CN113362044A - Method for improving approval efficiency process based on automobile retail - Google Patents

Method for improving approval efficiency process based on automobile retail Download PDF

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CN113362044A
CN113362044A CN202110775161.8A CN202110775161A CN113362044A CN 113362044 A CN113362044 A CN 113362044A CN 202110775161 A CN202110775161 A CN 202110775161A CN 113362044 A CN113362044 A CN 113362044A
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陈�光
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Beijing Ronglian Qimo Technology Co ltd
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Abstract

The invention provides a method for improving approval efficiency process based on automobile retail, which comprises the steps of inquiring a corresponding client file according to a client incoming call number, determining corresponding vehicle information, binding the incoming call number, the client file and the vehicle information with a historical work order, selecting a corresponding service type in a service directory according to the appeal of a client, matching the corresponding process for the service type based on a work order triggering system, creating a first work order, and carrying out corresponding intelligent reminding according to the first work order. The invention has the brand-new solution of meeting the special requirements of the automobile retail industry and improving the approval efficiency process, a perfect business process system, full-time standardization, real-time tracking of short message replies and guarantee of efficient work order processing.

Description

Method for improving approval efficiency process based on automobile retail
Technical Field
The invention relates to the technical field of automobile retail, in particular to a method for improving an approval efficiency process based on automobile retail.
Background
At present, the increasing material demand and the rapid scientific and technological development of China bring huge changes to the automobile industry, and the automobile market faces the phenomena of no customer consultation, no complaint, no door complaint, uneven after-sale service and the like while meeting new opportunities of informatization, intellectualization, networking and sharing of the automobile industry. The existing work order system flow can not completely meet the special requirements of the automobile retail and after-sale service industry, so that the problem of overstocking of work orders of customers is caused, the procedures are complicated, the service is low in efficiency, and the brand image is finally influenced. Therefore, schemes for realizing efficient control of various processes in the automobile retail and after-sales service industries need to be continuously researched and explored.
The system can respond to and process various problems and conditions fed back by customers timely and efficiently, and has a decisive role in improving customer satisfaction and brand recognition.
Therefore, the invention provides a method for improving the approval efficiency process based on automobile retail.
Disclosure of Invention
The invention provides a method for improving an approval efficiency process based on automobile retail, which is used for establishing a work order by matching processes according to customer appeal, is convenient for improving the approval efficiency process, further perfects a business process system, is convenient for real-time tracking and processing by reminding, and ensures efficient work order processing.
In order to achieve the aim, the invention provides a method for improving the approval efficiency process based on automobile retail, which comprises the following steps:
step 1: inquiring a corresponding client file according to the client incoming call number, determining corresponding vehicle information, and binding the incoming call number, the client file, the vehicle information and a historical work order;
step 2: selecting a corresponding service type from a service directory according to the appeal of a client, matching a corresponding flow for the service type based on a work order triggering system, and creating a first work order;
and step 3: and carrying out corresponding intelligent reminding according to the first work order.
Preferably, the method for improving the approval efficiency process based on automobile retail includes the following steps: inquiring a corresponding customer file according to the incoming call number of the customer, and determining corresponding vehicle information, wherein the steps comprise:
determining the vehicle type configuration of the client according to the client file;
judging whether a historical incoming call exists or not according to the customer file;
if so, extracting historical incoming call records, acquiring historical incoming call reasons and results, and integrating the customer vehicle type configuration and the historical incoming call reasons and results to generate vehicle information.
Preferably, the method for improving the approval efficiency process based on automobile retail includes: pre-sale service type and post-sale service type;
the pre-sale service type is used for providing commodity knowledge related to the appeal of the client to the client, guiding the client to select and purchase a commodity most suitable for the appeal, and providing a using and maintaining method of the commodity;
the after-sales service is used for predicting possible problems in the automobile retail process according to the appeal of the client and carrying out prediction reminding on the possible problems;
wherein the commodity is associated with an automobile.
Preferably, the method for improving the approval efficiency process based on automobile retail includes the following steps: before inquiring the corresponding customer file according to the incoming call number of the customer, the method comprises the following steps:
inquiring whether a client file corresponding to the incoming call number exists or not according to the incoming call number of the client;
if yes, extracting corresponding customer files;
if not, calling historical vehicle consultation information of the client and establishing a client file.
