the inquiry clue, the feature set comprising at least one product parameter; wherein each product parameter comprises at least parameter information and a parameter type.
[0119] The pre-evaluation module 802 is configured to generate a pre-evaluation task based on the feature set and send the pre-evaluation task to the salesperson evaluation unit.
[0120] The acquisition module 803 is configured to, in response to receiving a pre-evaluation pass message, acquire a salesperson identifier in the pre-evaluation pass message and acquire salesperson feature information from the salesperson information management unit based on the salesperson identifier; wherein the salesperson feature information comprises the grades of the salesperson corresponding to different parameter types.
[0121] The joint evaluation module 804 is configured to determine an effective pre-evaluation parameter in the pre-evaluation pass message based on the salesperson feature information, the effective pre-evaluation parameter being a product parameter evaluated independently by the salesperson.
[0122] The joint evaluation module 804 is further configured to determine other product parameters in the product parameters except the effective pre-evaluation parameter as joint evaluation parameters.
[0123] The joint evaluation module 804 is further configured to determine a joint evaluation task based on at least the joint evaluation parameters and send the joint evaluation task to the joint evaluation unit, so that the joint evaluation unit completes the evaluation of the inquiry clue.
[0124] According to the technical solutions provided by the embodiments of the present application, by extracting the feature set of the inquiry clue, generating and distributing the pre-evaluation task based on the feature set, acquiring the salesperson identifier under the condition of receiving the pre-evaluation pass message, acquiring the salesperson feature information according to the salesperson identifier, determining the effective pre-evaluation parameter in the pre-evaluation pass message by using the salesperson feature information, and then obtaining the joint evaluation parameters in the feature set except the effective pre-evaluation parameter, generating and distributing the joint evaluation task based on the joint evaluation parameters, so that the joint evaluation unit completes the evaluation of the inquiry clue, effectively combining the independent evaluation of the salesperson and the joint evaluation of each department, and improving the inquiry clue evaluation efficiency.
[0125] In some embodiments, the feature set further comprises at least one of order amount information, customer identification information, and product identification information; the salesperson evaluation unit processes the pre-evaluation task in the following manner: in response to determining that the feature set comprises the customer identification information, determining the target salesperson based on the customer identification information; in response to determining that the feature set does not comprise the customer identification information, and comprises at least one of the order amount information and the product identification information, determining the target salesperson based on a preset mapping relationship; wherein the preset mapping relationship comprises a mapping relationship between order amount, product identification, and salesperson; sending the pre-evaluation task to the target salesperson, and obtaining a pre-evaluation result of the inquiry clue by the target salesperson.
[0126] In some embodiments, the pre-evaluation passes through a message comprising evaluated product parameter information, the evaluated product parameter being at least one product parameter in the feature set; determining valid pre-evaluation parameters in the pre-evaluation pass message based on the salesperson feature information comprises: obtaining a target product type of each product parameter in the evaluated product parameter information; querying the salesperson's level corresponding to each target product type in the salesperson feature information; determining a product parameter corresponding to a target product type whose salesperson level is greater than a preset level threshold as a valid pre-evaluation parameter.
[0127] In some embodiments, determining the joint evaluation task based on the joint evaluation parameters comprises: obtaining the parameter type of each product parameter in the joint evaluation parameters; determining at least one evaluation department according to the parameter type; determining the joint evaluation task, the joint evaluation task at least comprising the determined evaluation departments and the feature set.
[0128] In some embodiments, the joint evaluation unit executes the joint evaluation task in the following manner: determining the process corresponding to each product parameter in the joint evaluation parameters; optimizing each process using a pre-trained path optimization algorithm to obtain at least one production scheme; determining the evaluation department corresponding to each process in the production scheme to obtain evaluation sub-tasks of each evaluation department; each evaluation sub-task comprising the to-be-evaluated product parameter of the sub-task; sending the evaluation sub-tasks to the corresponding evaluation departments for evaluation to obtain evaluation results of each department; wherein each department's evaluation result comprises production parameter feasibility information and duration information; in response to determining that the production parameters in each department's evaluation result are all feasible, determining the duration of the inquiry clue based on the duration information in each department's evaluation result, and generating the evaluation result.
