Order information processing method and device, storage medium, and computer equipment

Abstract

The present invention discloses a method and device for processing order information, a storage medium, and a computer device, which relate to the field of digital medical technology and are primarily intended to address the low efficiency of existing medical product order processing. The method comprises: obtaining order information for medical products and parsing all fields and product version information in the order information; if the number of fields is greater than a preset field threshold, extracting a target field from the field according to a field interval, and generating field features based on the target field; generating an index identifier based on the order time corresponding to the product version information, the index identifier being used to uniquely point to a feature storage location according to the order time; storing the field features in the corresponding feature storage location according to the index identifier, and marking the order information with the index identifier.

Description

Technical Field

[0001] The present invention relates to the field of digital medical technology, and in particular to a method and device for processing order information, a storage medium, and a computer device. Background Art

[0002] With the rapid development of digital medical technology, ordering medical products based on digital medical systems has become a common means for patients to seek medical treatment or treatment assistance. Therefore, the detailed information generated after placing an order for medical products needs to be accurately stored for query processing.

[0003] At present, the existing storage of medical product order information usually adopts the feature field list. However, when storing according to the extracted feature fields, the feature extraction of large fields will result in the multi-storage of non-differentiated information, which not only wastes more storage space but also reduces the effectiveness of storing medical product order information, thereby affecting the processing efficiency of medical product orders. Summary of the Invention

[0004] In view of this, the present invention provides a method and device for processing order information, a storage medium, and a computer device, the main purpose of which is to solve the problem of low processing efficiency of existing medical product orders.

[0005] According to one aspect of the present invention, a method for processing order information is provided, comprising:

[0006] Obtain order information for medical products and parse all fields in the order information, as well as product version information;

[0007] If the number of the fields is greater than a preset field threshold, extracting a target field from the fields according to the field interval, and generating a field feature through the target field;

[0008] Generate an index identifier based on the order time corresponding to the product version information, the index identifier being used to uniquely point to a feature storage location according to the order time;

[0009] The field features are stored in corresponding feature storage locations according to the index identifier, and the order information is marked with the index identifier.

[0010] Furthermore, extracting the target field from the field according to the field interval includes:

[0011] Determining the length of the field, and determining a field interval matching the length according to a preset field length multiple;

[0012] querying at least one interval field in the fields according to the field interval;

[0013] The interval fields are combined according to the commodity type of the medical commodity to obtain a target field, where the commodity type includes at least one of a manifestation item type, a drug type, and a treatment type.

[0014] Furthermore, before combining the interval fields according to the commodity type of the medical commodity, the method further includes:

[0015] When the interval field is a blank field, the next field corresponding to the blank field is determined as the interval field; or,

[0016] When the interval field is a blank field, the preset field length multiple is adjusted, and the field interval is redetermined according to the adjusted preset field length multiple, so as to query the interval field according to the redetermined field interval.

[0017] Furthermore, generating field features through the target field includes:

[0018] Calculating a hash value of the target field;

[0019] The feature extraction model based on the completed model training is used to extract features from the hash value to obtain field features, and the feature extraction model is obtained by training based on a hash value training sample set with labeled field features.

[0020] Furthermore, generating an index identifier based on the order time corresponding to the product version information includes:

[0021] Obtain all update times corresponding to the product version information, parse and generate time information corresponding to the order information, and determine it as the order time of the product version information;

[0022] Constructing an index position of the order placement time according to the time sequence of the update time, and determining a feature storage location corresponding to the index position;

[0023] An index identifier is generated according to the index position and the feature storage position.

[0024] Furthermore, after parsing all fields in the order information and product version information, the method further includes:

[0025] If the number of the fields is less than or equal to the preset field threshold, a hash value of the field is calculated, and the hash value is bound to the field and stored in a field storage location.

[0026] Furthermore, after storing the field feature in a corresponding feature storage location according to the index identifier and marking the order information with the index identifier, the method further includes:

[0027] When an order information query instruction is received, an index identifier is determined based on the order information carried in the query instruction, and field features are extracted from a corresponding feature storage location according to the index identifier;

[0028] All matched fields are parsed according to the field features and the fields are output.

