CN112800765A - Automatic work order generation method - Google Patents
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Abstract
The invention discloses an automatic work order generation method, which comprises the steps of inputting a work order and extracting entity words; error correction is carried out on the entity words extracted from the work order fields by adopting an error correction algorithm based on a knowledge base; the knowledge base comprises knowledge points and basic knowledge points generated by the system platform in use; error correction is carried out on the entity words extracted from the work order field, and further homophonic error correction, Pinyin flat tongue-curled tongue corresponding relation error correction and nasal sound error correction are carried out; thirdly, the method comprises the following steps: according to a preset work order mapping rule, automatically mapping to generate different types of work orders; the work order processing system informs a dispatcher through a message center, the dispatcher audits the work order, scores the generated work order according to a preset rule, and if the score is lower than a preset value, the dispatcher corrects the work order by listening to a recording recorded by the work order; and informing the checked work order to the corresponding order taker in a short message and WeChat mode. The invention can realize the voice input and the automatic order dispatching function, thereby saving the labor and time cost.
Description
Technical Field
The invention relates to the technical field of intelligent operation and maintenance management platforms, in particular to an automatic work order generation method.
Background
With the iterative upgrade of communication technology and the rapid development of artificial intelligence technology, the BIM, GIS and Internet of things technology has been widely applied to large-scale infrastructure projects. The mode of unified management of the client operation logistics of hospitals and universities is generally that a logistics intelligent operation and maintenance management platform is adopted, real-time operation sampling data of field equipment verification is used as a basis, the energy utilization efficiency is improved, and the logistics work is uniformly allocated and managed. In the operation of back office wisdom operation and maintenance management platform, not only the operation and maintenance management to equipment, work order type wherein contains to report for repair, transport, patrols and examines, still supports to dispose other business types, considers that back office staff is many older, and is not too much understood things such as program application, cell-phone input, uses relatively difficultly, and pronunciation are reported for repair and can be reduced the system and use the degree of difficulty. The traditional work order is generally submitted by filling in the work order through a web page and an app or produced by manually inputting the work order into a system after being sent to a call center through a telephone. The methods have the problems of low efficiency, synchronous work of personnel in the call center and very large workload. The work order automatic generation method is needed, so that the work efficiency is greatly improved when the work order automatic generation method is used, and the work pressure of call center personnel is greatly reduced due to asynchronous auditing of the call center personnel.
Disclosure of Invention
1. The technical problem to be solved is as follows:
aiming at the technical problems, the invention provides an automatic work order generation method, which solves the work order management problem in a logistics intelligent operation and maintenance management platform and improves the work order generation efficiency and accuracy.
2. The technical scheme is as follows:
an automatic work order generation method is characterized in that: the method comprises the following steps:
the method comprises the following steps: inputting and extracting entity words by a work order; the work order recording personnel records the work order information in a voice or text mode, and the work order processing system receives and stores the work order information; the work order entry personnel comprise logistics repair personnel and staff; if the work order is in a voice form, the voice interface of the work order processing system automatically converts the voice into characters; performing part-of-speech tagging and named entity recognition on work order contents expressed by characters, and extracting entity words according to the part-of-speech; the voice interface is a WeChat voice or science news communication voice interface; the part-of-speech tagging is to judge whether the work order content has related or similar nouns according to a specific field preset in a knowledge base; the named entity is identified as a word segmenter which distinguishes whether the noun marked by the part of speech is a person, an object or a place according to the part of speech.
Step two: error correction of the work order field; error correction is carried out on the entity words extracted from the work order fields by adopting an error correction algorithm based on a knowledge base; the knowledge base comprises knowledge points and basic knowledge points generated by the system platform in the using process; the system uses the knowledge points of the generated knowledge base to obtain entity words input by a work order and the entity words generated in the using process of the system as voice training corpora to obtain related user information, product information, position information and equipment information in the system; the error correction of the entity words extracted from the phonetic work single segment also comprises homophone error correction, Pinyin flat tongue-curled tongue corresponding relation error correction and nasal sound error correction.
Step three: different types of work orders are automatically generated; the work order types include: reporting a repair work order, a transport work order, a patrol work order and an after-sale operation and maintenance work order; after the work order is generated, the work order processing system informs a dispatcher through a message center, the dispatcher audits the work order, scores the generated work order according to a preset rule, and if the score is lower than a preset value, the dispatcher corrects the work order by listening to a recording recorded by the work order.
Step four: and informing the checked and dispatched work order to the corresponding order taker and the next processing person in a short message and WeChat mode.
Further, the entity words extracted in the step one comprise words of people, telephone, places, departments, objects, units and quantities, and corresponding attributes are given to the work order.
Further, the knowledge point and the basic knowledge point of the knowledge base in the error correction algorithm based on the knowledge base come from a customer information system, and comprise user names, telephones, organizations and department related information in user management; asset name from fixed asset management, location information for space management, and information related to repair person, repair object, repair location, carrier start and destination location, and carrier content of the service center worksheet; from the speech parsing error correction rules. The system information is very accurate linguistic data, so that the natural language processing of the system is more accurate.
