CN115438270A

CN115438270A - Intelligent equipment information recommendation method, device, equipment and storage medium

Info

Publication number: CN115438270A
Application number: CN202211314991.1A
Authority: CN
Inventors: 周文明; 田志国; 沈世通; 陈军; 冯建设
Original assignee: CITIC Holdings Co Ltd; Shenzhen Xinrun Fulian Digital Technology Co Ltd
Current assignee: CITIC Holdings Co Ltd; Shenzhen Xinrun Fulian Digital Technology Co Ltd
Priority date: 2022-10-26
Filing date: 2022-10-26
Publication date: 2022-12-06
Anticipated expiration: 2042-10-26
Also published as: CN115438270B

Abstract

The application discloses an intelligent equipment information recommendation method, an intelligent equipment information recommendation device, equipment and a storage medium, wherein the intelligent equipment information recommendation method comprises the following steps: acquiring a user tag of a user with information to be recommended; determining recommendation equipment information of a user to be recommended based on a user label and a preset label mapping relation; classifying the recommended equipment information, and transmitting the classified recommended equipment information batches to a user terminal; acquiring demand information of a user; the method comprises the steps of inputting demand information and recommendation equipment information into a preset equipment information recommendation model, and carrying out demand analysis processing on the demand information and the recommendation equipment information based on the equipment information recommendation model to obtain target equipment information. According to the method and the device, the recommendation device information is automatically generated, the target device information is screened out according to the requirement, the user does not need to screen the required device information in a large amount of data, the user terminal can quickly obtain the required device information, the device information obtaining efficiency is improved, and the user experience is improved.

Description

Intelligent equipment information recommendation method, device, equipment and storage medium

Technical Field

The present application relates to the field of computer information processing technologies, and in particular, to an apparatus information intelligent recommendation method, apparatus, device, and storage medium.

Background

With the development of the communication era, factories are gradually intelligentized, and users can obtain required information only by searching in a data center.

However, with the intelligentization of the devices in the factory, the device information becomes diversified and diversified, and the purposes are different, and under the condition that the transmission information is too much, the user needs to screen a large amount of information to obtain the required information, a large amount of time needs to be spent in the process, the efficiency of obtaining the device information is low, and the user experience is poor.

Disclosure of Invention

The application mainly aims to provide an intelligent equipment information recommendation method, device, equipment and storage medium, and aims to solve the technical problem that in the prior art, the user experience is poor due to low efficiency of obtaining equipment information.

In order to achieve the above object, the present application provides an intelligent device information recommendation method, including:

acquiring a user tag of a user with information to be recommended;

determining recommendation equipment information of the information user to be recommended based on the user tags and a preset tag mapping relation, wherein the tag mapping relation is mapping between each user tag and corresponding recommendation equipment information;

classifying the recommended equipment information, and transmitting the classified recommended equipment information batch to a user terminal;

acquiring demand information of a user;

and inputting the demand information and the recommendation equipment information into a preset equipment information recommendation model, and performing demand analysis processing on the demand information and the recommendation equipment information based on the equipment information recommendation model to obtain target equipment information.

Optionally, before the step of obtaining the user tag of the information user to be recommended, the method includes:

acquiring user information and equipment information in an internet of things platform;

calibrating the user information to corresponding user tags;

determining recommended equipment information corresponding to the user tag based on the equipment information;

and establishing a mapping relation between each user tag and the corresponding recommendation equipment information to obtain a tag mapping relation.

Optionally, the step of determining recommended device information corresponding to the user information based on the device information includes:

determining a first feature vector of the device information and a second feature vector of the user information;

calculating feature distances of the first feature vector and the second feature vector;

and determining the equipment information with the characteristic distance smaller than a preset distance threshold value as recommended equipment information of the corresponding user label.

Optionally, the step of classifying the piece of recommended device information and transmitting the classified piece of recommended device information to the user terminal includes:

classifying the recommendation equipment information based on a preset class label of the equipment information to obtain recommendation equipment information of a plurality of class sets;

determining the transmission priority of the recommendation equipment information of each category set;

based on the transmission priority, performing data arrangement on the recommendation device information of each category set to obtain arranged recommendation device information;

and transmitting the arranged recommended equipment information batches to the user terminal based on a preset batch transmission rule.