[0029] According to another aspect of the present invention, there is provided an order information processing device, comprising:

[0030] An acquisition module is used to obtain order information of medical products and parse all fields in the order information, as well as product version information;

[0031] an extraction module, configured to extract a target field from the field according to a field interval if the number of the fields is greater than a preset field threshold, and generate a field feature through the target field;

[0032] A generating module, configured to generate an index identifier based on the order time corresponding to the product version information, wherein the index identifier is configured to uniquely point to a feature storage location according to the order time;

[0033] The storage module is used to store the field feature in the corresponding feature storage location according to the index identifier, and mark the order information with the index identifier.

[0034] Furthermore, the extraction module includes:

[0035] a determining unit, configured to determine the length of the field, and determine a field interval matching the length according to a preset multiple of the field length;

[0036] a query unit, configured to query at least one interval field in the fields according to the field interval;

[0037] The combining unit is used to combine the interval fields according to the commodity type of the medical commodity to obtain a target field, where the commodity type includes at least one of a manifestation item type, a drug type, and a treatment type.

[0038] Furthermore, the determination unit is also used to, when the interval field is a blank field, determine the next field corresponding to the blank field as the interval field; or, when the interval field is a blank field, adjust the preset field length multiple, and redetermine the field interval after adjusting the preset field length multiple, so as to query the interval field according to the redetermined field interval.

[0039] Furthermore, the extraction module further includes:

[0040] A calculation unit, configured to calculate a hash value of the target field;

[0041] An extraction unit is used to extract features from the hash value based on a feature extraction model that has completed model training to obtain field features. The feature extraction model is trained based on a hash value training sample set with labeled field features.

[0042] Furthermore, the generation module includes:

[0043] a parsing unit, configured to obtain all update times corresponding to the product version information, parse and generate time information corresponding to the order information, and determine the time when the order was placed for the product version information;

[0044] A construction unit, configured to construct an index position of the order placement time according to the time sequence of the update time, and determine a feature storage position corresponding to the index position;

[0045] A generating unit is configured to generate an index identifier according to the index position and the feature storage position.

[0046] Furthermore,

[0047] The storage module is further configured to calculate a hash value of the field if the number of the fields is less than or equal to a preset field threshold, and bind the hash value to the field and store the result in a field storage location.

[0048] Furthermore, the device further comprises:

[0049] an extraction module configured to, upon receiving an order information query instruction, determine an index identifier based on the order information carried in the query instruction, and extract field features from a corresponding feature storage location according to the index identifier;

[0050] The output module is used to parse all matched fields according to the field characteristics and output the fields.

[0051] According to another aspect of the present invention, a storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a processor to execute an operation corresponding to the above-mentioned method for processing order information.

[0052] According to another aspect of the present invention, there is provided a computer device comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0053] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute an operation corresponding to the above-mentioned order information processing method.

[0054] By means of the above technical solution, the technical solution provided by the embodiment of the present invention has at least the following advantages:

[0055] The present invention provides a method and device for processing order information, a storage medium, and a computer device. Compared with the prior art, an embodiment of the present invention obtains order information of medical products and parses all fields in the order information and product version information; if the number of the fields is greater than a preset field threshold, a target field is extracted from the field according to the field interval, and a field feature is generated through the target field; an index identifier is generated based on the order time corresponding to the product version information, and the index identifier is used to uniquely point to a feature storage location according to the order time; the field feature is stored in the corresponding feature storage location according to the index identifier, and the order information is marked with the index identifier, so that the repeated storage of non-differentiated information can be reduced without distinguishing non-differentiated information in the order information, and the feature fields are stored in a fragmented form, saving system storage resources, improving the storage accuracy of the feature fields of the order information, and improving the query speed of the order information, thereby improving the processing efficiency of medical product orders.

