Business processing method and device, equipment and storage medium

Abstract

The invention provides a business processing method and device, equipment and a storage medium. Relates to the technical field of supplier management. The method comprises the following steps: receiving a service processing request sent by a user terminal; according to the business processing request, obtaining a personnel information table of the target supplier, the personnel information table comprising a plurality of personnel identifiers and basic information corresponding to each personnel identifier; performing text processing on the personnel information to obtain a plurality of presence personnel identifiers in a preset duration interval; performing feature extraction processing on the basic information of each presence personnel identifier to obtain feature values of a plurality of feature attributes of the target supplier; and adjusting the evaluation weight of the target supplier according to the plurality of on-site personnel identifiers and the feature values of the plurality of feature attributes to obtain the attribute weight of the target supplier, and sending the attribute weight to the user terminal. According to the method, the accuracy of determining the attribute weight of the supplier is improved.

Description

Technical Field

[0001] The present application relates to the technical field of supplier management, and in particular to a business processing method, device, equipment and storage medium. Background Art

[0002] Enterprises usually cooperate with multiple suppliers. Suppliers can provide staff for the corresponding positions of a certain object, and the performance of the staff hired by each supplier is scored to determine the attribute weight of the supplier.

[0003] In the prior art, managers can obtain the on-the-job information of multiple on-the-job personnel corresponding to the supplier, evaluate the on-the-job performance of each on-the-job personnel based on the on-the-job information, obtain the subjective weight of each on-the-job personnel, and determine the attribute weight of the supplier based on the subjective weight of each on-the-job personnel. However, the evaluation of managers is highly subjective, which makes the accuracy of the attribute weight of the supplier low. Summary of the invention

[0004] The present application provides a business processing method, apparatus, device and storage medium to solve the technical problem of low accuracy of supplier attribute weights in the prior art.

[0005] In a first aspect, the present application provides a business processing method, including:

[0006] Receiving a service processing request sent by a user terminal, wherein the service processing request is used to request an attribute weight of a target supplier within a preset time interval;

[0007] According to the business processing request, obtaining a personnel information table of the target supplier, the personnel information table including a plurality of personnel identifiers and basic information corresponding to each personnel identifier;

[0008] Performing text processing on the personnel information to obtain multiple identifications of persons present within the preset time interval;

[0009] Performing feature extraction processing on the basic information of each on-site person identification to obtain feature values ​​of multiple feature attributes of the target supplier, wherein the feature attributes include a stability attribute, an experience attribute, and an efficiency attribute of the target supplier;

[0010] The evaluation weight of the target supplier is adjusted according to the multiple on-site person identifications and the characteristic values ​​of the multiple characteristic attributes to obtain the attribute weight of the target supplier, and the attribute weight is sent to the user terminal.

[0011] In a possible design, feature extraction processing is performed on the basic information of each on-site person identification to obtain feature values ​​of multiple feature attributes of the target supplier, including:

[0012] Acquire a preset feature model, wherein the preset feature model includes a preset processing model corresponding to each feature attribute;

[0013] For any characteristic attribute, determining a preset processing model corresponding to the characteristic attribute;

[0014] By using the preset processing model corresponding to the characteristic attribute, data extraction processing is performed in the basic information of each on-site person identification to obtain the characteristic value corresponding to the characteristic attribute.

[0015] In a possible design, the characteristic attribute is the stable attribute; data extraction processing is performed in the basic information of each on-site person identification to obtain a characteristic value corresponding to the characteristic attribute, including:

[0016] Determine the number of absences within the preset time interval from the basic information of each on-site personnel identification;

[0017] The ratio of the number of departures to the number of employees is determined as the departure rate of the target supplier;

[0018] A characteristic value corresponding to the stability attribute is determined according to the departure rate.

[0019] In a possible design, the characteristic attribute is the efficiency attribute; data extraction processing is performed in the basic information of each on-site person identification to obtain a characteristic value corresponding to the characteristic attribute, including:

[0020] Obtaining the object position of each on-site person identification in the corresponding service object from the basic information of each on-site person identification;

[0021] For any present person identification, determining a first attribute value corresponding to the present person identification according to the service object and the object position of the present person identification, wherein the first attribute value is used to indicate the efficiency attribute value of the present person corresponding to the present person identification;

[0022] The first attribute value of each on-site person identification is weighted averaged to obtain a characteristic value corresponding to the efficiency attribute.

[0023] In a possible design, determining the first attribute value corresponding to the on-site person identification according to the service object and the object position of the on-site person identification includes:

[0024] Obtaining the position weight of each preset position corresponding to the service object;

[0025] Determine the position weight of the target position among the position weights of each preset position;

[0026] The product of the position weight and the basic attribute value of the present person is determined as the first attribute value.

[0027] In a possible design, the characteristic attribute is the experience attribute; data extraction processing is performed in the basic information of each on-site person identification to obtain a characteristic value corresponding to the characteristic attribute, including:

[0028] Determine the length of experience of each on-site personnel identification in the corresponding target position in the basic information of each on-site personnel identification;

[0029] Determine a second attribute value corresponding to the experience duration of each present person identification, where the second attribute value is used to indicate the experience of the present person in the position of the object;

[0030] The average value of the second attribute value corresponding to each of the on-site person identifications is determined as the feature value corresponding to the empirical attribute.