Optionally, before the step of inputting the requirement information and the recommended device information into a preset device information recommendation model, and performing requirement analysis processing on the requirement information and the recommended device information based on the device information recommendation model to obtain target device information, the method includes:

analyzing the recommendation equipment information into a target file with a preset format;

and determining the corresponding information matrix of the target file.

Optionally, the step of determining the information matrix corresponding to the target file includes:

partitioning the target file based on a preset information partitioning rule to obtain a block of the target file;

determining an information unit in the block and coordinate information of the information unit;

and vectorizing the information units and the coordinate information of the information units, and forming an information matrix corresponding to the target file by the vectorized information units and the coordinate information of the information units.

Optionally, before the step of acquiring the requirement information of the user, the method includes:

acquiring an equipment information set, demand information training data and an equipment information label of the demand information training data;

and performing iterative training on a preset model to be trained based on the equipment information set, the requirement information training data and the equipment information label of the requirement information training data to obtain an equipment information recommendation model meeting the precision condition.

The application also provides an equipment information intelligence recommendation device, equipment information intelligence recommendation device includes:

the tag acquisition module is used for acquiring a user tag of a user with information to be recommended;

the determining module is used for determining recommendation equipment information of the information user to be recommended based on the user tags and a preset tag mapping relation, wherein the tag mapping relation is mapping between each user tag and corresponding recommendation equipment information;

the transmission module is used for classifying the recommended equipment information and transmitting the classified recommended equipment information batches to the user terminal;

the demand information acquisition module is used for acquiring demand information of a user;

and the analysis module is used for inputting the demand information and the recommendation equipment information into a preset equipment information recommendation model, and performing demand analysis processing on the demand information and the recommendation equipment information based on the equipment information recommendation model to obtain target equipment information.

The present application further provides an apparatus for intelligently recommending apparatus for information of an apparatus, the apparatus for intelligently recommending apparatus for information of an apparatus includes: a memory, a processor, and a program stored on the memory for implementing the device information intelligent recommendation method,

the memory is used for storing a program for realizing the intelligent recommendation method of the equipment information;

the processor is used for executing the program for realizing the intelligent recommendation method of the equipment information so as to realize the steps of the intelligent recommendation method of the equipment information.

The application also provides a storage medium, wherein the storage medium stores a program for realizing the intelligent recommendation method of the equipment information, and the program for realizing the intelligent recommendation method of the equipment information is executed by a processor to realize the steps of the intelligent recommendation method of the equipment information.

Compared with the prior art that the efficiency of obtaining the equipment information by the user is low, and the user experience is poor, the equipment information intelligent recommendation method, the equipment information intelligent recommendation device, the equipment and the storage medium provided by the application acquire the user label of the information user to be recommended; determining recommendation equipment information of the information user to be recommended based on the user tags and a preset tag mapping relation, wherein the tag mapping relation is mapping between each user tag and corresponding recommendation equipment information; classifying the recommended equipment information, and transmitting the classified recommended equipment information batch to a user terminal; acquiring demand information of a user; and inputting the demand information and the recommendation equipment information into a preset equipment information recommendation model, and performing demand analysis processing on the demand information and the recommendation equipment information based on the equipment information recommendation model to obtain target equipment information. According to the method and the device, the recommendation device information of the information user to be recommended is generated through the mapping relation between each pre-established user label and the corresponding recommendation device information, the generated recommendation device information is classified and transmitted to the user terminal in batches, the target device information is quickly screened out according to the specific requirements of the device information of the user, the user does not need to screen a large amount of data of an internet of things platform or a data platform to obtain the required device information, the user quickly obtains the required device information at the terminal, the device information obtaining efficiency is improved, and the experience of the user is improved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and, together with the description, serve to explain the principles of the application. In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.

FIG. 1 is a schematic diagram of an apparatus architecture of a hardware operating environment according to an embodiment of the present application;

fig. 2 is a schematic flowchart of a first embodiment of an intelligent recommendation method for device information according to the present application;

FIG. 3 is a schematic system flow diagram illustrating a second embodiment of an intelligent device information recommendation method according to the present application;

fig. 4 is a schematic diagram of a classification module according to a first embodiment of the method for intelligently recommending device information according to the present application.