Preferably, the method for improving the approval efficiency process based on automobile retail includes the following steps: according to the appeal of the client, selecting a corresponding service type from a service directory, wherein the service type comprises the following steps:
determining corresponding client incoming call content according to the client appeal, performing text conversion, and performing word segmentation processing on the converted text to obtain a first phrase to be processed;
inputting the first phrase to be processed into a pre-trained word sense conversion model to obtain a plurality of first words with single word senses and a plurality of second words with self word senses, and calibrating the second words;
extracting word senses of adjacent front and back word groups of the marked second word, intelligently matching the second word according to the word senses of the adjacent front and back word groups, and selecting the best word sense matched with the word senses of the adjacent front and back word groups from a plurality of self word senses of the second word;
combining all the single word senses and the optimal word sense in sequence to obtain text translation;
fuzzy matching is carried out on the text translation and preset feature keywords, and whether the feature keywords exist in the text translation or not is judged;
if not, storing the incoming call content of the client into a first storage database;
if yes, calibrating the characteristic keywords to obtain calibrated keywords;
classifying the calibration keywords, calculating the similarity degree of each keyword in each classification with a preset classification keyword, and eliminating the calibration keywords with the similarity degree smaller than a preset threshold value to obtain a first keyword group of each classification;
calculating the weighted value of each classified first key phrase in the total key phrases, selecting a preset classified key word corresponding to the phrase with the largest weighted value as the final appeal of the client, and selecting a corresponding service type in the service directory according to the final appeal of the client.
Preferably, based on the work order triggering system, in the process of creating the first work order by matching the corresponding flow to the service type, the method further includes:
obtaining sub-services based on the selected service types, extracting flow information corresponding to the sub-services from a service database, dividing the flow information into a plurality of sub-flows, and presetting pre-cut-in information cut-in corresponding to each sub-flow according to the content of each sub-flow;
analyzing the appeal of the client, and judging whether a preposed cut-in instruction related to each sub-process exists or not;
if not, starting service from the initial sub-process in the sub-service;
if yes, determining a sub-process corresponding to the maximum direction of the preposed cut-in instruction as a first input sub-process, and judging whether the service content corresponding to the first input process is consistent with the maximum intention service content corresponding to the appeal of the client or not based on an artificial intelligence technology;
if not, analyzing service contents corresponding to the customer complaints, acquiring a first sub-process service sequence, acquiring a corresponding most sub-process according to the maximum intention service contents, adjusting the first sub-process service sequence to acquire a second sub-process service sequence, and serving the complaints according to the second sub-process service sequence;
preferably, the predicting the possible problems in the automobile retail process according to the customer appeal further includes:
searching for fault information of sold vehicle types according to the appeal of the client, integrating and classifying the searched fault information, and acquiring various types of fault information of various vehicle types;
step S201: extracting all hardware fault information as overall fault information, classifying all hardware fault information according to each hardware type, calculating the proportion of each type of hardware fault information in the vehicle type overall fault information, and acquiring the fault condition of each part in the proportion fault type;
step S202: calculating the failure rate of each part according to the failure condition of each part, sequencing each part according to the failure rate of each part, reminding a maintenance department to store accessories correspondingly according to the sequencing sequence, and calculating the average service life of each part according to failure information;
step S203: based on the customer file, calculating the service life of each part of the vehicle based on the vehicle maintenance and repair information, comparing the service life with the average service life of each part, and judging whether the service life of each part of the vehicle is longer than the average service life of the part;
if not, judging that the potential safety hazard of the vehicle is small;
step S204: if the part is in the safe state, communication connection is established with the vehicle-mounted machine system through the Internet, prompt voice is sent, and a user is reminded that potential safety hazards of corresponding parts are large, so that checking and maintenance are carried out.
Preferably, the establishing of the communication connection with the car machine system via the internet includes:
step S301: reading vehicle information according to the client file, acquiring the vehicle machine system terminal ip address, and positioning the vehicle machine system terminal ip address;
step S302: establishing a communication channel between the cloud server and a corresponding vehicle machine system terminal;
step S303: and based on the positioning information, sending a connection request to an area where an ip address of the terminal of the in-vehicle system is located from the cloud server, and transmitting a voice signal to the in-vehicle system based on a communication channel based on the cloud server after the in-vehicle system detects the connection request.