[0129] In some embodiments, each evaluation subtask further comprises an expected duration and an expected cost of a corresponding process of the department; each evaluation department evaluates the evaluation subtask in the following manner: obtaining production capacity information, current production situation and planned production situation of the evaluation department; calling the pre-trained evaluation algorithm, taking the to-be-evaluated product parameters, the expected duration and the expected cost of each subtask as input, and taking the production capacity information, the current production situation and the planned production situation as constraint conditions, to obtain the evaluation result of each subtask.
[0130] In some embodiments, the duration of the inquiry clue is determined based on the duration information in the evaluation results of each department, comprising: in response to determining that the durations in at least two evaluation results conflict, calling a pre-trained path optimization algorithm to optimize each process again with the duration conflict information as a constraint condition to obtain at least one updated production scheme; determining updated evaluation subtasks based on the updated production scheme, calling the pre-trained evaluation algorithm to evaluate the updated evaluation subtasks to obtain updated evaluation results; and determining the duration of the inquiry clue based on at least the updated evaluation results.
[0131] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
[0132] Figure 9 is a schematic diagram of an electronic device provided by an embodiment of the present application. As shown in Figure 9 The electronic device 9 of this embodiment comprises a processor 901, a memory 902, and a computer program 903 stored in the memory 902 and executable on the processor 901. The processor 901 implements the steps in each of the above method embodiments when executing the computer program 903. Alternatively, the processor 901 implements the functions of each module / unit in each of the above device embodiments when executing the computer program 903.
[0133] The electronic device 9 can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The electronic device 9 can include but is not limited to the processor 901 and the memory 902. Those skilled in the art can understand that Figure 9 The electronic device 9 is merely an example and does not constitute a limitation on the electronic device 9, which can include more or fewer components or different components than those shown.
[0134] The processor 901 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
[0135] The memory 902 can be an internal storage unit of the electronic device 9, for example, a hard disk or a memory of the electronic device 9. The memory 902 can also be an external storage device of the electronic device 9, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 9. The memory 902 can also include both the internal storage unit and the external storage device of the electronic device 9. The memory 902 is used to store computer programs and other programs and data required by the electronic device.
[0136] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.
[0137] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.
[0138] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.
Claims
1. A method for evaluating inquiry leads, characterized in that: include: Extracting features from the inquiry clue to obtain a feature set of the inquiry clue; the feature set includes at least one product parameter, and each product parameter includes at least parameter information and parameter type; generating a pre-assessment task based on the feature set, and sending the pre-assessment task to a salesperson assessment unit; In response to receiving a pre-assessment pass message, obtaining a salesperson identifier in the pre-assessment pass message, and obtaining salesperson characteristic information from a salesperson information management unit based on the salesperson identifier; wherein the salesperson characteristic information includes the salesperson's level corresponding to different parameter types; Determining valid pre-assessment parameters in the pre-assessment pass message based on the salesperson characteristic information, wherein the valid pre-assessment parameters are product parameters independently assessed by the salesperson; Determining other product parameters among the product parameters except the valid pre-assessment parameters as joint assessment parameters; A joint evaluation task is determined based at least on the joint evaluation parameter, and the joint evaluation task is sent to a joint evaluation unit, so that the joint evaluation unit completes the inquiry clue evaluation.
2. The method according to claim 1, characterized in that The feature set further includes at least one of order amount information, customer identification information, and product identification information; The salesperson evaluation unit processes the pre-evaluation task in the following manner: In response to determining that the feature set includes customer identification information, determining a target salesperson based on the customer identification information; In response to determining that the feature set does not include customer identification information and includes at least one of order amount information and product identification information, determining a target salesperson based on a preset mapping relationship; wherein the preset mapping relationship includes a mapping relationship between order amount, product identification, and salesperson; The pre-evaluation task is sent to the target salesperson, and a pre-evaluation result of the target salesperson on the inquiry clue is obtained.