The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

As shown in fig. 1, fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present application.

The terminal in the embodiment of the application may be a PC, or may be a mobile terminal device having a display function, such as a smart phone, a tablet computer, an e-book reader, an MP3 (Moving Picture Experts Group Audio Layer III, motion video Experts compression standard Audio Layer 3) player, an MP4 (Moving Picture Experts Group Audio Layer IV, motion video Experts compression standard Audio Layer 4) player, a portable computer, or the like.

As shown in fig. 1, the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. The communication bus 1002 is used to implement connection communication among these components. The user interface 1003 may include a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.

Optionally, the terminal may further include a camera, a Radio Frequency (RF) circuit, a sensor, an audio circuit, a WiFi module, and the like. Such as light sensors, motion sensors, and other sensors, among others. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display screen according to the brightness of ambient light, and a proximity sensor that may turn off the display screen and/or the backlight when the mobile terminal is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally, three axes), detect the magnitude and direction of gravity when the mobile terminal is stationary, and can be used for applications (such as horizontal and vertical screen switching, related games, magnetometer attitude calibration), vibration recognition related functions (such as pedometer and tapping) and the like for recognizing the attitude of the mobile terminal; of course, the mobile terminal may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which are not described herein again.

Those skilled in the art will appreciate that the terminal structure shown in fig. 1 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.

As shown in fig. 1, a memory 1005, which is a kind of computer storage medium, may include an operating device, a network communication module, a user interface module, and a device information intelligent recommendation program therein.

In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call the device information intelligent recommendation program stored in the memory 1005.

Referring to fig. 2, an embodiment of the present application provides an intelligent device information recommendation method, where the intelligent device information recommendation method includes:

step S100, obtaining a user tag of a user with information to be recommended;

step S200, determining recommendation equipment information of the information user to be recommended based on the user tags and a preset tag mapping relation, wherein the tag mapping relation is the mapping between each user tag and corresponding recommendation equipment information;

step S300, classifying the recommended equipment information, and transmitting the classified recommended equipment information to a user terminal in batches;

step S400, acquiring the requirement information of the user;

step S500, inputting the requirement information and the recommendation equipment information into a preset equipment information recommendation model, and performing requirement analysis processing on the requirement information and the recommendation equipment information based on the equipment information recommendation model to obtain target equipment information.

In this embodiment, the specific application scenarios may be:

along with the intellectualization of equipment in a factory, equipment information becomes diversified, and uses are different, and under the condition that transmission information is too much, a user needs to screen a large amount of information to obtain required information, a large amount of time needs to be spent in the process, the efficiency of equipment information acquisition is low, and user experience is poor.

The method comprises the following specific steps:

step S100, obtaining a user tag of a user with information to be recommended;

in this embodiment, the method for intelligently recommending the device information is applied to a device information intelligent recommendation apparatus.

In this embodiment, the information user to be recommended is a user who has a need for viewing device information in the internet of things platform, the user tag of the information user to be recommended is a user tag of a report user to be recommended, which corresponds to the user information in the internet of things platform, the user tag is generated according to the user information, the user information includes information such as a user name, a position, task allocation and the like, for example, if the user information includes a position of a user a as a detection engineer, the corresponding user tag includes machine detection, function detection and the like, and the device recommends corresponding device information according to the corresponding user tag.

In this embodiment, the manner for the device to obtain the user tag of the information user to be recommended may be that after the information user to be recommended logs in the interactive interface of the internet of things platform, the device obtains the tag of the corresponding user sent in the database of the internet of things platform; the information user A can also upload a user tag to the device by himself or herself, for example, the user A registers an account A in an Internet of things platform of an X factory, the Internet of things platform stores user information of the account A, when the user A logs in the intelligent equipment information recommendation device, the user information of the user A of the Internet of things platform is sent to the device, and the device marks the user A with an M tag and an N tag according to the user information.

Before the step of obtaining the user tag of the information user to be recommended in the step S100, the method includes the following steps a100-a400:

a100, acquiring user information and equipment information in an Internet of things platform;

in this embodiment, the internet of things platform is an integrated platform integrating capabilities of device management, data security communication, message subscription and the like. Supporting and connecting mass equipment downwards, and collecting equipment data to be cloudy; and a cloud API is provided upwards, and the server can issue the command to the equipment end by calling the cloud API to realize remote control. The device obtains the user information and the equipment information in the Internet of things platform and is used for receiving the user information and the equipment information sent by the Internet of things platform, the user information comprises information such as user names, positions, task allocation and the like, and the equipment information comprises running data, energy consumption data, visual data and the like of each equipment.