Preferably, the method for improving the approval efficiency process based on automobile retail includes the following steps: according to the first work order, the corresponding intelligent reminding process is carried out, and the method further comprises the following steps:
establishing a reminding mechanism according to the work order flow of the first work order, wherein the reminding mechanism comprises a plurality of reminding points, and sending out corresponding short message reminding or intelligent outbound reminding when the reminding points are reached;
if the stop reminding instruction of the corresponding work order processing personnel is not received in the preset time period after the reminding point, the reminding is continued according to the reminding mechanism, and in the reminding process, the signal to noise ratio of the corresponding reminding point for carrying out short message reminding or intelligent outbound reminding in the process of transmitting the reminding signal is calculated, and the method comprises the following steps:
detecting the number of signal nodes passing through in the transmission process of a reminding signal corresponding to the short message reminding or the intelligent outbound reminding and the corresponding transmission bandwidth;
calculating a loss coefficient F in the transmission process of the reminding signal according to the formula:
Figure BDA0003152490540000051
wherein,
Figure BDA0003152490540000053
the power coefficient of a valid signal in the reminding signal is; theta2The power coefficient of the noise signal in the reminding signal is obtained; n is the number of signal nodes passing through the reminding signal in the transmission process, and the number of the nodes is at least 2; kiIs the energy of the ith signal node; k is the average energy of all signal nodes; tau is the signal error coefficient in the transmission process and has the value range of 0.8,1.2]；
Calculating a corresponding signal-to-noise ratio gamma according to a loss coefficient F in the transmission process of the reminding signal, wherein the calculation formula is as follows:
Figure BDA0003152490540000052
wherein, P is the power of the effective signal in the reminding signal; d is the bandwidth of the reminding signal; xi is the unilateral power spectrum density of the noise signal in the transmission process of the reminding signal; upsilon is a measurement error factor;
comparing the obtained signal-to-noise ratio with a preset signal-to-noise ratio threshold, if the obtained signal-to-noise ratio is smaller than the preset signal-to-noise ratio threshold, judging that the transmission is unqualified, enhancing a reminding signal, and if a time point to be transmitted corresponding to the enhanced reminding signal is not overlapped with the next reminding point, transmitting the enhanced reminding signal based on the time point to be transmitted; if the reminding signals coincide with the enhanced reminding signals, the reminding signals corresponding to the subsequent reminding points are updated based on the enhanced reminding signals;
if the corresponding stop reminding instruction is not received after the reminding is finished based on the reminding mechanism, upgrading the second work order into an emergency work order and pushing the emergency work order to other processing personnel for processing;
and the time difference value between the current reminding point and the next reminding point is greater than the time value of the preset time period. Preferably, the calculating the weight value of the first keyword group in each category in the total keyword group includes:
acquiring the number of keywords in the first keyword group, and acquiring the number of keywords in the total keyword group;
calculating the weight value alpha of the first key phrase in the total key phrase according to the following formula:
Figure BDA0003152490540000061
wherein Q is the number of the key words of the total key word group; q is the number of the keywords of the first keyword group; lambda is an empirical constant, and the value of lambda is 0.01; e is a natural constant;
meanwhile, preprocessing the remaining various first key phrases to obtain corresponding weight values;
and sequencing all the weight values according to the sizes, and extracting the minimum weight value.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a method for improving approval efficiency based on automobile retail in an embodiment of the present invention;
FIG. 2 is a flow chart of a method for improving approval efficiency based on automobile retail sales in accordance with another embodiment of the present invention;
FIG. 3 is a flow chart of a method for improving approval efficiency based on automobile retail sales in accordance with yet another embodiment of the present invention;
FIG. 4 is a flow chart of a consultation work order based on a process of improving approval efficiency in automobile retail according to another embodiment of the present invention;
FIG. 5 is a flow chart of a major complaint worksheet based on a process of improving approval efficiency for retail automobile sales under still another embodiment of the present invention;
FIG. 6 is a flow chart of a generic complaint worksheet for a procedure of improving approval efficiency based on retail sales of automobiles according to still another embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
A method for improving an approval efficiency process based on automobile retail according to an embodiment of the present invention is described below with reference to fig. 1 to 6.
Example 1:
as shown in fig. 1, the present invention provides a method for improving approval efficiency flow based on automobile retail, including:
step 1: inquiring a corresponding client file according to the client incoming call number, determining corresponding vehicle information, and binding the incoming call number, the client file, the vehicle information and a historical work order;
step 2: selecting a corresponding service type from a service directory according to the appeal of a client, matching a corresponding flow for the service type based on a work order triggering system, and creating a first work order;
and step 3: and carrying out corresponding intelligent reminding according to the first work order.
The service types include a consultation work order, a complaint work order, a major complaint work order, an emergency rescue work order, i.e., a processing work order, and the corresponding specific embodiment flows are shown in fig. 4-6.
In this embodiment, the client file is a file for recording client information; the vehicle information is vehicle configuration parameters and fault information; the historical work order is a work order processed historically in the customer file; the service type is the service type corresponding to the telephone of the client determined according to the telephone content of the client; the corresponding flow is a corresponding service flow according to the service type; the first work order is a newly established work order according to the user complaint; the intelligent reminding is used for reminding when the processing time of the work order exceeds the standard.
In this embodiment, the customer's appeal may be pre-sale consultation or post-sale maintenance, complaint, emergency rescue, and other post-sale problems.
The beneficial effect of above-mentioned scheme: the work order is established by matching the flow according to the client appeal, so that the approval efficiency flow is improved conveniently, a perfect business flow system is further provided, real-time tracking and processing are facilitated through reminding, and efficient work order processing is guaranteed.