[0031] In a possible design, the evaluation weight of the target supplier is adjusted according to the multiple on-site person identifiers and the characteristic values ​​of the multiple characteristic attributes to obtain the attribute weight of the target supplier, including:

[0032] Determining personnel data of the target supplier according to the multiple on-site personnel identifications;

[0033] According to the number of personnel, obtaining a characteristic value corresponding to the scale attribute of the target supplier;

[0034] Determining an adjustment coefficient of the target supplier according to the characteristic value corresponding to the scale attribute and the characteristic values ​​of the multiple characteristic attributes;

[0035] The product of the adjustment coefficient and the evaluation weight is determined as the attribute weight.

[0036] In a possible design, determining the adjustment coefficient of the target supplier according to the characteristic value corresponding to the scale attribute and the characteristic values ​​of the multiple characteristic attributes includes:

[0037] Obtaining attribute adjustment formula or adjustment coefficient relationship table;

[0038] The adjustment coefficient is obtained by performing data processing on the characteristic values ​​of the multiple characteristic attributes and the characteristic value corresponding to the scale attribute through the attribute adjustment formula, or by performing query processing on the adjustment coefficient relationship table through the characteristic value corresponding to the scale attribute and the characteristic values ​​of the multiple characteristic attributes.

[0039] In a possible design, the personnel information is subjected to text processing to obtain multiple identifications of persons present within the preset time interval, including:

[0040] Performing text extraction processing on the personnel information to determine the service time interval corresponding to each personnel identification;

[0041] The multiple on-site person identifiers are determined according to the service time interval corresponding to each person identifier.

[0042] In a second aspect, an embodiment of the present application provides a business processing device, including a receiving module, an acquisition module, a text processing module, a feature extraction module, an adjustment processing module and a sending model:

[0043] The receiving module is used to receive a service processing request sent by a user terminal, wherein the service processing request is used to request an attribute weight of a target supplier within a preset time interval;

[0044] The acquisition module is used to acquire the personnel information table of the target supplier according to the business processing request, wherein the personnel information table includes multiple personnel identifiers and basic information corresponding to each personnel identifier;

[0045] The text processing module is used to perform text processing on the personnel information to obtain multiple on-site personnel identifications within the preset time interval;

[0046] The feature extraction module is used to perform feature extraction processing on the basic information of each on-site person identification to obtain feature values ​​of multiple feature attributes of the target supplier, wherein the feature attributes include a stability attribute, an experience attribute, and an efficiency attribute of the target supplier;

[0047] The adjustment processing module is used to adjust the evaluation weight of the target supplier according to the multiple on-site personnel identifications and the characteristic values ​​of the multiple characteristic attributes to obtain the attribute weight of the target supplier;

[0048] The sending module is used to send the attribute weight to the user terminal.

[0049] In a possible design, the feature extraction module is specifically used for:

[0050] Acquire a preset feature model, wherein the preset feature model includes a preset processing model corresponding to each feature attribute;

[0051] For any characteristic attribute, determining a preset processing model corresponding to the characteristic attribute;

[0052] By using the preset processing model corresponding to the characteristic attribute, data extraction processing is performed in the basic information of each on-site person identification to obtain the characteristic value corresponding to the characteristic attribute.

[0053] In a possible design, the characteristic attribute is the stable attribute; and the characteristic extraction module is specifically used for:

[0054] Determine the number of absences within the preset time interval from the basic information of each on-site personnel identification;

[0055] The ratio of the number of departures to the number of employees is determined as the departure rate of the target supplier;

[0056] A characteristic value corresponding to the stability attribute is determined according to the departure rate.

[0057] In a possible design, the characteristic attribute is the efficiency attribute; and the characteristic extraction module is specifically used for:

[0058] Obtaining the object position of each on-site person identification in the corresponding service object from the basic information of each on-site person identification;

[0059] For any present person identification, determining a first attribute value corresponding to the present person identification according to the service object and the object position of the present person identification, wherein the first attribute value is used to indicate the efficiency attribute value of the present person corresponding to the present person identification;

[0060] The first attribute value of each on-site person identification is weighted averaged to obtain a characteristic value corresponding to the efficiency attribute.

[0061] In a possible design, the feature extraction module is specifically used for:

[0062] Obtaining the position weight of each preset position corresponding to the service object;

[0063] Determine the position weight of the target position among the position weights of each preset position;

[0064] The product of the position weight and the basic attribute value of the present person is determined as the first attribute value.

[0065] In a possible design, the characteristic attribute is the empirical attribute; and the characteristic extraction module is specifically used for:

[0066] Determine the length of experience of each on-site personnel identification in the corresponding target position in the basic information of each on-site personnel identification;

[0067] Determine a second attribute value corresponding to the experience duration of each present person identification, where the second attribute value is used to indicate the experience of the present person in the position of the object;

[0068] The average value of the second attribute value corresponding to each of the on-site person identifications is determined as the feature value corresponding to the empirical attribute.