Step A200, calibrating the user information to a corresponding user label;

in this embodiment, the apparatus marks the user information with a corresponding user tag, that is, marks a corresponding user tag for each user according to the user information of each user, where, for example, the user information includes the job of the user a as a detection engineer, and the task allocation range is a stamping machine, and the corresponding user tag includes stamping machine detection, stamping machine function detection, and the like.

In this embodiment, if the user information is modified or updated, the device generates a corresponding updated user tag according to the modified or updated user information, and generates corresponding updated recommendation device information according to the updated user tag.

Step A300, determining recommended equipment information corresponding to the user tag based on the equipment information;

in this embodiment, the apparatus determines recommended equipment information corresponding to the user tag based on the equipment information, and since the equipment information includes a large amount of information, the recommended equipment information is recommended according to the tag of each user, for example, if the tag of the user a includes a punch detection and a punch function detection, the recommended equipment information provided by the apparatus to the user a includes operation data of the punch, energy consumption data of the punch, and visual data of the punch.

Specifically, the step a300 includes the following steps a310 to a330:

step A310, determining a first feature vector of the device information and a second feature vector of the user information;

in this embodiment, the apparatus determines the first feature vector of the device information and the second feature vector of the user information, and the apparatus determines the first feature vector of the device information and the second feature vector of the user information by using a corresponding feature extraction method according to information properties of the device information and the user information, and using an image feature extraction method if the device information and the user information are image information, and using a text feature extraction method if the device information and the user information are text information.

Step A320, calculating the feature distance between the first feature vector and the second feature vector;

in this embodiment, the apparatus calculates the feature distances of the first feature vector and the second feature vector, and calculates the feature distances of each feature vector in the first feature vector and each feature vector in the second feature vector in a recursive manner, for example, the first feature vector includes A1 and A2, and the second feature vector includes B1, B2, and B3, and then calculates the feature distances between A1 and B1, B2, and B3, and the feature distances between A2 and B1, B2, and B3, respectively, to obtain 6 feature distance results.

Step a330, determining the device information with the characteristic distance smaller than the preset distance threshold as recommended device information of the corresponding user tag.

In this embodiment, the apparatus determines the device information whose characteristic distance is smaller than a preset distance threshold as recommended device information of a corresponding user tag, where the distance threshold is a self-set threshold range, and if the characteristic distance is smaller than the preset distance threshold, it indicates that the device information conforms to a current user tag, and the device information is determined as recommended device information of the user tag, for example, if the characteristic distances obtained by a second characteristic vector of the user information of the user a and a first characteristic vector of the device information include X1, X2, X3, X4, and X5, where X1 and X3 are smaller than the preset distance threshold, the recommended device information corresponding to the user tag of the user a is device information corresponding to X1 and X3.

Step A400, establishing a mapping relation between each user tag and the corresponding recommendation device information to obtain a tag mapping relation.

In this embodiment, the apparatus establishes a mapping relationship between each user tag and the corresponding piece of recommendation device information to obtain a tag mapping relationship, and the apparatus establishes the mapping relationship between each user tag and the corresponding piece of recommendation device information in such a manner that each user tag is matched with the corresponding piece of recommendation device information to establish the mapping relationship between the user tag and the piece of recommendation device information.

in this embodiment, the tag mapping relationship includes mappings between user tags and corresponding pieces of recommendation device information, and the apparatus obtains the pieces of recommendation device information corresponding to the user tags based on the user tags and a preset tag mapping relationship, for example, the user tag of the user a includes X1 and X2, the tag mapping relationship X1 maps the pieces of recommendation device information Y1, and X2 maps the pieces of recommendation device information Y2, so that the pieces of recommendation device information of the information user to be recommended are Y1 and Y2.