Further, the scoring of the generated work order in the third step is specifically as follows: in the I-shaped single-field error correction stage in the step two, if the fruit body word is a word existing in the database, the word is directly used; if the word does not exist, replacing the word according to the score highest by the confusable pronunciation accumulated in the knowledge base, and counting the error rate of the whole text; error rate calculation formula: error rate = number of corrected words/number of full text participles 100%.
Further, the method also comprises the following step five: and optimizing a database, adding 1 point to the word replacement score of the pronunciation which is easy to be confused in the work order which is manually checked and passed, and recording the word replacement score into an error correction score table in a knowledge base to finish one positive feedback.
Further, the automatic generation of different types of work orders in the third step specifically includes: and (4) taking the accumulated work orders as a corpus, generating a classification model through machine learning, and identifying the type of the work orders.
3. Has the advantages that:
(1) the automatic work order generation method provided by the invention is simple, easy to use and high in efficiency through voice input.
(2) The work order is asynchronously audited through the call center before being sent to the submitting person, so that the pressure and the cost can be reduced.
(3) After the knowledge base is loaded, the accuracy of work order text analysis is greatly improved, the natural language processing of a proprietary system higher than the news flyover and Baidu is aimed at, and the accuracy is higher and higher as the use time of the system is advanced and the more the knowledge base is accumulated.
(4) The invention adopts the knowledge base to correct errors, can realize the recognition errors of polyphones, introduces pinyin full spelling check to correct errors, and further improves the accuracy.
(5) The accumulated data can be automatically classified through a machine learning model, and the automatic order dispatching algorithm can realize the functions of voice input and automatic order dispatching, thereby saving labor and time cost.
Drawings
Fig. 1 is an overall block diagram of the logistic intelligent operation and maintenance management platform according to the present invention.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, an automatic generation method of a work order is characterized in that: the method comprises the following steps:
the method comprises the following steps: inputting and extracting entity words by a work order; the work order recording personnel records the work order information in a voice or text mode, and the work order processing system receives and stores the work order information; the work order entry personnel comprise logistics repair personnel and staff; if the work order is in a voice form, the voice interface of the work order processing system automatically converts the voice into characters; performing part-of-speech tagging and named entity recognition on work order contents expressed by characters, and extracting entity words according to the part-of-speech; the voice interface is a WeChat voice or science news communication voice interface; the part-of-speech tagging is to judge whether the work order content has related or similar nouns according to a specific field preset in a knowledge base; the named entity is identified as a word segmenter which distinguishes whether the noun marked by the part of speech is a person, an object or a place according to the part of speech.
Step two: error correction of the work order field; error correction is carried out on the entity words extracted from the work order fields by adopting an error correction algorithm based on a knowledge base; the knowledge base comprises knowledge points and basic knowledge points generated by the system platform in the using process; the system uses the knowledge points of the generated knowledge base to obtain entity words input by a work order and the entity words generated in the using process of the system as voice training corpora to obtain related user information, product information, position information and equipment information in the system; the error correction of the entity words extracted from the phonetic work single segment also comprises homophone error correction, Pinyin flat tongue-curled tongue corresponding relation error correction and nasal sound error correction.
Step three: different types of work orders are automatically generated; the work order types include: reporting a repair work order, a transport work order, a patrol work order and an after-sale operation and maintenance work order; after the work order is generated, the work order processing system informs a dispatcher through a message center, the dispatcher audits the work order, scores the generated work order according to a preset rule, and if the score is lower than a preset value, the dispatcher corrects the work order by listening to a recording recorded by the work order.
Step four: and informing the checked and dispatched work order to the corresponding order taker and the next processing person in a short message and WeChat mode.
Further, the entity words extracted in the step one comprise words of people, telephone, places, departments, objects, units and quantities, and corresponding attributes are given to the work order.
Further, the knowledge point and the basic knowledge point of the knowledge base in the error correction algorithm based on the knowledge base come from a customer information system, and comprise user names, telephones, organizations and department related information in user management; asset name from fixed asset management, location information for space management, and information related to repair person, repair object, repair location, carrier start and destination location, and carrier content of the service center worksheet; from the speech parsing error correction rules. The system information is very accurate linguistic data, so that the natural language processing of the system is more accurate.
Further, the scoring of the generated work order in the third step is specifically as follows: in the I-shaped single-field error correction stage in the step two, if the fruit body word is a word existing in the database, the word is directly used; if the word does not exist, replacing the word according to the score highest by the confusable pronunciation accumulated in the knowledge base, and counting the error rate of the whole text; error rate calculation formula: error rate = number of corrected words/number of full text participles 100%.
Further, the method also comprises the following step five: and optimizing a database, adding 1 point to the word replacement score of the pronunciation which is easy to be confused in the work order which is manually checked and passed, and recording the word replacement score into an error correction score table in a knowledge base to finish one positive feedback.
Further, the automatic generation of different types of work orders in the third step specifically includes: and (4) taking the accumulated work orders as a corpus, generating a classification model through machine learning, and identifying the type of the work orders.