[0069] In a possible design, the adjustment processing module is specifically used to:

[0070] Determining personnel data of the target supplier according to the multiple on-site personnel identifications;

[0071] According to the number of personnel, obtaining a characteristic value corresponding to the scale attribute of the target supplier;

[0072] Determining an adjustment coefficient of the target supplier according to the characteristic value corresponding to the scale attribute and the characteristic values ​​of the multiple characteristic attributes;

[0073] The product of the adjustment coefficient and the evaluation weight is determined as the attribute weight.

[0074] In a possible design, the adjustment processing module is specifically used to:

[0075] Obtaining attribute adjustment formula or adjustment coefficient relationship table;

[0076] The adjustment coefficient is obtained by performing data processing on the characteristic values ​​of the multiple characteristic attributes and the characteristic value corresponding to the scale attribute through the attribute adjustment formula, or by performing query processing on the adjustment coefficient relationship table through the characteristic value corresponding to the scale attribute and the characteristic values ​​of the multiple characteristic attributes.

[0077] In a possible design, the text processing module is specifically used for:

[0078] Performing text extraction processing on the personnel information to determine the service time interval corresponding to each personnel identification;

[0079] The multiple on-site person identifiers are determined according to the service time interval corresponding to each person identifier.

[0080] In a third aspect, an embodiment of the present application provides an electronic device, comprising: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, so that the at least one processor executes the business processing method described in the first aspect and various possible designs of the first aspect.

[0081] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer execution instructions are stored. When a processor executes the computer execution instructions, the business processing method described in the first aspect and various possible designs of the first aspect is implemented.

[0082] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the business processing method described in the first aspect and various possible designs of the first aspect.

[0083] The business processing method, apparatus, device and storage medium provided in the present application can obtain the personnel information table of the target supplier after receiving the business processing request, determine multiple on-site personnel identifications within a preset time interval in the personnel information table, perform feature extraction processing on the basic information of each on-site personnel identification, obtain feature values ​​of multiple feature attributes of the target supplier, adjust the evaluation weights through multiple feature attributes, and obtain the attribute weights of the target supplier, which can improve the accuracy of determining the attribute weights of the suppliers. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0085] Figure 1 A schematic diagram of an application scenario provided in an embodiment of the present application;

[0086] Figure 2 A flowchart of a business processing method improved by an embodiment of the present application;

[0087] Figure 3 A schematic diagram of another business process flow provided in an embodiment of the present application;

[0088] Figure 4 A schematic diagram of the architecture of a business processing method provided in an embodiment of the present application;

[0089] Figure 5 A schematic diagram of the structure of a service processing device provided in an embodiment of the present application;

[0090] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0091] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0092] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0093] Figure 1 A schematic diagram of an application scenario provided by an embodiment of the present application. Figure 1 , including a user device 101 and a server 102.

[0094] The user device 101 may be a mobile phone, a laptop computer, a desktop computer, a tablet computer, a smart wearable device, etc. The user device 101 may access the business system through application software, a browser, or a small program. The user may select a target supplier in the business system through the user device 101, generate a business processing request corresponding to the target supplier, and the user device 101 may send the business processing request.

[0095] The server 102 may receive the business processing request, and obtain the personnel information table of the target supplier according to the business processing request, wherein the personnel information table may include multiple personnel identifications and basic information corresponding to each personnel identification. The personnel information may be subjected to text processing to obtain multiple on-site personnel identifications within a preset time interval; the basic information of each on-site personnel identification may be subjected to feature extraction processing to obtain feature values ​​of multiple feature attributes of the target supplier, wherein the feature attributes may include the stability attribute, experience attribute, and efficiency attribute of the target supplier.

[0096] The server 102 also adjusts the evaluation weight of the target supplier according to the multiple on-site person identifications and the characteristic values ​​of the multiple characteristic attributes, obtains the attribute weight of the target supplier, and sends the attribute weight to the user terminal 101 .

[0097] In the prior art, managers can obtain the basic information of multiple on-site personnel corresponding to the supplier, evaluate the on-the-job performance of each on-site personnel based on the basic information, obtain the subjective weight of each on-site personnel, and determine the attribute weight of the supplier based on the subjective weight of each on-site personnel. However, the evaluation of managers is highly subjective, which makes the accuracy of the attribute weight of the supplier low.

[0098] The improved business processing method of the embodiment of the present application can obtain the personnel information table of the target supplier after receiving the business processing request, determine multiple on-site personnel identifications within a preset time interval in the personnel information table, perform feature extraction processing on the basic information of each on-site personnel identification, and obtain feature values ​​of multiple feature attributes of the target supplier. The evaluation weights are adjusted through the multiple feature attributes to obtain the attribute weights of the target supplier, which can improve the accuracy of determining the attribute weights of the suppliers.