In the embodiment, the recommendation device information of the information user to be recommended is generated through the pre-established mapping relationship between each user tag and the corresponding recommendation device information, the generated recommendation device information is classified and transmitted to the user terminal in batches, the user does not need to screen a large amount of data of an internet of things platform or a data platform to obtain the required device information, the device information obtaining efficiency is improved, and the user experience is improved.

After the step of determining the recommendation device information of the information user to be recommended based on the user tag and the preset tag mapping relationship in the step S200, the method includes the following steps B100-B300:

step B100, encrypting the recommendation device information to obtain encrypted recommendation device information;

in this embodiment, specifically, the method for encrypting the recommendation device information by the apparatus is as follows: firstly, a spectrogram is generated through an import scale. Io. Wavfile library, then, user preset voice is sampled, then, FFT conversion is carried out on the user preset voice signal so as to convert the user preset voice signal into a region graph of an original voice signal, then, the voice signal is converted into a binary system from a decimal system, after the conversion is finished, number iteration is carried out, the number is a preset number interval, and voice encryption is carried out through the method.

Step B200, acquiring a verification signal;

in this embodiment, the verification signal is a user signal received by the terminal, and is used for verifying the identity of the user.

And B300, decrypting the encrypted recommendation device information based on the verification signal.

In this embodiment, encryption is performed when data transmission is performed on the computer end and the mobile phone end, and the data information to be transmitted is opened in a corresponding manner after the data information to be transmitted is transmitted to the mobile end or the computer end.

In the embodiment, the recommendation device information is encrypted, verified and decrypted, and a layman obtains the recommendation device information through the information of the login user, so that the information safety is improved, and the user experience is improved.

Specifically, the step B300 includes the following steps B310 to B330:

step B310, segmenting the voice verification signal to obtain a segmented voice verification signal;

step B320, constructing a preset number of encryption matrixes based on the segmented voice verification signals;

and step B330, decrypting the encrypted recommendation device information based on the encryption matrix.

In this embodiment, the decryption method is as follows: dividing the voice verification signal for each frame into P segments:

where T is the length of the segment and K is the frame pointer, in the algorithm for generating the key signal, since the parameters P and T are necessary, they will be inserted in the header of the encrypted speech data transmitted in a certain format, the original part can be separated by constructing a matrix Ae of underdetermined mixing ratios,

p is a full rank array of P x P, with uniform distribution between pseudo-random productions-1 and 1,

is a standard quantity value of the signal to be measured,

so that the original speech can be well stripped, then a P x 2P dimension underdetermined encryption matrix is constructed by using Kronecker (matrix convolution) to form a matrix A, the secret matrix is also used as a part of the secret key, another form of P x 2P dimension underdetermined secret matrix can be constructed,

where B is a full rank matrix of dimensions P, generated by uniformly distributed pseudo-random numbers between (-1, 1), and C is a matrix of dimensions 1X 2, and is also important for the selection of matrices B and C, but for attacks this is not only known about the key signal

A matrix C is also needed to prevent the key from being cracked, toThis improves the security of the key.

After the step of determining the recommendation device information of the information user to be recommended based on the user tag and the preset tag mapping relationship in the step S200, the method further includes the following steps C100 to C300:

step C100, determining a data signal frequency range of the recommendation device information;

step C200, filtering signals outside the signal frequency range to obtain recommendation equipment information after signal filtering;

in this embodiment, the device adopts the wave filter, rejects unnecessary frequency, and the wave filter can carry out effective filtering to the frequency point of specific frequency in the power cord or the frequency beyond this frequency point, obtains the power signal of a specific frequency, or eliminates the power signal behind a specific frequency, reduces the work content of information receiving terminal, improves information transmission efficiency for the user obtains required equipment information at the terminal fast, has improved the efficiency that equipment information obtained, promotes user's experience and feels.

And step S300, classifying the recommended equipment information, and transmitting the classified recommended equipment information batch to a user terminal.

In this embodiment, because the recommendation device information has a possibility of storing a large amount of information, in order to further improve the efficiency of sending information to the user terminal, the device classifies the recommendation device information, and transmits the classified recommendation device information to the user terminal in batches, so as to prevent the transmission speed from being too slow due to too large data, thereby improving the efficiency of sending information to the user terminal, improving the efficiency of obtaining device information, and improving the experience of a user.