[0056] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0058] Figure 1 A flow chart of a method for processing order information provided by an embodiment of the present invention is shown;

[0059] Figure 2 A flow chart showing another method for processing order information provided by an embodiment of the present invention is shown;

[0060] Figure 3 A flow chart showing another method for processing order information provided by an embodiment of the present invention is shown;

[0061] Figure 4 A block diagram showing the composition of an order information processing device provided by an embodiment of the present invention is shown;

[0062] Figure 5 A schematic structural diagram of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0063] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0064] The storage of medical product order information is usually performed using a feature field list. However, when storing according to the extracted feature fields, the feature extraction of large fields may result in the storage of non-differentiated information multiple times, which not only wastes more storage space but also reduces the effectiveness of storing medical product order information, thereby affecting the processing efficiency of medical product orders. The embodiment of the present invention provides a method for processing order information, such as Figure 1 As shown, the method includes:

[0065] 101. Obtain order information for medical products and parse all fields in the order information, as well as product version information.

[0066] In an embodiment of the present invention, the current execution end is the service end of the digital medical system, which generates order information based on the medical products traded by the user on the client, thereby processing the order information of the medical products. Among them, medical products are digital products or services in the medical field provided to users in digital medicine, including but not limited to registration services, medicines, treatment services, examination items, etc., to generate order information for medical products based on the completion of the user's order. At this time, the order information includes but is not limited to order time, transaction amount, user information, medical product information, etc. At the same time, since the order information in the digital medical system is generated in the form of fields, the current execution end parses all fields in the order information and determines the product version information according to the medical product information of the completed order. For example, after the CT examination order is completed, all fields in the CT examination order information are parsed and determined, and the product version information is determined to be the product CT examination version 1.0 according to the product details information of the CT examination of the completed order. The embodiment of the present invention does not make specific limitations.

[0067] It should be noted that the current executing entity, as the digital medical service provider, provides different medical product information to the user's client so that the user can place an order for the medical product information through the client.

[0068] 102. If the number of the fields is greater than a preset field threshold, extract a target field from the fields according to the field interval, and generate a field feature through the target field.

[0069] In this embodiment of the present invention, to efficiently store fields, after obtaining all fields, a determination is first made as to whether they are large fields, that is, whether the number of fields exceeds a preset field threshold, to determine whether to execute the method in step 102. The preset field threshold is preconfigured and may be 100, 200, etc., and is not specifically limited in this embodiment of the present invention. Furthermore, target fields are extracted from the fields belonging to the large field according to the field interval to generate field features. In this case, the field interval is determined based on the length of the field. After extracting the target field, the field features of the target field can be determined using a machine learning algorithm, and this is not specifically limited in this embodiment of the present invention.

[0070] For example, if the number of fields is greater than 100, the field interval is determined to be 10, that is, a field is taken every 10 words, thereby obtaining all the target fields.

[0071] 103. Generate an index identifier based on the order time corresponding to the product version information.

[0072] In an embodiment of the present invention, the order time corresponding to the product version information is obtained from the order information. In this case, an index identifier is generated based on the order time, so that the field features stored based on the index identifier have product relevance. The index identifier is used to uniquely point to a feature storage location based on the order time. That is, each index identifier points to a feature storage location corresponding to an order time. When the field features are stored, the storage location is determined based on the time attribute, thereby improving the accuracy of searches after storage.

[0073] It should be noted that, in the embodiment of the present invention, the characteristic storage location is a virtual unit with a storage identifier obtained by dividing a database into multiple storage locations. Since the order time of a product version information corresponds to an index identifier and a characteristic storage location, a small amount of data is stored in the virtual storage location corresponding to the same order time, thereby fragmenting the storage database, greatly improving the accuracy of data storage, and reducing the search pressure of the system during query.

[0074] 104. Store the field feature in a corresponding feature storage location according to the index identifier, and mark the order information with the index identifier.

[0075] In this embodiment of the present invention, after generating an index identifier, the generated field features are stored in the corresponding feature storage location, so that the fragmented database only stores the field content after feature extraction, greatly reducing system storage pressure. At the same time, the order information is marked to identify the index location of this order information, so that reverse queries based on the index identifier in this embodiment of the present invention can be performed, improving the efficiency of querying medical product information.