Example 2:
based on embodiment 1, the querying a corresponding customer profile according to the customer calling number and determining corresponding vehicle information includes:
determining the vehicle type configuration of the client according to the client file;
judging whether a historical incoming call exists or not according to the customer file;
if so, extracting historical incoming call records, acquiring historical incoming call reasons and results, and integrating the customer vehicle type configuration and the historical incoming call reasons and results to generate vehicle information.
In this embodiment, the vehicle type is configured as specific parameter information of the vehicle type; the historical incoming call is the historical incoming call condition of the client;
the beneficial effect of above-mentioned scheme: the vehicle type configuration information is determined according to the client incoming call number, whether historical incoming call information exists or not is judged, and the historical appeal and the after-sale condition of the vehicle are obtained according to the historical incoming call information, so that the generated vehicle condition is more comprehensive, and the appeal is conveniently processed.
Example 3:
based on embodiment 1, the service types include: pre-sale service type and post-sale service type;
the pre-sale service type is used for providing commodity knowledge related to the appeal of the client to the client, guiding the client to select and purchase a commodity most suitable for the appeal, and providing a using and maintaining method of the commodity;
the after-sales service is used for predicting possible problems in the automobile retail process according to the appeal of the client and carrying out prediction reminding on the possible problems;
wherein the commodity is associated with an automobile.
In this embodiment, the commodity knowledge is configuration parameter information and usage knowledge of the sold automobile;
the beneficial effect of above-mentioned scheme: the automobile retail is classified, when customer appeal is received, the appeal is processed according to the corresponding classification, and the processing efficiency can be improved.
Example 4:
based on embodiment 1, before querying the corresponding customer profile according to the customer calling number, the method includes:
inquiring whether a client file corresponding to the incoming call number exists or not according to the incoming call number of the client;
if yes, extracting corresponding customer files;
if not, calling historical vehicle consultation information of the client and establishing a client file.
In this embodiment, the historical vehicle advisory information is advisory information for the vehicle in the customer's historical phone record.
The beneficial effect of above-mentioned scheme: and inquiring whether a client file exists or not, if not, establishing the client file according to the historical vehicle consultation information of the client, and recording the client information so as to conveniently recommend a proper vehicle type to the client according to the recorded information.
Example 5:
based on embodiment 1, according to the appeal of the client, selecting a corresponding service type from the service directory, including:
determining corresponding client incoming call content according to the client appeal, performing text conversion, and performing word segmentation processing on the converted text to obtain a first phrase to be processed;
inputting the first phrase to be processed into a pre-trained word sense conversion model to obtain a plurality of first words with single word senses and a plurality of second words with self word senses, and calibrating the second words;
extracting word senses of adjacent front and back word groups of the marked second word, intelligently matching the second word according to the word senses of the adjacent front and back word groups, and selecting the best word sense matched with the word senses of the adjacent front and back word groups from a plurality of self word senses of the second word;
combining all the single word senses and the optimal word sense in sequence to obtain text translation;
fuzzy matching is carried out on the text translation and preset feature keywords, and whether the feature keywords exist in the text translation or not is judged;
if not, storing the incoming call content of the client into a first storage database;
if yes, calibrating the characteristic keywords to obtain calibrated keywords;
classifying the calibration keywords, calculating the similarity degree of each keyword in each classification with a preset classification keyword, and eliminating the calibration keywords with the similarity degree smaller than a preset threshold value to obtain a first keyword group of each classification;
calculating the weighted value of each classified first key phrase in the total key phrases, selecting a preset classified key word corresponding to the phrase with the largest weighted value as the final appeal of the client, and selecting a corresponding service type in the service directory according to the final appeal of the client.
In this embodiment, text conversion converts telephone content to text information; the word segmentation processing is to split the converted text information into word groups; the first phrase to be processed is a phrase for splitting the text information; the word meaning conversion model is a pre-trained model which can acquire the word meaning of an input phrase; the first word is a word with only one word meaning; the second word is a word with a plurality of word senses; the optimal sense is the most suitable sense of the selected sense; fuzzy matching is a matching method suitable for the condition that a database is incompletely matched with a text; the characteristic keywords are preset characteristic words of each service type.
The beneficial effect of above-mentioned scheme: the method comprises the steps of performing text conversion on the content of the incoming call of the client, performing word segmentation processing, calibrating words with multiple meanings, selecting the best meaning according to the meanings of the front and the back to obtain text translation, extracting the characteristic keywords, improving the accuracy, determining the final appeal of the user according to the weight values of the characteristic keywords, further reducing errors and ensuring the accuracy of matched services.