[0099] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0100] Figure 2 A flowchart of a business processing method for improving the embodiment of the present application. Figure 2 , the method may include:

[0101] S201: Receive a service processing request sent by a user terminal.

[0102] The execution subject of the embodiment of the present application may be a server, or a service processing device arranged in the server. The service processing device may be implemented by software, or by a combination of software and hardware.

[0103] The business processing request can be used to request the attribute weights of the target supplier within a preset time interval.

[0104] The attribute weight of each supplier can be periodically determined, the attribute weight of each supplier can be sorted, the sorting status of multiple suppliers can be determined, or the supplier whose attribute weight is less than the preset weight can be given an alarm.

[0105] Since the execution process of each supplier determining the attribute weight through the business processing request is the same, the embodiment of the present application is described with respect to any one supplier.

[0106] The preset time interval may be the time interval from the last service processing request to the current moment, for example, the preset time interval may be a quarter or a year.

[0107] S202: According to the business processing request, obtain the personnel information table of the target supplier.

[0108] The target supplier may provide employees of various positions for different objects, and a personnel information table of the target supplier may be stored in a database or memory. The personnel information table may include multiple personnel IDs and basic information corresponding to each personnel ID.

[0109] For any personnel identification, the basic information corresponding to the personnel identification may include the service object, object position, time of joining, time of leaving, years of work, graduation school, age, etc. of the staff member corresponding to the personnel identification.

[0110] S203: Perform text processing on the personnel information to obtain multiple identifications of the persons present within a preset time interval.

[0111] Text extraction processing can be performed on the personnel information to determine the service time interval corresponding to each personnel identification; based on the service time interval corresponding to each personnel identification, multiple on-site personnel identifications within the preset time interval are determined.

[0112] The joining time can be extracted from the personnel information. If there is a leaving time, the leaving time is extracted, and the joining time and the leaving time are determined as the service time interval. If there is no leaving time, the joining time and the current time are determined as the service time interval.

[0113] If the service duration interval and the preset duration interval have an intersection, the person identification corresponding to the service duration interval is determined as the on-site person identification.

[0114] The staff member corresponding to the on-site personnel identification includes any of the following: the staff member has served the entire preset time interval; the staff member joined the job after the start time of the preset time interval and has served until the current time; the staff member resigned within the preset time interval.

[0115] S204: Perform feature extraction processing on the basic information of each on-site person identification to obtain feature values ​​of multiple feature attributes of the target supplier.

[0116] Characteristic attributes may include stability attributes, experience attributes, and efficiency attributes of the target supplier.

[0117] The stability attribute can be used to indicate the stability of the target supplier in providing staff. The stronger the stability, the larger the characteristic value of the stability attribute.

[0118] The experience attribute is used to indicate the work experience of the staff provided by the target supplier in the industry. The more experience the staff has, the greater the characteristic value of the experience attribute.

[0119] The efficiency attribute is used to indicate the actual work performance of the workers provided by the target supplier in the service object.

[0120] S205. Adjust the evaluation weight of the target supplier according to the multiple on-site personnel identifications and the characteristic values ​​of the multiple characteristic attributes to obtain the attribute weight of the target supplier.

[0121] The subjective weight corresponding to each present person can be obtained, and the subjective weight of each present person is weighted averaged to obtain the evaluation weight of the target supplier.

[0122] The subjective weight may be a subjective evaluation of the manager of the service object of the present personnel.

[0123] S206: Send the attribute weight to the user terminal.

[0124] After receiving the attribute weight, the user terminal can display the attribute weight of the target supplier on the system. At the same time, if the attribute weight is lower than the preset weight, a prompt text can be generated. For example, the prompt text can be "Prompt: The attribute weight of supplier xx is lower than the preset weight."

[0125] The business processing method of the embodiment of the present application can obtain the personnel information table of the target supplier after receiving the business processing request, determine multiple on-site personnel identifications within a preset time interval in the personnel information table, perform feature extraction processing on the basic information of each on-site personnel identification, obtain the characteristic values ​​of multiple characteristic attributes of the target supplier, process the evaluation weights by the characteristic attributes and the number of personnel, obtain the attribute weights of the target supplier, and improve the accuracy of determining the attribute weights of the supplier. At the same time, the efficiency of determining the attribute weights of the supplier can be improved.

[0126] Figure 3 This is a flow chart of another business process provided by the embodiment of the present application. Figure 3 , the method may include:

[0127] S301: Receive a service processing request sent by a user terminal.

[0128] S302: According to the business processing request, obtain the personnel information table of the target supplier.

[0129] S303: Perform text processing on the personnel information to obtain multiple identifications of the persons present within a preset time interval.

[0130] The execution process of S301-S303 can refer to the execution process of S201-S203, which will not be repeated here.

[0131] S304: Obtain a preset feature model.

[0132] The preset feature model may include a preset processing model corresponding to each feature attribute, wherein the preset processing model may include a text extraction program and a data processing program.

[0133] The text extraction program can segment words through the maximum matching method, the maximum probability method or the shortest path method.

