CN114648310A - Supplier behavior data analysis method, system and device - Google Patents
Supplier behavior data analysis method, system and device Download PDFInfo
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Abstract
The invention discloses a method, a system and a device for analyzing behavior data of a supplier, wherein the method comprises the following steps: acquiring actual metering data related to a target object by a supplier under each service index, wherein the target object comprises personnel docked with the supplier or projects participated by the supplier; acquiring reference metering data of the supplier under each service index, wherein the reference metering data is used for representing metering data of the supplier related to other objects except the target object and/or metering data of the target object aiming at other suppliers; and comparing the actual metering data with the reference metering data, and judging whether a cheating relationship exists between the supplier and the target object according to a comparison result. The technical scheme provided by the invention can effectively screen out the supplier data with cheating relation, thereby ensuring the authenticity of the data.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method, a system and a device for analyzing behavior data of a supplier.
Background
At present, new technologies such as an internet of things technology, a cloud computing technology and a big data technology are applied more and more deeply in material acceptance and management, and a traditional material acceptance and management mode is changed greatly. At present, the material acceptance and management system and method based on the Internet of things can replace manual work to realize the rapid weighing of materials, obtain the weighing data of the materials each time, record and process data information in the form of electronic data, reduce errors of data entry and improve the efficiency of the data entry.
Although relevant business analysis bases have been developed, the material acceptance and management system and method based on the internet of things still have the problems of data delay and relatively coarse data analysis. Similarly, the possibility that a supplier and a project communicate with each other and cheat on a construction site and a supplier and a material collector communicate with each other cannot be effectively avoided, so that the authenticity of data cannot be guaranteed.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, a system, and a device for analyzing behavioral data of a provider, which can effectively screen out provider data having a cheating relationship, thereby ensuring authenticity of the data.
One aspect of the present invention provides a method for analyzing behavioral data of a vendor, the method including: acquiring actual metering data related to a target object under each service index of a supplier, wherein the target object comprises personnel docked with the supplier or projects participated by the supplier; acquiring reference metering data of the supplier under each service index, wherein the reference metering data is used for representing metering data of the supplier related to other objects except the target object and/or metering data of the target object aiming at other suppliers; and comparing the actual metering data with the reference metering data, and judging whether a cheating relationship exists between the supplier and the target object according to a comparison result.
According to the technical scheme provided by the embodiment, when the behavior data of the supplier is analyzed, a plurality of service indexes to be assessed can be established in advance. Under each service index, actual metering data between the supplier and the target object can be obtained first. The target object may be a project in which the supplier participates, or may be a person who interfaces with the supplier. In order to measure whether a cheating relationship exists between a provider and a target object, reference metering data of the provider under various service indexes can be collected additionally. The reference metering data can be metering data between the supplier and other objects, and can also be metering data of a target object aiming at other suppliers. In this way, by comparing the big data, it can be found whether there is an abnormality in the actual metering data between the supplier and the target object. Finally, whether a cheating relationship exists between the supplier and the target object can be determined according to the comparison result.
By analyzing the metering data of the business indexes and finishing the processing by combining with the big data, whether a cheating relation exists between a supplier and the current target object can be accurately analyzed, and thus the material management process is effectively supervised.
In one embodiment, the service indicator includes: at least one of deduction amount, waybill filling rate, vehicle tare weight abnormal ratio, pound weight repeated printing ratio and volume weight ratio.
Specific service indexes related in the material management process are supervised, and the method can be more fit with an actual application scene, so that the accuracy of data analysis is improved.
In one embodiment, comparing the actual metrology data and the reference metrology data comprises: traversing each service index, calculating a difference value between the actual metering data and the reference metering data aiming at the current service index, and judging whether the difference value is within a specified error range; and if the difference value is outside the specified error range, generating early warning information aiming at the current service index.
The difference value between the actual metering data and the reference metering data is calculated, whether the difference value is within an allowable error range can be judged, if not, the possibility of cheating exists in a supplier aiming at the current service index is indicated, and at the moment, the early warning information of the service index can be generated, so that a data basis is provided for the follow-up judgment of whether the cheating relation exists.