Example 6:
based on the embodiment 1, in the process of creating the first work order by matching the corresponding flow for the service type based on the work order triggering system, the method further includes:
obtaining sub-services based on the selected service types, extracting flow information corresponding to the sub-services from a service database, dividing the flow information into a plurality of sub-flows, and presetting pre-cut-in information cut-in corresponding to each sub-flow according to the content of each sub-flow;
analyzing the appeal of the client, and judging whether a preposed cut-in instruction related to each sub-process exists or not;
if not, starting service from the initial sub-process in the sub-service;
if yes, determining a sub-process corresponding to the maximum direction of the preposed cut-in instruction as a first input sub-process, and judging whether the service content corresponding to the first input process is consistent with the maximum intention service content corresponding to the appeal of the client or not based on an artificial intelligence technology;
if not, analyzing service contents corresponding to the customer complaints, acquiring a first sub-process service sequence, acquiring a corresponding most sub-process according to the maximum intention service contents, adjusting the first sub-process service sequence to acquire a second sub-process service sequence, and serving the complaints according to the second sub-process service sequence;
and if so, taking the first input sub-process as an initial service point of the appeal.
In this embodiment, the process information is complete process information corresponding to the matched service, and the process information includes, for example, each split sub-process; the pre-cut-in information is a precondition for cutting in the sub-process, and can also be regarded as a cut-in trigger point for cutting in the sub-process; the sub-process is a plurality of sub-processes obtained by splitting the process information, for example, splitting the process information a into sub-processes a1, a2, a3 and the like; the first input process is a first switching sub-process; the initial service point is the service starting point requested at this time; the maximum intention is the intention with the maximum probability, namely the intention is the sub-process service which is needed by the customer most; the maximum direction is the maximum direction which is the most matched with the sub-process service after the analysis of the preposed cut-in instruction is carried out; the first sub-process service sequence is a service sequence based on the first sub-process service, and can be a service selected according to the appeal of a client, and the service has a default sub-process service sequence; the second sub-process service sequence is a service sequence of a second sub-process obtained by adjusting the most sub-process corresponding to the most intented service content to the first service sequence and sequencing the rest according to the first sub-process service sequence;
the beneficial effect of above-mentioned scheme: the matched sub-services can be split, the precondition information is preset according to each sub-process, the precondition information is searched according to the client appeal information, the service initial point can be directly determined from the client appeal, redundant steps are reduced, waste of human resources is avoided, and the approval speed is increased.
Example 7:
based on embodiment 3, as shown in fig. 2, the predicting a possible problem in the automobile retail process according to the customer appeal further includes:
step S201: searching for fault information of sold vehicle types according to the appeal of the client, integrating and classifying the searched fault information, and acquiring various types of fault information of various vehicle types;
step S202: extracting all hardware fault information as overall fault information, classifying all hardware fault information according to each hardware type, calculating the proportion of each type of hardware fault information in the vehicle type overall fault information, and acquiring the fault condition of each part in the proportion fault type;
step S203: calculating the failure rate of each part according to the failure condition of each part, sequencing each part according to the failure rate of each part, reminding a maintenance department to store accessories correspondingly according to the sequencing sequence, and calculating the average service life of each part according to failure information;
step S204: based on the customer file, calculating the service life of each part of the vehicle based on the vehicle maintenance and repair information, comparing the service life with the average service life of each part, and judging whether the service life of each part of the vehicle is longer than the average service life of the part;
if not, judging that the potential safety hazard of the vehicle is small;
if the part is in the safe state, communication connection is established with the vehicle-mounted machine system through the Internet, prompt voice is sent, and a user is reminded that potential safety hazards of corresponding parts are large, so that checking and maintenance are carried out.
In this embodiment, the fault information is vehicle fault condition information; the failure rate is the rate of each part; the average service life is the average service life of each part obtained according to the service life of each part obtained by the fault information when the part fails;
the beneficial effect of above-mentioned scheme: the proportion of various hardware fault information in the vehicle type total fault information can be calculated according to the fault information of the vehicle type to be sold, the average service life of parts is analyzed according to the fault information, the service life of each part of a client vehicle is calculated according to dangerous information in a client file, and when the service life exceeds the average service life, prompt is carried out, so that after-sales caused by part faults can be effectively reduced, after-sales processing can be reduced, and human resources are liberated.
Example 8:
based on embodiment 7, as shown in fig. 3, the establishing a communication connection with the car machine system via the internet includes:
step S301: reading vehicle information according to the client file, acquiring the vehicle machine system terminal ip address, and positioning the vehicle machine system terminal ip address;
step S302: establishing a communication channel between the cloud server and a corresponding vehicle machine system terminal;
step S303: and based on the positioning information, sending a connection request to an area where an ip address of the terminal of the in-vehicle system is located from the cloud server, and transmitting a voice signal to the in-vehicle system based on a communication channel based on the cloud server after the in-vehicle system detects the connection request.
In the embodiment, the ip address of the terminal of the vehicle-mounted machine system is the network ip address of the vehicle-mounted machine system in the vehicle; the cloud server is a server cluster connected by the Internet; the communication channel is a path for data transmission.