Specifically, the step S300 includes the following steps S310 to S340:

step S310, classifying the recommendation equipment information based on a preset class label of the equipment information to obtain recommendation equipment information of a plurality of class sets;

in this embodiment, the apparatus classifies the recommended device information based on a preset class tag of the device information to obtain recommended device information of a plurality of class sets, for example, the recommended device information includes A1, A2, A3, and A4, where the class tag of A1 and A2 is device energy consumption data, the class tag of A3 is device visual data, and the class tag of A4 is device operation data.

In this embodiment, the apparatus classifies the same type of data, and arranges the data in sequence to obtain the classified data, where the type includes a user type and a data type. For example, in a factory, data types are divided into equipment, energy consumption and vision, a device classifies the data to be transmitted, and the equipment, energy consumption and vision data in the data to be transmitted are classified correspondingly.

In another embodiment, the serial number of the data packet in the recommendation device information is determined; and forwarding the first arrived data packet in the data to be transmitted based on the serial number of the data packet, detecting the later arrived data packet, discarding the data packet which is the same as the first arrived data packet in the later arrived data packet, and obtaining the screened data.

Step S320, determining the transmission priority of the recommendation equipment information of each category set;

in this embodiment, the device determines the transmission priority of the recommendation device information according to the user information, and defines four dimensions of feedback information, which are evaluated for importance, urgency, user tendency, technical processing feasibility, and the like.

Step S330, based on the transmission priority, performing data arrangement on the recommendation equipment information of each category set to obtain arranged recommendation equipment information;

in this embodiment, a numerical value comprehensive comparison is performed, and the numerical value summary is low, which means that a high priority level is processed; the values are summed high, indicating a low priority treatment. And performing data arrangement on the recommendation equipment information of each category set, preventing data intersection and improving data transmission efficiency.

Step S340, transmitting the arranged recommended device information batch to the user terminal based on a preset batch transmission rule.

In this embodiment, the apparatus transmits the arranged pieces of recommended equipment information to the user terminal in batches based on a preset batch transmission rule, where the batch transmission rule includes dividing the pieces of recommended equipment information in each category set into transmission batches according to categories and packet sizes, for example, the arranged pieces of recommended equipment information include A1, A2, B1, B2, and C3, where the packet sizes are 3M, 2M, 4M, 5M, and 10M, the categories A1 and A2 are equipment operation data, B1 and B2 are equipment energy consumption data, C3 is equipment visual data, and the preset data size threshold is 5M, and the transmission batches are A1 and A2, a second batch, B2, a third batch, and C3, a fourth batch.

In this embodiment, the apparatus performs batch division according to the category and the size of the data packet according to the recommended device information, that is, device information classification, after device information classification is completed, part of classified data is directly transmitted in sequence, while unclassified data is transmitted after waiting for classification completion, a timer is added therein, if the time waiting for classification completion exceeds the time threshold of the timer, unclassified data is input to a database of error-reporting data, error-reporting content is imported to an error feedback port, then the error feedback port sends the data to a feedback information analysis end, the feedback information analysis end analyzes feedback of each user, and the feedback information analysis end performs priority arrangement on feedback of the user at first. For error reporting data, a developer regularly checks the error reporting data, and if the error reporting data has the data category label missing problem, the developer marks the corresponding category label on the error reporting data; and if the new category data exists, establishing a corresponding category label.

Step S400, acquiring the requirement information of the user;

in this embodiment, the requirement information is specific information of a certain part required in the recommendation device information after the user receives the recommendation device information at the terminal, for example, the requirement of the user is "energy consumption data of a punch".

In this embodiment, first, the requirement information of the user is obtained, where the method for the apparatus to obtain the requirement information of the user may be:

the method I comprises the following steps: if the voice instruction is detected, extracting voice information in the voice instruction, and analyzing the voice information to obtain target demand information;

the second method comprises the following steps: in this embodiment, an inquiry interface may be further provided, and the target demand information is acquired based on information input on the inquiry interface.

In this embodiment, in the query interface, corresponding requirement information is output, and the requirement information may be one or multiple.