[0076] In another embodiment of the present invention, for further explanation and limitation, as Figure 2 As shown, the step of extracting the target field from the field according to the field interval includes:

[0077] 201. Determine the length of the field, and determine a field interval matching the length according to a preset field length multiple;

[0078] 202. Query at least one interval field in the fields according to the field interval;

[0079] 203. Combine the interval fields according to the product type of the medical product to obtain a target field.

[0080] In an embodiment of the present invention, in order to reduce the storage space of field features of large fields and reduce the complexity of feature extraction, the target field is extracted from the field according to the field interval. Specifically, the length of the field is determined, and the field interval is determined according to a preset field length multiple of this length. The preset field multiple is a pre-configured half, one-third, etc., so that the interval field is smaller than the field length. The embodiment of the present invention does not make specific restrictions. After determining the field interval, multiple interval fields are queried from the field and combined according to the product type of medical products to obtain the target field.

[0081] It should be noted that, since different types of medical products have different field contents, after extraction, in order to unify the fields, they are combined according to the product type of the medical product, where the product type includes at least one of the project type, drug type, and treatment type. Specifically, if it is a project type, the text field text is first spliced, then the numerical field int is spliced, and finally the remaining other types are spliced ​​in the order of extraction to obtain the target field; if it is a drug type, the numerical field int is first spliced, then the text field text is spliced, and finally the remaining other types are spliced ​​in the order of extraction to obtain the target field; if it is a treatment type, the other fields that do not belong to the text field text and the numerical field int are first spliced, and then the text field text and the numerical field int are spliced ​​to obtain the target field, thereby performing feature extraction based on the target field.

[0082] In another embodiment of the present invention, for further explanation and limitation, before the step of grouping the interval fields according to the commodity type of the medical commodity, the method further includes:

[0083] When the interval field is a blank field, the next field corresponding to the blank field is determined as the interval field; or,

[0084] When the interval field is a blank field, the preset field length multiple is adjusted, and the field interval is redetermined according to the adjusted preset field length multiple, so as to query the interval field according to the redetermined field interval.

[0085] In an embodiment of the present invention, to avoid extracting invalid fields, when extracting interval fields, the interval fields are each determined to be blank. If they are blank, the interval fields need to be redefined. Specifically, in one implementation scenario, if the interval field is blank, the next field relative to the blank field is determined as the interval field. If the next field is still blank, the next field is determined as the interval field, and so on until the interval field is not blank. In another implementation scenario, the preset field length multiple is readjusted to redefine the field interval according to the adjusted preset field length multiple, and the interval field is extracted. Since determining that the interval field is blank indicates that there are useless blank fields in the field, adjusting the preset field length multiple means reducing the preset field length multiple. For example, the original preset field length multiple can be reduced from one-third to one-quarter, thereby reducing the extraction frequency and improving the efficiency of extracting interval fields.

[0086] In another embodiment of the present invention, for further illustration and limitation, the step of generating a field feature from the target field includes:

[0087] Calculating a hash value of the target field;

[0088] Perform feature extraction on the hash value based on the feature extraction model for which model training has been completed to obtain field features.

[0089] In an embodiment of the present invention, in order to accurately generate field features, the hash value of the target field is first calculated by a hash function, and the calculated hash value is subjected to feature extraction based on a feature extraction model to obtain field features. The feature extraction model is obtained by training based on a hash value training sample set of labeled field features, and the feature extraction model can be a character-level N-gram feature extraction model, so that after the model training is performed based on the hash value training sample set of labeled field features, field features are extracted based on the trained feature extraction model. In addition, the hash value of the labeled field feature can be manually labeled or labeled based on a specific labeling algorithm, which is not specifically limited in the embodiment of the present invention.

[0090] In another embodiment of the present invention, for further explanation and limitation, as Figure 3 As shown, the steps of generating an index identifier based on the order time corresponding to the product version information include:

[0091] 301. Obtain all update times corresponding to the product version information, parse and generate time information corresponding to the order information, and determine it as the order time of the product version information;

[0092] 302. Construct an index position of the order placement time according to the time sequence of the update time, and determine a feature storage location corresponding to the index position;

[0093] 303. Generate an index identifier according to the index position and the feature storage position.