The beneficial effect of above-mentioned scheme: according to the scheme, the ip address of the client vehicle machine system terminal can be acquired according to the client file, the vehicle is positioned according to the address and then connected, information is sent, hidden dangers of the client vehicle can be timely reminded, maintenance is needed, vehicle faults can be reduced, and after-sale cost is reduced.
Example 9:
on the basis of embodiment 1, the process of performing corresponding intelligent reminding according to the first work order further includes:
establishing a reminding mechanism according to the work order flow of the first work order, wherein the reminding mechanism comprises a plurality of reminding points, and sending out corresponding short message reminding or intelligent outbound reminding when the reminding points are reached;
if the stop reminding instruction of the corresponding work order processing personnel is not received in the preset time period after the reminding point, the reminding is continued according to the reminding mechanism, and in the reminding process, the signal to noise ratio of the corresponding reminding point for carrying out short message reminding or intelligent outbound reminding in the process of transmitting the reminding signal is calculated, and the method comprises the following steps:
detecting the number of signal nodes passing through in the transmission process of a reminding signal corresponding to the short message reminding or the intelligent outbound reminding and the corresponding transmission bandwidth;
calculating a loss coefficient F in the transmission process of the reminding signal according to the formula:
Figure BDA0003152490540000131
wherein,
Figure BDA0003152490540000132
the power coefficient of a valid signal in the reminding signal is; theta2The power coefficient of the noise signal in the reminding signal is obtained; n is the number of signal nodes passing through the reminding signal in the transmission process, and the number of the nodes is at least 2; kiIs the energy of the ith signal node; k is the average energy of all signal nodes; tau is the signal error coefficient in the transmission process and has the value range of 0.8,1.2]；
Calculating a corresponding signal-to-noise ratio gamma according to a loss coefficient F in the transmission process of the reminding signal, wherein the calculation formula is as follows:
Figure BDA0003152490540000141
wherein, P is the power of the effective signal in the reminding signal; d is the bandwidth of the reminding signal; xi is the unilateral power spectrum density of the noise signal in the transmission process of the reminding signal; upsilon is a measurement error factor;
comparing the obtained signal-to-noise ratio with a preset signal-to-noise ratio threshold, if the obtained signal-to-noise ratio is smaller than the preset signal-to-noise ratio threshold, judging that the transmission is unqualified, enhancing a reminding signal, and if a time point to be transmitted corresponding to the enhanced reminding signal is not overlapped with the next reminding point, transmitting the enhanced reminding signal based on the time point to be transmitted; if the reminding signals coincide with the enhanced reminding signals, the reminding signals corresponding to the subsequent reminding points are updated based on the enhanced reminding signals;
if the corresponding stop reminding instruction is not received after the reminding is finished based on the reminding mechanism, upgrading the second work order into an emergency work order and pushing the emergency work order to other processing personnel for processing;
and the time difference value between the current reminding point and the next reminding point is greater than the time value of the preset time period.
In this embodiment, the reminding point is a preset reminding standard point; the signal-to-noise ratio is the ratio of a signal to noise in the signal transmission process; bandwidth is the amount of data that is used to identify the data transmission capability of a signal transmission, identifying the amount of data that passes through a link per unit of time.
The beneficial effect of above-mentioned scheme: when a stop reminding instruction corresponding to the work order processing personnel is not received within a set time period, reminding is carried out again, the signal to noise ratio of reminding instruction signal transmission is calculated, and maintenance is carried out when the signal to noise ratio is smaller than a preset threshold value, so that the problem that the number of examination and approval work orders is increased due to the influence of signal transmission caused by overhigh signal to noise ratio, and the problem that the examination and approval work orders are too much and untimely due to overhigh task amount is solved.
Example 10:
based on embodiment 4, the calculating a weight value of each classified first keyword group in the total keyword group includes:
acquiring the number of keywords in the first keyword group, and acquiring the number of keywords in the total keyword group;
calculating the weight value alpha of the first key phrase in the total key phrase according to the following formula:
Figure BDA0003152490540000151
wherein Q is the number of the key words of the total key word group; q is the number of the keywords of the first keyword group; lambda is an empirical constant, and the value of lambda is 0.01; e is a natural constant;
meanwhile, preprocessing the remaining various first key phrases to obtain corresponding weight values;
and sequencing all the weight values according to the sizes, and extracting the minimum weight value.
In this embodiment, the weight values are the amount of workload occupied by each item and the degree of importance of affecting the overall capacity, and are respectively the proportion scores specified for each item; the preprocessing is the same processing as the calculation process; the beneficial effect of above-mentioned scheme: the weighted value of each first keyword group is calculated by obtaining the number of the first keyword groups and the number of the total keyword groups, and the weighted values are sequenced, so that subsequent calculation is facilitated, and the approval efficiency is improved.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A method for improving approval efficiency process based on automobile retail is characterized by comprising the following steps:
step 1: inquiring a corresponding client file according to the client incoming call number, determining corresponding vehicle information, and binding the incoming call number, the client file, the vehicle information and a historical work order;
step 2: selecting a corresponding service type from a service directory according to the appeal of a client, matching a corresponding flow for the service type based on a work order triggering system, and creating a first work order;
and step 3: and carrying out corresponding intelligent reminding according to the first work order.