[0134] S305: For any characteristic attribute, determine a preset processing model corresponding to the characteristic attribute.

[0135] For example, assuming that there are three feature attributes, namely feature attribute 1, feature attribute 2 and feature attribute 3, it is assumed that feature attribute 1 corresponds to preset processing model 1, feature attribute 2 corresponds to preset processing model 2 and feature attribute 3 corresponds to preset processing model 3. Assuming that the feature attribute is feature attribute 1, it can be determined that the preset processing model is preset processing model 1.

[0136] S306: Perform data extraction processing on the basic information of each person present through a preset processing model corresponding to the characteristic attribute to obtain a characteristic value corresponding to the characteristic attribute.

[0137] The characteristic attributes may include stability attributes, efficiency attributes, and experience attributes, and the execution process of determining the characteristic value of each characteristic attribute may be as follows:

[0138] (1) If the characteristic attribute is a stable attribute, the number of off-duty employees within a preset time interval can be determined from the basic information of each on-site personnel identification, and the ratio of the number of off-duty employees to the number of employees can be used as the off-duty rate of the target supplier. The characteristic value corresponding to the stable attribute can be determined based on the off-duty rate.

[0139] In some embodiments, the difference between the preset value and the departure rate may be determined as the characteristic value corresponding to the stable attribute. For example, the preset value may be 1.

[0140] In some embodiments, the inverse of the departure rate may be determined as the characteristic value corresponding to the stability attribute.

[0141] For example, assuming there are 5 on-site personnel identifications, namely on-site personnel 1-5, and assuming on-site personnel 2 resigns within the preset period of time, the departure rate can be determined to be 20%, and assuming the preset value is 1, the characteristic value of the stable attribute can be determined to be 80%.

[0142] (2) If the characteristic attribute is an efficiency attribute, then in the basic information of each on-site person identification, the object position of each on-site person identification in the corresponding service object is obtained; for any on-site person identification, the first attribute value corresponding to the on-site person identification is determined according to the service object and the object position of the on-site person identification; the first attribute value of each on-site person identification is weighted averaged to obtain the characteristic value corresponding to the efficiency attribute.

[0143] The first attribute value may be used to indicate the efficiency attribute value of the present person corresponding to the present person identification.

[0144] Furthermore, the position weight of each preset position corresponding to the service object can be obtained; among the position weights of each preset position, the position weight of the object position is determined; and the product of the position weight and the basic attribute value of the present person is determined as the first attribute value.

[0145] For example, suppose the preset positions can be person in charge, senior engineer, intermediate engineer and junior engineer. The weight of the person in charge is a, the weight of the senior engineer is b, the weight of the intermediate engineer is c, and the weight of the junior engineer is d. If the position of the present person is junior engineer, the weight of the position is d.

[0146] The basic attribute value may be a preset attribute value of the present person. Each present person has a corresponding basic attribute value, which can be determined by multiple historical service objects that the present person has served in the enterprise. If the present person has not served in the enterprise, the basic attribute value may be an initial attribute value. After each service object is completed, the corresponding preset attribute value can be obtained.

[0147] (3) If the characteristic attribute is an experience attribute, the length of experience of each on-site person identification in the corresponding object position can be determined in the basic information of each on-site person identification; the second attribute value corresponding to the length of experience of each on-site person identification can be determined; and the average of the second attribute values ​​corresponding to each on-site person identification can be determined as the characteristic value corresponding to the experience attribute.

[0148] The basic information may include at least one work experience of the corresponding staff member. The target work experience corresponding to the object position can be extracted from the at least one work experience through the text extraction program in the preset processing model, and the sum of the service time of the target work experience can be determined as the experience time.

[0149] The experience attribute value corresponding to each experience interval may be obtained, and the experience attribute value of the experience interval corresponding to the experience duration may be determined as the second attribute value.

[0150] The second attribute value may be used to indicate the experience of the person present in the object position.

[0151] S307: Determine the personnel data of the target supplier according to the multiple on-site personnel identifications.

[0152] The number of personnel can be used to indicate the supply scale of the target supplier. The larger the supply scale, the greater the impact on the target supplier.

[0153] S308. Obtain a characteristic value corresponding to the scale attribute of the target supplier according to the number of employees.

[0154] The scale attribute value corresponding to each scale interval may be obtained, and the scale attribute value of the scale interval corresponding to the number of personnel may be determined as the second attribute value.

[0155] Each scale interval and the scale attribute value corresponding to each scale interval may be updated according to actual conditions.

[0156] S309: Determine the adjustment coefficient of the target supplier according to the characteristic value corresponding to the scale attribute and the characteristic values ​​of multiple characteristic attributes.

[0157] In some embodiments, an attribute adjustment formula may be obtained, and the attribute adjustment formula may be used to perform data processing on the characteristic values ​​of multiple characteristic attributes and the characteristic values ​​corresponding to the scale attribute to obtain an adjustment coefficient.

[0158] The property adjustment formula may further include at least one fixed parameter, and relevant technical personnel may adjust the fixed parameter.