In one embodiment, after generating the warning information for the current traffic indicator, the method further comprises: counting early warning information generated by the supplier under each service index, and recording a first early warning value matched with the counted early warning information; identifying the ranking of the actual metering data of the supplier under each service index, and recording a second early warning value of the supplier according to a ranking result; and taking the sum of the first early warning value and the second early warning value as an actual early warning value of the supplier.
The actual early warning value of the supplier is generated together through the early warning information of the service index and the ranking of the actual metering data under the service index, and high precision can be achieved.
In one embodiment, the method further comprises: and acquiring the actual early warning value of each supplier associated with the target object, and taking the sum of the actual early warning values of the suppliers as the actual early warning value of the target object.
The actual early warning value of the target object can be the sum of the actual early warning values of all suppliers related to the target object, so that the early warning level of the target object can be effectively supervised.
In one embodiment, the determining whether a cheating relationship exists between the supplier and the target object according to the comparison result comprises: generating a relation early warning graph between the target object and the supplier according to the comparison result; in the relationship early warning diagram, the respective areas occupied by the target object and the supplier are in direct proportion to respective early warning values.
The cheating relation between the supplier and the target object is represented through the relation early warning graph, and visual display effect can be provided. The higher the early warning value is, the larger the corresponding area is, so that suppliers or target objects with higher early warning values can be efficiently supervised.
In one embodiment, a first specified number of target objects with the largest warning values are presented in the relational warning graph, and for each of the target objects presented, a second specified number of suppliers with the largest warning values associated with the target object are displayed.
By displaying a limited number of target objects and suppliers in the relationship early warning diagram, not only can the display content be simplified, but also the plurality of suppliers or the target objects with the highest early warning values can be quickly positioned, so that the convenience of data screening is improved.
In one embodiment, the method further comprises: when the target object or the supplier in the relation early warning graph is selected, displaying the detailed early warning relation of the target object or the supplier in a current page.
When a certain target object or supplier is selected, the detailed early warning relation of the target object or supplier can be displayed, so that a comprehensive data screening mode is provided.
In another aspect, the present invention provides a system for analyzing behavioral data of a supplier, including: the actual data acquisition unit is used for acquiring actual metering data related to a target object under each service index of a supplier, wherein the target object comprises personnel docked with the supplier or projects participated by the supplier; the reference data acquisition unit is used for acquiring reference metering data of the supplier under each business index, wherein the reference metering data is used for representing metering data of the supplier related to other objects except the target object and/or metering data of the target object aiming at other suppliers; and the relationship judging unit is used for comparing the actual metering data with the reference metering data and judging whether a cheating relationship exists between the supplier and the target object according to a comparison result.
The invention also provides a supplier behavior data analysis device, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the computer program realizes the supplier behavior data analysis method when being executed by the processor.
In another aspect, the present invention further provides a computer storage medium for storing a computer program, which when executed by a processor, implements the above-mentioned behavior data analysis method for a vendor.
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The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
FIG. 1 is a schematic diagram illustrating the steps of a supplier behavior data analysis method according to an embodiment of the present invention;
FIG. 2 illustrates a relationship warning diagram between a vendor and a project in one embodiment of the invention;
FIG. 3 illustrates a relationship warning diagram between a supplier and a person in one embodiment of the invention;
FIG. 4 is a flow chart illustrating the execution of a project in an exemplary embodiment of the present invention;
FIG. 5 is a functional block diagram of a supplier behavior data analysis system in accordance with one embodiment of the present invention;
fig. 6 is a schematic structural diagram showing a behavior data analysis device of a vendor in one embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1, a supplier behavior data analysis method according to an embodiment of the present application may include the following steps.
S1: and acquiring actual metering data related to a target object by a supplier under each service index, wherein the target object comprises personnel interfacing with the supplier or projects participated by the supplier.
In this embodiment, the behavior data of the supplier may be analyzed to determine whether a cheating relationship exists between the supplier and the project or between the supplier and the person who is docked. Specifically, the behavior data of the provider may be analyzed respectively for a plurality of preset business indexes. The plurality of business indicators may include at least one of deduction amount, waybill filling rate, vehicle tare weight abnormal proportion, pound-weight repeat print proportion, volume-to-weight ratio. Of course, the actual service index can be flexibly adjusted according to different application scenarios. Specific service indexes related in the material management process are supervised, and the method can be more fit with an actual application scene, so that the accuracy of data analysis is improved.