3. The method according to claim 1, characterized in that The pre-assessment pass message includes information on evaluated product parameters, where the evaluated product parameter is at least one product parameter in the feature set; Determining valid pre-assessment parameters in the pre-assessment pass message based on the salesperson characteristic information includes: Obtaining a target product type for each product parameter in the evaluated product parameter information; Querying the salesperson's level corresponding to each target product type in the salesperson's characteristic information; The product parameters corresponding to the target product type whose salesperson level is greater than the preset level threshold are determined to be the effective pre-evaluation parameters.
4. The method according to claim 1, wherein Determining a joint evaluation task based on the joint evaluation parameters includes: Get the parameter type of each product parameter in the joint evaluation parameters; determining at least one evaluation department according to the parameter type; A joint evaluation task is determined, where the joint evaluation task at least includes the determined evaluation departments and the feature set.
5. The method according to claim 1, wherein The joint assessment unit performs the joint assessment task in the following manner: Determining the process corresponding to each product parameter in the joint evaluation parameter; Use the pre-trained path optimization algorithm to optimize each process and obtain at least one production plan; Determine the evaluation department corresponding to each process in the production plan and obtain the evaluation subtasks of each evaluation department; Each evaluation subtask includes the parameters of the product to be evaluated in this subtask; Send the evaluation subtask to the corresponding evaluation department for evaluation, and obtain the evaluation results of each department; wherein the evaluation results of each department include feasibility information of production parameters and construction period information; In response to determining that the production parameters in the evaluation results of each department are all feasible, the construction period of the inquiry clue is determined based on the construction period information in the evaluation results of each department, and an evaluation result is generated.
6. The method according to claim 5, characterized in that Each assessment subtask also includes the expected duration and expected cost of the corresponding process in this department; Each assessment department shall assess the assessment subtasks in the following manner: Obtain the production capacity information, current production status and planned production status of the assessee; The pre-trained evaluation algorithm is called, and the product parameters, expected duration and expected cost of each subtask to be evaluated are used as inputs, and the production capacity information, the current production situation and the planned production situation are used as constraints to obtain the evaluation results of each subtask.
7. The method according to claim 6, characterized in that Determine the construction period of the inquiry clue based on the construction period information in the evaluation results of each department, including: In response to determining a construction period conflict in at least two evaluation results, calling the pre-trained path optimization algorithm, optimizing each process again with the construction period conflict information as a constraint condition, and obtaining at least one updated production plan; Determining an updated evaluation subtask based on the updated production plan, calling the pre-trained evaluation algorithm to evaluate the updated evaluation subtask, and obtaining an updated evaluation result; The duration of the inquiry clue is determined based at least on the updated evaluation result.
8. A price inquiry clue evaluation device, characterized in that: include: a feature extraction module configured to extract features from the inquiry clue to obtain a feature set of the inquiry clue, wherein the feature set includes at least one product parameter; wherein each product parameter includes at least parameter information and parameter type; a pre-assessment module configured to generate a pre-assessment task based on the feature set and send the pre-assessment task to a salesperson assessment unit; an acquisition module configured to, in response to receiving a pre-assessment pass message, acquire a salesperson identifier in the pre-assessment pass message, and acquire salesperson characteristic information from a salesperson information management unit based on the salesperson identifier; wherein the salesperson characteristic information includes the salesperson's level corresponding to different parameter types; a joint evaluation module configured to determine valid pre-evaluation parameters in the pre-evaluation pass message based on the salesperson characteristic information, wherein the valid pre-evaluation parameters are product parameters that have passed the independent evaluation of the salesperson; The joint evaluation module is further configured to determine other product parameters among the product parameters except the valid pre-evaluation parameters as joint evaluation parameters; The joint evaluation module is further configured to determine a joint evaluation task based at least on the joint evaluation parameters, and send the joint evaluation task to the joint evaluation unit, so that the joint evaluation unit completes the inquiry clue evaluation.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.