[0159] It is worth noting that the eigenvalues ​​of each feature attribute and the eigenvalues ​​of the scale attribute are normalized to achieve a unified measurement.

[0160] In some embodiments, an adjustment coefficient relationship table may be obtained, and the adjustment coefficient may be obtained by querying the adjustment coefficient relationship table through the characteristic value corresponding to the scale attribute and the characteristic values ​​corresponding to multiple characteristic attributes.

[0161] The adjustment coefficient relationship table may include a plurality of scale intervals corresponding to the scale attribute and a plurality of feature intervals corresponding to each feature attribute.

[0162] For example, assuming that the adjustment coefficient relationship table includes scale attributes, efficiency attributes and stability attributes, assuming that the scale attribute corresponds to 2 scale intervals, namely scale interval 1 and scale interval 2, assuming that the efficiency attribute corresponds to 2 efficiency intervals, namely efficiency interval 1 and efficiency interval 2, assuming that the stability attribute corresponds to 2 stability intervals, namely stability interval 1 and stability interval 2, then the adjustment coefficient relationship table can be shown in Table 1.

[0163] Table 1

[0164]

[0165] S310: Determine the product of the adjustment coefficient and the evaluation weight as the attribute weight.

[0166] The adjustment coefficient and the evaluation weight have a unified measurement standard. For example, the adjustment coefficient is between 0-1 and the evaluation weight is also between 0-1.

[0167] S311. Send attribute weights to the user terminal.

[0168] The execution process of S311 can refer to the execution process of S206, which will not be repeated here.

[0169] The business processing method of the embodiment of the present application can determine multiple on-site personnel identifications of the target supplier after receiving a business processing request, and can perform feature extraction processing on the basic information of each on-site personnel identification to obtain feature values ​​of multiple feature attributes of the target supplier, and process the evaluation weights by the feature attributes and the number of personnel to obtain the attribute weights of the target supplier, which can improve the accuracy of determining the attribute weights of the supplier. At the same time, the basic information can be feature extracted by a preset feature model to improve the efficiency of determining the attribute weights of the supplier.

[0170] Figure 4 This is a schematic diagram of the architecture of a business processing method provided in an embodiment of the present application. Figure 4 After receiving the service processing request sent by the user terminal, the personnel information table of the target supplier can be obtained, and the personnel information can be subjected to text extraction processing to determine the service time interval corresponding to each personnel identification; according to the service time interval corresponding to each personnel identification, multiple on-site personnel identifications are determined. The basic information of each on-site personnel identification is subjected to feature extraction processing to obtain feature values ​​of multiple feature attributes of the target supplier, wherein the feature attributes can include the stability attribute, experience attribute, and efficiency attribute of the target supplier.

[0171] The personnel data of the target supplier can be determined based on multiple on-site personnel identifications; the characteristic value corresponding to the scale attribute of the target supplier can be obtained based on the number of personnel; the adjustment coefficient of the target supplier can be determined based on the characteristic value corresponding to the scale attribute and the characteristic values ​​of multiple characteristic attributes; the product of the adjustment coefficient and the evaluation weight is determined as the attribute weight, and the attribute weight is sent to the user terminal.

[0172] Figure 5 This is a schematic diagram of the structure of a service processing device provided in an embodiment of the present application. Figure 5 , the business processing device 10 may include a receiving module 11, an acquisition module 12, a text processing module 13, a feature extraction module 14, an adjustment processing module 15 and a sending model 16:

[0173] The receiving module 11 is used to receive a service processing request sent by a user terminal, where the service processing request is used to request the attribute weight of a target supplier within a preset time interval;

[0174] The acquisition module 12 is used to acquire the personnel information table of the target supplier according to the business processing request, where the personnel information table includes multiple personnel identifiers and basic information corresponding to each personnel identifier;

[0175] The text processing module 13 is used to perform text processing on the personnel information to obtain multiple on-site personnel identifications within a preset time interval;

[0176] The feature extraction module 14 is used to perform feature extraction processing on the basic information of each on-site person identification to obtain feature values ​​of multiple feature attributes of the target supplier, wherein the feature attributes include the stability attribute, experience attribute, and efficiency attribute of the target supplier;

[0177] The adjustment processing module 15 is used to adjust the evaluation weight of the target supplier according to the multiple on-site personnel identifications and the characteristic values ​​of the multiple characteristic attributes to obtain the attribute weight of the target supplier;

[0178] The sending module 16 is used to send the attribute weight to the user terminal.

[0179] The business processing device provided in the embodiment of the present application can execute the technical solution shown in the above method embodiment, and its implementation principle and beneficial effects are similar, which will not be repeated here.

[0180] In a possible design, the feature extraction module 14 is specifically used for:

[0181] Obtaining a preset feature model, the preset feature model including a preset processing model corresponding to each feature attribute;

[0182] For any characteristic attribute, determine a preset processing model corresponding to the characteristic attribute;

[0183] Through the preset processing model corresponding to the characteristic attribute, data extraction processing is performed in the basic information of each on-site person identification to obtain the characteristic value corresponding to the characteristic attribute.