In this embodiment, both the personnel interfacing with the supplier and the items participated by the supplier can be used as the target object to be considered, and whether a cheating relationship exists between the supplier and the target object can be determined by analyzing the behavior data between the supplier and the target object.
For a project, the actual metering data corresponding to each service index may be calculated as follows:
hold (in percent): labels to display sand and gravel; the calculation method is as follows: the deduction amount of the supplier under a certain label of the item/the net weight of the supplier on the label of the item;
filling rate of waybill: displaying a concrete label; the calculation method is as follows: the supplier fills in the receiving times of the freight note under the project concrete label (N pieces of multiple material calculation)/the supplier total receiving times under the project concrete label (N pieces of multiple material calculation);
the tare weight of the vehicle is abnormal: displaying the total proportion of all materials; the calculation method is as follows: the number of times of vehicles with abnormal tare weight of the vehicle in the project/the number of times of vehicles for receiving the gross weight of the project of the supplier;
pound per repeat print to weight ratio: displaying the total proportion of all materials; the calculation method is as follows: the supplier weighs the pound order on the item to print the number of times/the supplier weighs the item to receive the material;
the volume-weight ratio is as follows: displaying material detail under the concrete material label; the calculation method is as follows: the supplier is at the median of the material detail volume-to-weight ratio for the project.
For a person, the actual metering data corresponding to each service index may be calculated as follows:
hold (in percent): labels to display sand and gravel; the calculation method is as follows: the amount the supplier withholds on the item under a label of the person/the net weight of the supplier under the label of the person on the item;
filling rate of waybill: displaying a concrete label; the calculation method is as follows: the supplier fills the receiving times of the freight note under the personnel concrete label of the project (N pieces of multi-material calculation)/the supplier fills the total receiving times under the personnel concrete label of the project (N pieces of multi-material calculation);
the tare weight of the vehicle is abnormal: displaying the total proportion of all materials; the calculation method is as follows: the number of times of the supplier on the abnormal tare weight vehicle of the person on the project/the number of times of the supplier on the total weighing and material collecting vehicle of the person on the project;
pound per repeat print to weight ratio: displaying the total proportion of all materials; the calculation method is as follows: the supplier receives the weighing pound order printing times of the person on the project/the weighing receiving vehicle times of the person on the project;
the volume-weight ratio is as follows: displaying material detail under the concrete material label; the calculation method is as follows: the supplier has a median value of the material specific volume weight ratio of the person in the project.
In this embodiment, for each business index, the actual metering data of the supplier related to the target object may be obtained according to the above calculation method.
S3: and acquiring reference metering data of the supplier under each service index, wherein the reference metering data is used for representing metering data of the supplier related to other objects except the target object and/or metering data of the target object aiming at other suppliers.
In this embodiment, if only the actual metrology data of the supplier related to the target object is considered, it is usually impossible to measure whether there is a deviation in the actual metrology data. In view of this, reference metering data of the provider under each of the service indexes may be further collected. Specifically, the reference metering data may be metering data of a supplier related to other objects except the target object, or may be metering data of the target object for other suppliers.
In practical applications, for a project, metering data of a supplier related to other projects can be calculated as follows:
deduction (in percent): labels to display sand and gravel; the calculation method is as follows: deduction amount of the supplier under a certain label of the tenant/net weight of the supplier on the label of the tenant; a tenant refers to a company or a group, and under a tenant, a plurality of items may exist.
Filling rate of waybill: displaying a concrete label; the calculation method is as follows: the supplier fills the receiving times (N pieces of multi-material calculation) of the shipping note under the concrete label of the tenant/the total receiving times (N pieces of multi-material calculation) of the supplier under the concrete label of the tenant;
the tare weight of the vehicle is abnormal: displaying the total proportion of all materials; the calculation method is as follows: the supplier times of vehicles with abnormal tare weights in the tenants/the supplier times of vehicles which are weighed and collected by the tenants;
pound per repeat print to weight ratio: displaying the total proportion of all materials; the calculation method is as follows: the supplier receives the material weighing pound order of the tenant and prints the times/the supplier weighs the material receiving vehicle times of the tenant;
volume-weight ratio: displaying material detail under the concrete material label; the calculation method is as follows: the supplier is in the median of the material detail volume-weight ratio of the tenant.