[0094] In an embodiment of the present invention, in order to accurately generate an index identifier so as to quickly search during query, specifically, all update times corresponding to the product version information are first obtained, so as to determine the time information of the generated order information as the order time within the update time range. Specifically, since the specific update times corresponding to different product version information are different, the index position of the order time is constructed in the time sequence of the update time. For example, the times for product version updates are update time 1, update time 2, and update time 3 respectively. If the order time is between update time 2 and update time 3, the index position for the order is constructed as "2-", 2 is used to represent the second product version, and - is used to connect the specific feature storage position, that is, the feature storage position corresponding to the index position can include any one of the 0-10 storage units. At this time, it can be sorted and stored according to the storage time. For example, if the feature field corresponding to 3 order times has been stored in index position 2, then the feature storage position is 4, that is, the index identifier "2-4" can be generated.

[0095] In another embodiment of the present invention, for further explanation and limitation, after parsing all fields in the order information and the product version information, the method further includes:

[0096] If the number of the fields is less than or equal to the preset field threshold, a hash value of the field is calculated, and the hash value is bound to the field and stored in a field storage location.

[0097] In an embodiment of the present invention, to achieve the storage effect of small fields, when it is detected that the number of fields is less than or equal to a preset field threshold, the field is determined to be a small field. At this time, a hash value of the field is calculated, and this hash value is bound to the field and stored in a field storage location. In the database of the current execution end, a dedicated field storage location is created for the hash value corresponding to the small field, and the field is stored after the field and hash value are bound together, greatly improving storage efficiency and reducing storage system consumption.

[0098] In another embodiment of the present invention, for further explanation and limitation, after the step of storing the field feature in the corresponding feature storage location according to the index identifier and marking the order information with the index identifier, the method further includes:

[0099] When an order information query instruction is received, an index identifier is determined based on the order information carried in the query instruction, and field features are extracted from a corresponding feature storage location according to the index identifier;

[0100] All matched fields are parsed according to the field features and the fields are output.

[0101] In an embodiment of the present invention, since the order information is marked with an index identifier, when a query instruction for the order information is received, the index identifier is determined based on the carried order information. For example, for an order with an order number of 12434235, the index identifier 4-34 marked with this order number is queried, so that the field features can be extracted from the 34th feature storage location corresponding to the product version 4 according to the index identifier, and all field contents can be obtained based on the field feature analysis and output to complete the query of the order information, thereby greatly improving the query efficiency.

[0102] An embodiment of the present invention provides a method for processing order information. Compared with the prior art, the embodiment of the present invention obtains order information of medical products and parses all fields in the order information and product version information; if the number of the fields is greater than a preset field threshold, the target field is extracted from the field according to the field interval, and field features are generated through the target field; an index identifier is generated based on the order time corresponding to the product version information, and the index identifier is used to uniquely point to a feature storage location according to the order time; the field feature is stored in the corresponding feature storage location according to the index identifier, and the order information is marked with the index identifier, so that the repeated storage of non-differentiated information can be reduced without distinguishing non-differentiated information in the order information, and the feature fields are stored in a fragmented form, which saves system storage resources, improves the storage accuracy of the feature fields of the order information, and improves the query speed of the order information, thereby improving the processing efficiency of medical product orders.

[0103] Furthermore, as a response to the above Figure 1 The embodiment of the present invention provides a device for processing order information, such as Figure 4 As shown, the device includes:

[0104] Acquisition module 41, used to obtain order information of medical products and parse all fields in the order information and product version information;

[0105] An extraction module 42 is configured to extract target fields from the fields according to field intervals if the number of the fields is greater than a preset field threshold, and generate field features using the target fields;

[0106] A generating module 43 is configured to generate an index identifier based on the order time corresponding to the product version information, wherein the index identifier is configured to uniquely point to a feature storage location according to the order time;

[0107] The storage module 44 is configured to store the field feature in a corresponding feature storage location according to the index identifier, and mark the order information with the index identifier.