[0184] In a possible design, the characteristic attribute is a stable attribute; the characteristic extraction module 14 is specifically used for:

[0185] In the basic information of each on-site personnel identification, determine the number of absences within the preset time interval;

[0186] The ratio of the number of departures to the number of employees is indeed the departure rate of the target supplier;

[0187] According to the departure rate, the characteristic value corresponding to the stable attribute is determined.

[0188] In a possible design, the characteristic attribute is an efficiency attribute; the characteristic extraction module 14 is specifically used for:

[0189] From the basic information of each on-site person identification, obtain the object position of each on-site person identification in the corresponding service object;

[0190] For any on-site person identification, determine a first attribute value corresponding to the on-site person identification according to the service object and the object position of the on-site person identification, where the first attribute value is used to indicate the efficiency attribute value of the on-site person corresponding to the on-site person identification;

[0191] The first attribute value of each on-site person identification is weighted averaged to obtain the characteristic value corresponding to the efficiency attribute.

[0192] In a possible design, the feature extraction module 14 is specifically used for:

[0193] Get the position weight of each preset position corresponding to the service object;

[0194] In the position weight of each preset position, determine the position weight of the target position;

[0195] The product of the position weight and the basic attribute value of the present person is determined as the first attribute value.

[0196] In a possible design, the feature attribute is an empirical attribute; the feature extraction module 14 is specifically used for:

[0197] In the basic information of each on-site person identification, determine the length of experience of each on-site person identification in the corresponding object position;

[0198] Determine a second attribute value corresponding to the experience duration of each present person identification, the second attribute value being used to indicate the present person's experience in the object position;

[0199] The mean value of the second attribute value corresponding to each on-site person identification is determined as the characteristic value corresponding to the empirical attribute.

[0200] In a possible design, the adjustment processing module 15 is specifically used for:

[0201] Determine the personnel data of the target supplier based on multiple on-site personnel identifications;

[0202] According to the number of employees, obtain the characteristic value corresponding to the scale attribute of the target supplier;

[0203] Determine the adjustment coefficient of the target supplier according to the characteristic value corresponding to the scale attribute and the characteristic values ​​of multiple characteristic attributes;

[0204] The product of the adjustment coefficient and the evaluation weight is determined as the attribute weight.

[0205] In a possible design, the adjustment processing module 15 is specifically used for:

[0206] Obtaining attribute adjustment formula or adjustment coefficient relationship table;

[0207] The adjustment coefficient is obtained by performing data processing on the characteristic values ​​of multiple characteristic attributes and the characteristic values ​​corresponding to the scale attribute through the attribute adjustment formula, or by performing query processing in the adjustment coefficient relationship table through the characteristic values ​​corresponding to the scale attribute and the characteristic values ​​of multiple characteristic attributes.

[0208] In a possible design, the text processing module 13 is specifically used for:

[0209] Perform text extraction on personnel information to determine the service time interval corresponding to each personnel ID;

[0210] According to the service time interval corresponding to each personnel identification, a plurality of on-site personnel identifications are determined.

[0211] The business processing device provided in the embodiment of the present application can execute the technical solution shown in the above method embodiment, and its implementation principle and beneficial effects are similar, which will not be repeated here.

[0212] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 The electronic device 20 may include a processor 21 and a memory 22. Exemplarily, the processor 21 and the memory 22 are interconnected via a bus 23.

[0213] The memory 22 stores computer executable instructions;

[0214] The processor 21 executes the computer execution instructions stored in the memory 22, so that the processor 21 executes the service processing method shown in the above method embodiment.

[0215] Accordingly, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the business processing method of the above method embodiment.

[0216] Accordingly, an embodiment of the present application may also provide a computer program product, including a computer program, which, when executed by a processor, can implement the business processing method shown in the above method embodiment.

[0217] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0218] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0219] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0220] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0221] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0222] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0223] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0224] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0225] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A business processing method, characterized in that: include: Receiving a service processing request sent by a user terminal, wherein the service processing request is used to request an attribute weight of a target supplier within a preset time interval; According to the business processing request, obtaining a personnel information table of the target supplier, the personnel information table including a plurality of personnel identifiers and basic information corresponding to each personnel identifier; Performing text processing on the personnel information to obtain multiple identifications of persons present within the preset time interval; Performing feature extraction processing on the basic information of each on-site person identification to obtain feature values ​​of multiple feature attributes of the target supplier, wherein the feature attributes include a stability attribute, an experience attribute, and an efficiency attribute of the target supplier; The evaluation weight of the target supplier is adjusted according to the multiple on-site person identifications and the characteristic values ​​of the multiple characteristic attributes to obtain the attribute weight of the target supplier, and the attribute weight is sent to the user terminal.

2. The method according to claim 1, characterized in that Perform feature extraction processing on the basic information of each on-site person identification to obtain feature values ​​of multiple feature attributes of the target supplier, including: Acquire a preset feature model, wherein the preset feature model includes a preset processing model corresponding to each feature attribute; For any characteristic attribute, determining a preset processing model corresponding to the characteristic attribute; By using the preset processing model corresponding to the characteristic attribute, data extraction processing is performed in the basic information of each on-site person identification to obtain the characteristic value corresponding to the characteristic attribute.