2. The method for improving the approval efficiency process based on the automobile retail sales according to claim 1, wherein the step 1: inquiring a corresponding customer file according to the incoming call number of the customer, and determining corresponding vehicle information, wherein the steps comprise:
determining the vehicle type configuration of the client according to the client file;
judging whether a historical incoming call exists or not according to the customer file;
if so, extracting historical incoming call records, acquiring historical incoming call reasons and results, and integrating the customer vehicle type configuration and the historical incoming call reasons and results to generate vehicle information.
3. The method for improving approval efficiency process based on automobile retail sales of claim 1, wherein the service types comprise: pre-sale service type and post-sale service type;
the pre-sale service type is used for providing commodity knowledge related to the appeal of the client to the client, guiding the client to select and purchase a commodity most suitable for the appeal, and providing a using and maintaining method of the commodity;
the after-sales service is used for predicting possible problems in the automobile retail process according to the appeal of the client and carrying out prediction reminding on the possible problems;
wherein the commodity is associated with an automobile.
4. The method for improving the approval efficiency process based on the automobile retail sales according to claim 1, wherein the step 1: before inquiring the corresponding customer file according to the incoming call number of the customer, the method comprises the following steps:
inquiring whether a client file corresponding to the incoming call number exists or not according to the incoming call number of the client;
if yes, extracting corresponding customer files;
if not, calling historical vehicle consultation information of the client and establishing a client file.
5. The method for improving the approval efficiency process based on the automobile retail sales of claim 1, wherein the step 2: according to the appeal of the client, selecting a corresponding service type from a service directory, wherein the service type comprises the following steps:
determining corresponding client incoming call content according to the client appeal, performing text conversion, and performing word segmentation processing on the converted text to obtain a first phrase to be processed;
inputting the first phrase to be processed into a pre-trained word sense conversion model to obtain a plurality of first words with single word senses and a plurality of second words with self word senses, and calibrating the second words;
extracting word senses of adjacent front and back word groups of the marked second word, intelligently matching the second word according to the word senses of the adjacent front and back word groups, and selecting the best word sense matched with the word senses of the adjacent front and back word groups from a plurality of self word senses of the second word;
combining all the single word senses and the optimal word sense in sequence to obtain text translation;
fuzzy matching is carried out on the text translation and preset feature keywords, and whether the feature keywords exist in the text translation or not is judged;
if not, storing the incoming call content of the client into a first storage database;
if yes, calibrating the characteristic keywords to obtain calibrated keywords;
classifying the calibration keywords, calculating the similarity degree of each keyword in each classification with a preset classification keyword, and eliminating the calibration keywords with the similarity degree smaller than a preset threshold value to obtain a first keyword group of each classification;
calculating the weighted value of each classified first key phrase in the total key phrases, selecting a preset classified key word corresponding to the phrase with the largest weighted value as the final appeal of the client, and selecting a corresponding service type in the service directory according to the final appeal of the client.
6. The method for improving the approval efficiency process based on the automobile retail sales of claim 1, wherein the step 2: based on the work order triggering system, matching the corresponding flow for the service type, and in the process of creating the first work order, the method further comprises the following steps:
obtaining sub-services based on the selected service types, extracting flow information corresponding to the sub-services from a service database, dividing the flow information into a plurality of sub-flows, and presetting pre-cut-in information cut-in corresponding to each sub-flow according to the content of each sub-flow;
analyzing the appeal of the client, and judging whether a preposed cut-in instruction related to each sub-process exists or not;
if not, starting service from the initial sub-process in the sub-service;
if yes, determining a sub-process corresponding to the maximum direction of the preposed cut-in instruction as a first input sub-process, and judging whether the service content corresponding to the first input process is consistent with the maximum intention service content corresponding to the appeal of the client or not based on an artificial intelligence technology;
if not, analyzing service contents corresponding to the customer complaints, acquiring a first sub-process service sequence, acquiring a corresponding most sub-process according to the maximum intention service contents, adjusting the first sub-process service sequence to acquire a second sub-process service sequence, and serving the complaints according to the second sub-process service sequence;
and if so, taking the first input sub-process as an initial service point of the appeal.