For a project, the metering data of the project for other suppliers may be calculated in the following manner (with the other suppliers as a whole):
hold (in percent): labels to display sand and gravel; the calculation method is as follows: the item the tag deduction/the item the tag net weight;
filling rate of waybill: displaying a concrete label; the calculation method is as follows: filling the receiving times of the freight note under the project concrete label (N pieces of material calculation)/the total receiving times under the project concrete label (N pieces of material calculation);
the tare weight of the vehicle is abnormal: displaying the total proportion of all materials; the calculation method is as follows: the times of vehicles with abnormal tare weight/the times of vehicles for collecting gross tare weight of the project;
pound per repeat print to weight ratio: displaying the total proportion of all materials; the calculation method is as follows: the item receiving weighing pound order printing times/the item weighing receiving vehicle times;
the volume-weight ratio is as follows: displaying material details under the concrete material label; the calculation method is as follows: the median of the material weight ratios for this project.
In practical applications, for a person, metering data of a supplier related to other persons except the current person and metering data of the person for other suppliers may be calculated in a similar manner, and will not be described herein again.
According to the method, the reference metering data of the supplier under each service index can be collected. Referring to table 1, table 1 shows actual metering data and reference metering data between suppliers and projects.
TABLE 1 actual and reference metering data between suppliers and projects
By analyzing the contents of table 1, it can be found that:
the withholding amount of sand is relatively small, but the withholding amount of other sand suppliers on the project is large. It is therefore necessary to check whether the deduction meets the requirements.
The volume weight ratio of the C30 concrete is smaller, but the volume weight ratio of the concrete of other suppliers on the project is larger, namely C30 concrete. There is therefore a need to check whether there is a risk of filling a small volume-to-weight ratio circumventing negative differential monitoring.
The filling rate of the concrete waybill is relatively low, but the filling rate of the waybill of other concrete suppliers on the project is high. There is therefore a need to check whether there is a risk of evading negative monitoring without filling in the waybill.
The tare warning of the vehicle is high in percentage, but tare warning of other suppliers on the project is low in percentage. There is therefore a need to check whether there is a risk of cheating.
Pound sign-on is more common, but other suppliers on the project have less pound sign-on. It is therefore necessary to check whether there is a risk of duplicate settlement.
S5: and comparing the actual metering data with the reference metering data, and judging whether a cheating relation exists between the supplier and the target object according to a comparison result.
In this embodiment, by comparing the actual metering data with the reference metering data, it can be determined whether risk check needs to be performed on one or more of the service indexes. Considering that a certain deviation exists between the actual metering data and the reference metering data, if the deviation is within an allowable range, risk checking can not be carried out; and if the deviation exceeds the allowable range, risk check is required, and whether a cheating relationship exists between the target object and the supplier is further judged.
Specifically, in one embodiment, each business metric may be traversed and the difference between the actual metering data and the reference metering data calculated for the current business metric. If the difference value is out of the specified error range, the difference value indicates that the deviation between the actual metering data and the reference metering data is too large, and at this time, the early warning information can be generated aiming at the current service index.
Generally, the difference between the actual metrology data and the reference metrology data may be represented by a percentage. In practical application, if the difference value satisfies any one of the following conditions, corresponding early warning information may be generated:
and (4) deduction amount: the supplier has less than 10% of the two reference metering data at the same time in the actual metering data of the project. For example, if both reference metrology data are 5%, then if the actual metrology data is less than 10% of 5%, i.e., less than 4.5%, then early warning information may be generated for the deduction;
concrete volume-weight ratio: the actual metering data of the supplier in the project is less than 3% of the two reference metering data;
filling rate of concrete freight notes: the supplier has the actual metering data of the project less than 10% of the two reference metering data;
abnormal ratio of train weight: the supplier has more than 10% of the two reference metering data at the same time in the actual metering data of the project;
repeated printing proportion: the supplier has more than 10% of the two reference metering data at the same time in the actual metering data of the project.