In the step S400, before the step of acquiring the requirement information of the user, the method includes the following steps D100 to D200:

step D100, acquiring an equipment information set, requirement information training data and an equipment information label of the requirement information training data;

in this embodiment, the device information set includes a large amount of device information, which may be all device information in a factory range or all device information in a certain area range; the requirement information training data is data of user requirement information used for training, and the requirement information training data can be characters or voice; the equipment information label of the requirement information training data is a label of equipment information corresponding to the requirement information.

And step D200, performing iterative training on a preset model to be trained on the basis of the equipment information set, the requirement information training data and the equipment information label of the requirement information training data to obtain an equipment information recommendation model meeting the precision condition.

In this embodiment, the device performs iterative training on a preset model to be trained based on the device information set, the demand information training data and the device information label of the demand information training data to obtain a device information recommendation model meeting precision conditions, and the device information recommendation model obtained through the process of big data model training can quickly and accurately find corresponding device information in the device information set according to the demand information of the user, so that the device information acquisition efficiency is improved, and the experience of the user is improved. It should be noted that, if the user does not need to further request the device information after receiving the recommendation device information at the terminal, the request information is absent, and the obtained target information is absent, and this method is applicable to a scene with an excessive data amount, that is, when there is a lot of recommendation device information of the information user to be recommended, further request retrieval is required.

Specifically, the step D200 includes the following steps D210-D240:

step D210, inputting the requirement information and the equipment information set training data into the preset model to be trained to obtain predicted equipment information in the equipment information set;

step D220, performing difference calculation on the predicted equipment information and the equipment information label of the demand information training data to obtain an error result;

step D230, judging whether the error result meets an error standard indicated by a preset error threshold range or not based on the error result;

and D240, if the error result does not meet the error standard indicated by the preset error threshold range, returning to the step of inputting the required information and the equipment information set training data into the preset model to be trained to obtain the predicted equipment information in the equipment information set, and stopping training until the training error result meets the error standard indicated by the preset error threshold range to obtain the delay time prediction model.

In the step S500, before the steps of inputting the requirement information and the recommended device information into a preset device information recommendation model, and performing requirement analysis processing on the requirement information and the recommended device information based on the device information recommendation model to obtain target device information, the method includes the following steps E100 to E200:

step E100, analyzing the recommendation equipment information into a target file with a preset format;

in this embodiment, the apparatus parses the recommendation device information into a target file in a preset format, specifically, the apparatus parses the recommendation device information into a target file in a preset format through open source software, for example, the recommendation device information is a file in a PDF format, that is, a parsing result of each page of content of the PDF is output in a Json format, where each page of the PDF may be Json data, a logical structure of the Json includes blocks (a block) composed of many blocks (each page has multiple blocks), each block composed of one line (row), each line composed of many lines, one line may be represented by a dictionary, keys of the dictionary are composed of dir (directions of text blocks), spans (small blocks or unit units), and the like, each span is composed of multiple spans, that is, a span is a smallest expression unit or unit, where the span has a size (font size), font (font), color (color), text (text), bb (x-y coordinates, and the like.

And E200, determining an information matrix corresponding to the target file.

In this embodiment, the apparatus determines an information matrix corresponding to the target file, where the information matrix finally includes information of each piece of recommended equipment, so that the model can conveniently find corresponding target equipment information in the information matrix according to the demand information.

Specifically, the step E200 includes the following steps E210 to E230:

step E210, partitioning the target file based on a preset information partitioning rule to obtain a block of the target file;

in this embodiment, the apparatus performs partition processing on the target file based on a preset information partition rule to obtain a block of the target file, specifically, the type of the target file is a text type, and uses a content corresponding to one paragraph in a certain page text in the PDF as one block according to the preset information partition rule to perform partition processing on the target file, and if the type of the target file is a table type, performs partition processing on the target file according to a preset table partition rule on a table large unit or an area unit in a certain table in the PDF to obtain each block of the target file in the json format.

Step E220, determining the information units in the blocks and the coordinate information of the information units;

in this embodiment, the apparatus determines an information unit in the block and coordinate information of the information unit, specifically, uses a segment of text, a table, or a video in the block of the target file as an information unit, and records the coordinate information of the information unit in the block, so that the model can find corresponding target device information in the information matrix according to the required information.

And step E230, vectorizing the information units and the coordinate information of the information units, and forming an information matrix corresponding to the target file by the vectorized information units and the coordinate information of the information units.