7. The method of claim 3, wherein predicting possible problems in the automobile retail process based on the customer's appeal comprises:
searching for fault information of sold vehicle types according to the appeal of the client, integrating and classifying the searched fault information, and acquiring various types of fault information of various vehicle types;
extracting all hardware fault information as overall fault information, classifying all hardware fault information according to each hardware type, calculating the proportion of each type of hardware fault information in the vehicle type overall fault information, and acquiring the fault condition of each part in the proportion fault type;
calculating the failure rate of each part according to the failure condition of each part, sequencing each part according to the failure rate of each part, reminding a maintenance department to store accessories correspondingly according to the sequencing sequence, and calculating the average service life of each part according to failure information;
based on the customer file, calculating the service life of each part of the vehicle based on the vehicle maintenance and repair information, comparing the service life with the average service life of each part, and judging whether the service life of each part of the vehicle is longer than the average service life of the part;
if not, judging that the potential safety hazard of the vehicle is small;
if the part is in the safe state, communication connection is established with the vehicle-mounted machine system through the Internet, prompt voice is sent, and a user is reminded that potential safety hazards of corresponding parts are large, so that checking and maintenance are carried out.
8. The method for improving approval efficiency process based on automobile retail sale of claim 7, wherein the establishing communication connection with the car machine system via internet comprises:
reading vehicle information according to the client file, acquiring the vehicle machine system terminal ip address, and positioning the vehicle machine system terminal ip address;
establishing a communication channel between the cloud server and a corresponding vehicle machine system terminal;
and based on the positioning information, sending a connection request to an area where an ip address of the terminal of the in-vehicle system is located from the cloud server, and transmitting a voice signal to the in-vehicle system based on a communication channel based on the cloud server after the in-vehicle system detects the connection request.
9. The method for improving the approval efficiency process based on the automobile retail sales of claim 1, wherein the step 3: according to the first work order, the corresponding intelligent reminding process is carried out, and the method further comprises the following steps:
establishing a reminding mechanism according to the work order flow of the first work order, wherein the reminding mechanism comprises a plurality of reminding points, and sending out corresponding short message reminding or intelligent outbound reminding when the reminding points are reached;
if the stop reminding instruction of the corresponding work order processing personnel is not received in the preset time period after the reminding point, the reminding is continued according to the reminding mechanism, and in the reminding process, the signal to noise ratio of the corresponding reminding point for carrying out short message reminding or intelligent outbound reminding in the process of transmitting the reminding signal is calculated, and the method comprises the following steps:
detecting the number of signal nodes passing through in the transmission process of a reminding signal corresponding to the short message reminding or the intelligent outbound reminding and the corresponding transmission bandwidth;
calculating a loss coefficient F in the transmission process of the reminding signal according to the formula:
Figure FDA0003152490530000041
wherein,
Figure FDA0003152490530000042
the power coefficient of a valid signal in the reminding signal is; theta2The power coefficient of the noise signal in the reminding signal is obtained; n is the number of signal nodes passing through the reminding signal in the transmission process, and the number of the nodes is at least 2; kiIs the energy of the ith signal node; k is the average energy of all signal nodes; tau is the signal error coefficient in the transmission process and has the value range of 0.8,1.2]；
Calculating a corresponding signal-to-noise ratio gamma according to a loss coefficient F in the transmission process of the reminding signal, wherein the calculation formula is as follows:
Figure FDA0003152490530000051
wherein, P is the power of the effective signal in the reminding signal; d is the bandwidth of the reminding signal; xi is the unilateral power spectrum density of the noise signal in the transmission process of the reminding signal; upsilon is a measurement error factor;
comparing the obtained signal-to-noise ratio with a preset signal-to-noise ratio threshold, if the obtained signal-to-noise ratio is smaller than the preset signal-to-noise ratio threshold, judging that the transmission is unqualified, enhancing a reminding signal, and if a time point to be transmitted corresponding to the enhanced reminding signal is not overlapped with the next reminding point, transmitting the enhanced reminding signal based on the time point to be transmitted; if the reminding signals coincide with the enhanced reminding signals, the reminding signals corresponding to the subsequent reminding points are updated based on the enhanced reminding signals;
if the corresponding stop reminding instruction is not received after the reminding is finished based on the reminding mechanism, upgrading the second work order into an emergency work order and pushing the emergency work order to other processing personnel for processing;
and the time difference value between the current reminding point and the next reminding point is greater than the time value of the preset time period.
10. The method of claim 4, wherein the calculating the weight value of each classified first keyword group in the total keyword group comprises:
acquiring the number of keywords in the first keyword group, and acquiring the number of keywords in the total keyword group;
calculating the weight value alpha of the first key phrase in the total key phrase according to the following formula:
Figure FDA0003152490530000052
wherein Q is the number of the key words of the total key word group; q is the number of the keywords of the first keyword group; lambda is an empirical constant, and the value of lambda is 0.01; e is a natural constant;
meanwhile, preprocessing the remaining various first key phrases to obtain corresponding weight values;
and sequencing all the weight values according to the sizes, and extracting the minimum weight value.
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