Of course, in practical applications, the specific ratio greater than or less than the above ratio may be flexibly set according to the need, and is not limited to the above-mentioned values.
The difference value between the actual metering data and the reference metering data is calculated, whether the difference value is within an allowable error range can be judged, if not, the possibility of cheating exists in a supplier aiming at the current service index is indicated, and at the moment, the early warning information of the service index can be generated, so that a data basis is provided for the follow-up judgment of whether the cheating relation exists.
In one embodiment, after the early warning information is generated for each service index, the early warning information generated by the supplier under each service index can be counted, and a first early warning value matched with the counted early warning information is recorded.
Wherein, when one piece of early warning information is generated, 10 points of early warning value can be recorded. Assuming that a certain supplier generates warning information for 5 service indicators, the first warning value may be 50 points.
In addition, the ranking of the actual metering data of the supplier under each service index can be identified, and the second early warning value of the supplier is recorded according to the ranking result.
In practical applications, the ranking may be performed in different ways for different traffic indicators. For example, for deduction amount, volume-to-weight ratio and waybill filling rate, the smaller the actual metering data, the greater the risk. Therefore, for the three service indexes, the actual metering data of each supplier can be arranged in the order from small to large, and the early warning values of 10 points, 8 points, 6 points, 4 points and 2 points can be respectively allocated to the 5 suppliers with the highest ranking. And for the abnormal proportion of the train weight and the proportion of the repeated printing, the larger the actual metering data is, the larger the risk is. Therefore, for the two service indexes, the actual metering data of each supplier can be arranged in the descending order, and the early warning values of 10 points, 8 points, 6 points, 4 points and 2 points can be respectively allocated to the 5 suppliers with the highest ranking.
For a certain supplier, the early warning values ranked according to the service indexes can be accumulated, so that a corresponding second early warning value is obtained.
Finally, the first warning value and the second warning value are added, and the added sum can be used as the actual warning value of the supplier. Therefore, the actual early warning value of the supplier is generated together through the early warning information of the service index and the ranking of the actual metering data under the service index, and higher precision can be achieved.
For the target object, the actual early warning values of the suppliers associated with the target object may be obtained, and the sum of the actual early warning values of the suppliers may be used as the actual early warning value of the target object.
The actual early warning value of the target object can be the sum of the actual early warning values of all suppliers associated with the target object, so that the early warning level of the target object can be effectively supervised.
In this embodiment, a threshold for determining the cheating relationship may be set, and if the actual early warning value of the provider exceeds the threshold, it indicates that the provider currently has cheating behavior. Similarly, if the actual warning value of the target object exceeds the threshold, it also indicates that a cheating relationship exists between the target object and the supplier.
In one embodiment, a relationship pre-warning map between the target object and the supplier may be generated according to the comparison result. In the relationship early warning diagram, the respective areas occupied by the target object and the supplier may be in direct proportion to respective early warning values.
Specifically, fig. 2 shows a relationship warning diagram between a project and a supplier, and fig. 3 shows a relationship warning diagram between a person and a supplier. The cheating relationship between the supplier and the target object is represented through the relationship early warning diagram, and visual display effect can be provided. The higher the early warning value is, the larger the corresponding area is, so that the suppliers or target objects with higher early warning values can be efficiently supervised.
In one embodiment, in the relational warning map, a first specified number of target objects having a largest warning value may be presented, and for each of the presented target objects, a second specified number of suppliers having a largest warning value associated with the target object may be displayed.
For example, in fig. 2 and 3, the first specified number may be 4, and the second specified number may be 3, so that in the relationship warning diagram, 4 target objects with the largest warning values are shown, and for each target object, 3 suppliers with the largest warning values are shown.
By displaying a limited number of target objects and suppliers in the relationship early warning diagram, not only can the display content be simplified, but also the plurality of suppliers or the target objects with the highest early warning values can be quickly positioned, so that the convenience of data screening is improved.