3. The method according to claim 2, characterized in that The characteristic attribute is the stable attribute; data extraction processing is performed in the basic information of each on-site person identification to obtain a characteristic value corresponding to the characteristic attribute, including: Determine the number of absences within the preset time interval from the basic information of each on-site personnel identification; The ratio of the number of departures to the number of employees is determined as the departure rate of the target supplier; A characteristic value corresponding to the stability attribute is determined according to the departure rate.

4. The method according to claim 2, characterized in that: The characteristic attribute is the efficiency attribute; Data extraction is performed on the basic information of each on-site person identification to obtain a feature value corresponding to the feature attribute, including: Obtaining the object position of each on-site person identification in the corresponding service object from the basic information of each on-site person identification; For any present person identification, determining a first attribute value corresponding to the present person identification according to the service object and the object position of the present person identification, wherein the first attribute value is used to indicate the efficiency attribute value of the present person corresponding to the present person identification; The first attribute value of each on-site person identification is weighted averaged to obtain a characteristic value corresponding to the efficiency attribute.

5. The method according to claim 4, characterized in that Determining a first attribute value corresponding to the on-site person identification according to the service object and the object position of the on-site person identification includes: Obtaining the position weight of each preset position corresponding to the service object; Determine the position weight of the target position among the position weights of each preset position; The product of the position weight and the basic attribute value of the present person is determined as the first attribute value.

6. The method according to claim 2, characterized in that The characteristic attribute is the experience attribute; Data extraction is performed on the basic information of each on-site person identification to obtain a feature value corresponding to the feature attribute, including: Determine the length of experience of each on-site personnel identification in the corresponding object position in the basic information of each on-site personnel identification; Determine a second attribute value corresponding to the experience duration of each present person identification, where the second attribute value is used to indicate the experience of the present person in the position of the object; The average value of the second attribute value corresponding to each of the on-site person identifications is determined as the feature value corresponding to the empirical attribute.

7. The method according to any one of claims 1 to 6, characterized in that: According to the multiple on-site person identifiers and the characteristic values ​​of the multiple characteristic attributes, the evaluation weight of the target supplier is adjusted to obtain the attribute weight of the target supplier, including: Determining personnel data of the target supplier according to the multiple on-site personnel identifications; According to the number of personnel, obtaining a characteristic value corresponding to the scale attribute of the target supplier; Determining an adjustment coefficient of the target supplier according to the characteristic value corresponding to the scale attribute and the characteristic values ​​of the multiple characteristic attributes; The product of the adjustment coefficient and the evaluation weight is determined as the attribute weight.

8. The method according to claim 7, characterized in that Determining the adjustment coefficient of the target supplier according to the characteristic value corresponding to the scale attribute and the characteristic values ​​of the multiple characteristic attributes includes: Obtaining attribute adjustment formula or adjustment coefficient relationship table; The adjustment coefficient is obtained by performing data processing on the characteristic values ​​of the multiple characteristic attributes and the characteristic value corresponding to the scale attribute through the attribute adjustment formula, or by performing query processing on the adjustment coefficient relationship table through the characteristic value corresponding to the scale attribute and the characteristic values ​​of the multiple characteristic attributes.

9. The method according to any one of claims 1 to 8, characterized in that: The personnel information is subjected to text processing to obtain multiple identifications of personnel present within the preset time interval, including: Performing text extraction processing on the personnel information to determine the service time interval corresponding to each personnel identification; The multiple on-site person identifiers are determined according to the service time interval corresponding to each person identifier.

10. A service processing device, characterized in that: Including receiving module, acquisition module, text processing module, feature extraction module, adjustment processing module and sending model: The receiving module is used to receive a service processing request sent by a user terminal, wherein the service processing request is used to request an attribute weight of a target supplier within a preset time interval; The acquisition module is used to acquire the personnel information table of the target supplier according to the business processing request, wherein the personnel information table includes multiple personnel identifiers and basic information corresponding to each personnel identifier; The text processing module is used to perform text processing on the personnel information to obtain multiple on-site personnel identifications within the preset time interval; The feature extraction module is used to perform feature extraction processing on the basic information of each on-site person identification to obtain feature values ​​of multiple feature attributes of the target supplier, wherein the feature attributes include a stability attribute, an experience attribute, and an efficiency attribute of the target supplier; The adjustment processing module is used to adjust the evaluation weight of the target supplier according to the multiple on-site personnel identifications and the characteristic values ​​of the multiple characteristic attributes to obtain the attribute weight of the target supplier; The sending module is used to send the attribute weight to the user terminal.

11. An electronic device, characterized in that: include: Memory and processor, The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor executes the business processing method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the business processing method according to any one of claims 1 to 9 when executed by a processor.

13. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the service processing method according to any one of claims 1 to 9 is implemented.

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