The specific embodiment is as follows:
as shown in fig. 1, fig. 1 includes an overall block diagram of the logistics intelligent operation and maintenance management platform according to the present invention. The figure shows that the logistics intelligent operation and maintenance management platform realizes unified management on assets, spaces, routing inspection, repair reporting, work orders, contracts and the like through the intelligent logistics system, and improves the efficiency level of operation and maintenance management and the standard degree of the operation flow. The efficient management of the work orders in the whole operation and maintenance management platform is the guarantee of the orderly and efficient operation of the whole platform. The automatic generation of the work order comprises a work order processing background; and the work order processing background receives the work order, identifies the content of the work order and transmits the content to related personnel. The work order processing background comprises a system knowledge base, and the system knowledge base comprises two types: one is a knowledge base generated in the using process of the system, such as the name and the telephone of a user, the name of a product, the position of space management and the like; the second type is that the system software has the same basic knowledge base with reference, such as the error correction knowledge base for correcting homophone errors, and the stored information, such as the Pinyin flat tongue-curled tongue corresponding relation, whether the corresponding relation of the nasal sound exists or not, when the system software serves different customers. The knowledge base is classified and learned based on a Bayesian algorithm, so that samples in the knowledge base are continuously trained and updated, and the classified learning based on the Bayesian algorithm is a conventional technical means in the field, and is not described in detail.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. An automatic work order generation method is characterized in that: the method comprises the following steps:
the method comprises the following steps: inputting and extracting entity words by a work order; the work order recording personnel records the work order information in a voice or text mode, and the work order processing system receives and stores the work order information; the work order entry personnel comprise logistics repair personnel and staff; if the work order is in a voice form, the voice interface of the work order processing system automatically converts the voice into characters; performing part-of-speech tagging and named entity recognition on work order contents expressed by characters, and extracting entity words according to the part-of-speech; the voice interface is a WeChat voice or science news communication voice interface; the part-of-speech tagging is to judge whether the work order content has related or similar nouns according to a specific field preset in a knowledge base; the named entity is identified as a word segmenter which distinguishes whether the noun marked by the part of speech is a person, a thing or a place according to the part of speech;
step two: error correction of the work order field; error correction is carried out on the entity words extracted from the work order fields by adopting an error correction algorithm based on a knowledge base; the knowledge base comprises knowledge points and basic knowledge points generated by the system platform in the using process; the system uses the knowledge points of the generated knowledge base to obtain entity words input by a work order and the entity words generated in the using process of the system as voice training corpora to obtain related user information, product information, position information and equipment information in the system; error correction is carried out on the entity words extracted from the phonetic work single segment, and further the error correction of homophone errors, the error correction of Pinyin flat tongue-Vocal correspondence and the error correction of nasal sound are carried out;
step three: different types of work orders are automatically generated; the work order types include: reporting a repair work order, a transport work order, a patrol work order and an after-sale operation and maintenance work order; after the work order is generated, the work order processing system informs a dispatcher through a message center, the dispatcher audits the work order, scores the generated work order according to a preset rule, and if the score is lower than a preset value, the dispatcher corrects the work order by listening to a recording recorded by the work order;
step four: and informing the checked and dispatched work order to the corresponding order taker and the next processing person in a short message and WeChat mode.
2. The method of claim 1, wherein the method further comprises: and the entity words extracted in the step one comprise words of people, telephone, places, departments, objects, units and quantities, and are simultaneously assigned to corresponding attributes of the work order.
3. The method of claim 1, wherein the method further comprises: knowledge points and basic knowledge points of a knowledge base in the error correction algorithm based on the knowledge base come from an information system of a client, and comprise user names, telephones, organizations and department related information in user management; or asset name from fixed asset management, location information of space management, and related information of repair person, repair object, repair location, delivery person, delivery start and destination location, delivery content of the service center worksheet; or from preset speech parsing error correction rules.
4. The method of claim 1, wherein the method further comprises: the scoring of the generated work order in the third step is specifically as follows: in the second step of correcting the work order field, if the extracted entity word is a word existing in the knowledge base, the word is directly used; if the word does not exist, replacing the word according to the score highest by the confusable pronunciation accumulated in the knowledge base, and counting the error rate of the whole text; wherein the error rate calculation formula is: error rate = number of corrected words/number of full text participles 100%.
5. The method of claim 4, wherein the method further comprises: further comprises the following steps: and optimizing a database, adding 1 point to the word replacement score of the pronunciation which is easy to be confused in the work order which is manually checked and passed, and recording the word replacement score into an error correction score table in a knowledge base to finish one positive feedback.
6. The method of claim 1, wherein the method further comprises: the automatic generation of different types of work orders in the third step specifically comprises the following steps: and (4) taking the accumulated work orders as a corpus, generating a classification model through machine learning, and identifying the type of the work orders.
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| CN114118817A (en) * | 2021-11-30 | 2022-03-01 | 济南农村商业银行股份有限公司 | Bank sunshine loan-handling loan examination and dispatching method, device and system |
| CN114218962A (en) * | 2021-12-16 | 2022-03-22 | 哈尔滨工业大学 | Artificial intelligent emergency semantic recognition system and recognition method for solid waste management information |
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