In one embodiment, when the target object or the supplier in the relationship early warning diagram is selected, a detailed early warning relationship of the target object or the supplier may be displayed in a current page. For example, when "zhang san" in fig. 3 is selected, the warning values of the respective suppliers related to zhang san "may be shown in the current page. The number of suppliers shown in the detailed early warning relationship is usually more than the second specified number described above. Of course, if the number of suppliers associated with the target object is only a second specified number by nature, then only the second specified number of suppliers will be shown in the detailed early warning relationship.
Therefore, when a certain target object or supplier is selected, the detailed early warning relation of the target object or supplier can be displayed, and a comprehensive data screening mode is provided.
Referring to fig. 4, in a specific application scenario, the execution process of the project may be as follows:
and (4) creating a pound order on a material platform, and performing material self-inspection. Weighing the materials in and out of the site, selecting materials and suppliers on a platform, filling the quantity of the waybills, calculating an actual weighing weight of the waybills in the weighing end of the system according to weighing data of the materials in and out of the site, setting different conversion coefficients by different supply units to obtain a weight of the waybills, and calculating a deviation value according to the weight of the waybills and the actual weight of the waybills. The deviation amount may be within a normal and reasonable interval range, which indicates that the material receiving and discharging are normal and that the suppliers and the working responsible personnel on the project have no relationship. And if the deviation of the personnel to the supplier material receiving is always larger than that of other personnel, the personnel needs to carry out reverse deduction through the deviation to judge whether cheating behaviors exist.
The pound sheet is uploaded to a cloud-end platform, a cloud-end server runs a timing task in the background (the timing task is carried out once every day), and related projects, personnel and suppliers calculate points and the points are stored in a database. Therefore, the early warning relation conditions of data, items and suppliers, personnel and suppliers can be checked on a home page interface, a drilling interface is further arranged on the home page, five risk items including deduction amount, volume-weight ratio, waybill filling, vehicle tare weight and repeated printing of each supplier and personnel or items are used for early warning evaluation, early warning values are sorted, whether the deviation of the waybill weight and the actual weight exceeds the range or not is analyzed, and red warning is carried out on the overnegative difference.
In practical application, the prompt information can be set by the page of the relation early warning diagram. Specifically, when the current user views the data center for the first time, the tab of the data center can prompt the number of items and personnel pre-warned by the current month, the current organization and the current page through the small red dots. When the data center is accessed again, the new early warning items and the number of personnel in the current month, the current organization and the current page can be prompted through the small red dots. For the aspect of the display effect of the relationship early warning diagram, circles corresponding to suppliers, projects and personnel can be displayed in a hierarchical manner according to the account number and the grouping level, the current month is displayed by default, and the user can also select the month for viewing. The page of the relationship early warning graph can be flexibly set according to a User Interface (UI).
According to the technical scheme provided by one or more embodiments of the application, when the behavior data of the supplier is analyzed, a plurality of service indexes to be assessed can be established in advance. Under each service index, actual metering data between the supplier and the target object can be obtained first. The target object may be a project in which the supplier participates, or may be a person who interfaces with the supplier. In order to measure whether a cheating relationship exists between a provider and a target object, reference metering data of the provider under various service indexes can be collected additionally. The reference metering data can be metering data between the supplier and other objects, and can also be metering data of a target object aiming at other suppliers. In this way, through comparison of big data, whether the actual metering data between the supplier and the target object has an abnormality can be found. Finally, whether a cheating relationship exists between the supplier and the target object can be determined according to the comparison result.
By analyzing the metering data of the service index and finishing the processing by combining big data, whether a cheating relation exists between a supplier and a current target object can be accurately analyzed, so that the material management process is effectively supervised.
Referring to fig. 5, an embodiment of the present application further provides a behavior data analysis system of a supplier, where the system includes:
the actual data acquisition unit is used for acquiring actual metering data related to a target object under each service index of a supplier, wherein the target object comprises personnel docked with the supplier or projects participated by the supplier;
the reference data acquisition unit is used for acquiring reference metering data of the supplier under each business index, and the reference metering data is used for representing metering data of the supplier related to other objects except the target object and/or metering data of the target object aiming at other suppliers;
and the relationship judging unit is used for comparing the actual metering data with the reference metering data and judging whether a cheating relationship exists between the supplier and the target object according to a comparison result.