[0108] Furthermore, the extraction module includes:

[0109] a determining unit, configured to determine the length of the field, and determine a field interval matching the length according to a preset multiple of the field length;

[0110] a query unit, configured to query at least one interval field in the fields according to the field interval;

[0111] The combining unit is used to combine the interval fields according to the commodity type of the medical commodity to obtain a target field, where the commodity type includes at least one of a manifestation item type, a drug type, and a treatment type.

[0112] Furthermore, the determination unit is also used to, when the interval field is a blank field, determine the next field corresponding to the blank field as the interval field; or, when the interval field is a blank field, adjust the preset field length multiple, and redetermine the field interval after adjusting the preset field length multiple, so as to query the interval field according to the redetermined field interval.

[0113] Furthermore, the extraction module further includes:

[0114] A calculation unit, configured to calculate a hash value of the target field;

[0115] An extraction unit is used to extract features from the hash value based on a feature extraction model that has completed model training to obtain field features. The feature extraction model is trained based on a hash value training sample set with labeled field features.

[0116] Furthermore, the generation module includes:

[0117] a parsing unit, configured to obtain all update times corresponding to the product version information, parse and generate time information corresponding to the order information, and determine the time when the order was placed for the product version information;

[0118] A construction unit, configured to construct an index position of the order placement time according to the time sequence of the update time, and determine a feature storage position corresponding to the index position;

[0119] A generating unit is configured to generate an index identifier according to the index position and the feature storage position.

[0120] Furthermore,

[0121] The storage module is further configured to calculate a hash value of the field if the number of the fields is less than or equal to a preset field threshold, and bind the hash value to the field and store the result in a field storage location.

[0122] Furthermore, the device further comprises:

[0123] an extraction module configured to, upon receiving an order information query instruction, determine an index identifier based on the order information carried in the query instruction, and extract field features from a corresponding feature storage location according to the index identifier;

[0124] The output module is used to parse all matched fields according to the field characteristics and output the fields.

[0125] An embodiment of the present invention provides an order information processing device. Compared with the prior art, the embodiment of the present invention obtains order information of medical products and parses all fields in the order information and product version information; if the number of the fields is greater than a preset field threshold, the target field is extracted from the field according to the field interval, and field features are generated through the target field; an index identifier is generated based on the order time corresponding to the product version information, and the index identifier is used to uniquely point to a feature storage location according to the order time; the field feature is stored in the corresponding feature storage location according to the index identifier, and the order information is marked with the index identifier, so that the repeated storage of non-differentiated information can be reduced without distinguishing non-differentiated information in the order information, and the feature fields are stored in a fragmented form, which saves system storage resources, improves the storage accuracy of the feature fields of the order information, and improves the query speed of the order information, thereby improving the processing efficiency of medical product orders.

[0126] According to one embodiment of the present invention, a storage medium is provided, wherein the storage medium stores at least one executable instruction. The computer-executable instruction can execute the method for processing order information in any of the above method embodiments.

[0127] Figure 5 A schematic structural diagram of a computer device provided according to an embodiment of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the computer device.

[0128] like Figure 5 As shown, the computer device may include: a processor (processor) 502 , a communication interface (Communications Interface) 504 , a memory (memory) 506 , and a communication bus 508 .

[0129] The processor 502 , the communication interface 504 , and the memory 506 communicate with each other via a communication bus 508 .

[0130] The communication interface 504 is used to communicate with other devices such as clients or other servers.

[0131] The processor 502 is used to execute the program 510, and specifically can execute the relevant steps in the embodiment of the method for processing order information.

[0132] Specifically, the program 510 may include program codes, which include computer operation instructions.

[0133] Processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in a computer device may be of the same type, such as one or more CPUs, or may be of different types, such as one or more CPUs and one or more ASICs.