Referring to fig. 6, an embodiment of the present application further provides a supplier behavior data analysis apparatus, where the apparatus includes a memory and a processor, the memory is used for storing a computer program, and the computer program, when executed by the processor, implements the supplier behavior data analysis method described above.
An embodiment of the present application further provides a computer storage medium for storing a computer program, which when executed by a processor, implements the above-mentioned behavior data analysis method for a vendor.
The processor may be a Central Processing Unit (CPU). The Processor may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or a combination thereof.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the methods of the embodiments of the present invention. The processor executes various functional applications and data processing of the processor by executing non-transitory software programs, instructions and modules stored in the memory, that is, the method in the above method embodiment is realized.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor, and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and such remote memory may be coupled to the processor via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk Drive (Hard Disk Drive, abbreviated as HDD), or a Solid State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (11)
1. A method for analyzing behavioral data of a supplier, the method comprising:
acquiring actual metering data related to a target object by a supplier under each service index, wherein the target object comprises personnel docked with the supplier or projects participated by the supplier;
acquiring reference metering data of the supplier under each service index, wherein the reference metering data is used for representing metering data of the supplier related to other objects except the target object and/or metering data of the target object aiming at other suppliers;
and comparing the actual metering data with the reference metering data, and judging whether a cheating relationship exists between the supplier and the target object according to a comparison result.
2. The method of claim 1, wherein the traffic indicator comprises: at least one of deduction amount, waybill filling rate, vehicle tare weight abnormal ratio, pound weight repeated printing ratio and volume weight ratio.
3. The method of claim 1 or 2, wherein comparing the actual metrology data and the reference metrology data comprises:
traversing each service index, calculating a difference value between the actual metering data and the reference metering data aiming at the current service index, and judging whether the difference value is within a specified error range;
and if the difference value is outside the specified error range, generating early warning information aiming at the current service index.
4. The method of claim 3, wherein after generating early warning information for the current traffic indicator, the method further comprises:
counting early warning information generated by the supplier under each service index, and recording a first early warning value matched with the counted early warning information;
identifying the ranking of the actual metering data of the supplier under each service index, and recording a second early warning value of the supplier according to a ranking result;
and taking the sum of the first early warning value and the second early warning value as an actual early warning value of the supplier.
5. The method of claim 4, further comprising:
and acquiring the actual early warning value of each supplier associated with the target object, and taking the sum of the actual early warning values of the suppliers as the actual early warning value of the target object.
6. The method of claim 1 or 5, wherein determining whether a cheating relationship exists between the provider and the target object based on the comparison comprises:
generating a relation early warning graph between the target object and the supplier according to the comparison result; in the relationship early warning diagram, the respective areas occupied by the target object and the supplier are in direct proportion to respective early warning values.
7. The method according to claim 6, characterized in that in the relational forewarning map, a first specified number of target objects with the greatest forewarning value are presented, and for each of the target objects presented, a second specified number of suppliers with the greatest forewarning value associated with the target object are displayed.
8. The method of claim 6, further comprising:
when the target object or the supplier in the relation early warning graph is selected, displaying the detailed early warning relation of the target object or the supplier in a current page.
9. A supplier behavior data analysis system, the system comprising:
the actual data acquisition unit is used for acquiring actual metering data related to a target object under each service index of a supplier, wherein the target object comprises personnel docked with the supplier or projects participated by the supplier;
the reference data acquisition unit is used for acquiring reference metering data of the supplier under each business index, wherein the reference metering data is used for representing metering data of the supplier related to other objects except the target object and/or metering data of the target object aiming at other suppliers;
and the relationship judging unit is used for comparing the actual metering data with the reference metering data and judging whether a cheating relationship exists between the supplier and the target object according to a comparison result.
10. An apparatus for behavioral data analysis of a supplier, the apparatus comprising a memory for storing a computer program and a processor, the computer program when executed by the processor implementing the method of any one of claims 1 to 8.
11. A computer storage medium for storing a computer program which, when executed by a processor, implements a method as claimed in any one of claims 1 to 8.
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