[0134] The memory 506 is used to store the program 510. The memory 506 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0135] The program 510 may be specifically configured to enable the processor 502 to perform the following operations:

[0136] Obtain order information for medical products and parse all fields in the order information, as well as product version information;

[0137] If the number of the fields is greater than a preset field threshold, extracting a target field from the fields according to the field interval, and generating a field feature through the target field;

[0138] Generate an index identifier based on the order time corresponding to the product version information, the index identifier being used to uniquely point to a feature storage location according to the order time;

[0139] The field features are stored in corresponding feature storage locations according to the index identifier, and the order information is marked with the index identifier.

[0140] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, centralized on a single computing device, or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. In some cases, the steps shown or described can be performed in a different order than that shown, or can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0141] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for processing order information, characterized in that: include: Obtain order information for medical products and parse all fields in the order information, as well as product version information; If the number of the fields is greater than a preset field threshold, extracting a target field from the fields according to the field interval, and generating a field feature through the target field; Generate an index identifier based on the order time corresponding to the product version information, the index identifier being used to uniquely point to a feature storage location according to the order time; Storing the field feature in a corresponding feature storage location according to the index identifier, and marking the order information with the index identifier; Extracting the target field from the field according to the field interval includes: Determining a field length of the field, and determining a field interval matching the field length according to a preset field length multiple; querying at least one interval field in the fields according to the field interval; The interval fields are combined according to the commodity type of the medical commodity to obtain a target field, where the commodity type includes at least one of a manifestation item type, a drug type, and a treatment type.

2. The method according to claim 1, characterized in that Before combining the interval fields according to the commodity type of the medical commodity, the method further includes: When the interval field is a blank field, the next field corresponding to the blank field is determined as the interval field; or, When the interval field is a blank field, the preset field length multiple is adjusted, and the field interval is redetermined according to the adjusted preset field length multiple, so as to query the interval field according to the redetermined field interval.

3. The method according to claim 1, characterized in that Generating a field feature through the target field includes: Calculating a hash value of the target field; The feature extraction model based on the completed model training is used to extract features from the hash value to obtain field features, and the feature extraction model is obtained by training based on a hash value training sample set with labeled field features.

4. The method according to claim 1, wherein Generating an index identifier based on the order time corresponding to the product version information includes: Obtain all update times corresponding to the product version information, parse and generate time information corresponding to the order information, and determine it as the order time of the product version information; Constructing an index position of the order placement time according to the time sequence of the update time, and determining a feature storage location corresponding to the index position; An index identifier is generated according to the index position and the feature storage position.

5. The method according to claim 1, wherein After parsing all fields in the order information and product version information, the method further includes: If the number of the fields is less than or equal to the preset field threshold, a hash value of the field is calculated, and the hash value is bound to the field and stored in a field storage location.

6. The method according to any one of claims 1 to 5, characterized in that After storing the field feature in the corresponding feature storage location according to the index identifier and marking the order information with the index identifier, the method further includes: When an order information query instruction is received, an index identifier is determined based on the order information carried in the query instruction, and field features are extracted from a corresponding feature storage location according to the index identifier; All matched fields are parsed according to the field features and the fields are output.

7. A device for processing order information, characterized in that: include: An acquisition module is used to obtain order information of medical products and parse all fields in the order information, as well as product version information; an extraction module, configured to extract a target field from the field according to a field interval if the number of the fields is greater than a preset field threshold, and generate a field feature through the target field; A generating module, configured to generate an index identifier based on the order time corresponding to the product version information, wherein the index identifier is configured to uniquely point to a feature storage location according to the order time; A storage module, configured to store the field feature in a corresponding feature storage location according to the index identifier, and mark the order information with the index identifier; The extraction module includes: a determining unit, configured to determine a field length of the field, and determine a field interval matching the field length according to a preset multiple of the field length; a query unit, configured to query at least one interval field in the fields according to the field interval; The combining unit is used to combine the interval fields according to the commodity type of the medical commodity to obtain a target field, where the commodity type includes at least one of a manifestation item type, a drug type, and a treatment type.

8. A storage medium storing at least one executable instruction, wherein the executable instruction enables a processor to execute an operation corresponding to the method for processing order information according to any one of claims 1 to 6.

9. A computer device comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute an operation corresponding to the order information processing method according to any one of claims 1 to 6.

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