In this embodiment, the apparatus performs vectorization processing on the information unit and the coordinate information of the information unit, and forms an information matrix corresponding to the target file with the vectorized information unit and the coordinate information of the information unit.

In this embodiment, the device inputs the demand information and the recommendation device information into a preset device information recommendation model, and based on the device information recommendation model, the demand information and the recommendation device information are subjected to demand analysis processing to obtain target device information, and the device information recommendation model obtained through the big data model training process can quickly and accurately find the corresponding device information in the device information set according to the demand information of the user, so that the device information acquisition efficiency is improved, and the experience of the user is improved.

In another embodiment, the apparatus is provided with an error feedback port, and error reporting content and opinion feedback content in the computer side and the mobile side are imported into the error feedback port, the error feedback port sends data to the apparatus for feedback information analysis, the apparatus analyzes feedback of each user, and the feedback information analysis side prioritizes feedback of the users first. Firstly, in the first step, the data push error is a first sequence, the priority level is high, and a numerical value 1 is defined; the data content part error is a second sequence, a first value 2; the user feedback is a third sequence, and a numerical value 3 is defined; secondly, defining four dimensions of feedback information, namely, evaluating importance, urgency, problem solving benefit and technical processing feasibility, wherein a first sequence of each dimension represents a high priority level and defines a numerical value of 1; the second sequence represents the medium priority, defining a value of 2; the third sequence represents a low priority level, defining a value of 3;

in conclusion, the numerical value comprehensive comparison is carried out, the numerical value summary is low, and the high-priority processing is represented; the values are summed high, representing low priority processing.

Specifically, the first sequence of processing: after receiving the first sequence of problems, the feedback information analysis end imports information into the information receiving module and imports user information into the information receiving module together, and then the information receiving module re-sends an instruction to enable the information extraction system to re-extract information required by the user in the Internet of things database according to the information subscribed by the user, and then the information extraction system and the next batch of data are packaged and sent together through the steps;

and a second sequence of processing: comparing part of errors with original data, searching corresponding data in a database, and adding all the front part and the rear part into the repair data to ensure the data consistency for a user to check;

and (3) third sequence treatment: the user feedback can be directly displayed through the feedback information analysis end, and managers directly check the user feedback and modify more feedback.

Based on the first embodiment, the present application further provides another embodiment, and with reference to fig. 3, the device information intelligent recommendation apparatus includes: the system comprises an internet of things information database, an information extraction system, an information classification module, a network information reference module, an encryption terminal, a sending module, a first server, a second server, a mobile terminal, a computer terminal, an error feedback terminal and a feedback information analysis terminal. The Internet of things information base is used for storing required data and storing the data in a classified manner; the information extraction system is used for extracting internal data of the information base of the Internet of things; the information analysis module is used for classifying the collected information and comparing the information, and the network information reference module is connected with the information classification module and is used as one of comparison reference objects; the encryption terminal is used for encrypting the classified data information; the sending module is used for sending the information in the encryption terminal; the first server is used for receiving the information from the sending module; the second server is used for assisting the first server in data processing when the first server is overloaded, and the function of the second server is the same as that of the first server; the computer end and the mobile end are communication data demand ends; the error feedback port is used for a port for the computer end and the mobile end to find data problems and feed back the data problems; the feedback information analysis end is used for specifically analyzing the feedback problem and then guiding the feedback problem into the information classification module.

Specifically, referring to fig. 4, the information classification module includes a data receiving module, a data analysis module, an information data arrangement module, a data type presetting module, a comparison model establishing module, where the data receiving module receives and processes information in the information receiving module, the data analysis module performs category analysis on data in the data receiving module, the data type presetting module stores user information and pushes data according to the requirements of users, the comparison model establishing module establishes a data model by using the data in the data type presetting module in cooperation with the data analysis module and simplifies data required by the users, and the information data arrangement module classifies users of the same data type according to the users and the data types and arranges the users of the same data type in sequence.

Specifically, the method corresponding to the device information intelligent recommendation apparatus includes:

subscribing information by a user;

extracting user information and Internet of things data: firstly, an information base of the internet of things is used for storing required data and storing the data in a classified manner, then an information extraction system is used for extracting internal data of the information base of the internet of things, an information classification module classifies collected information and compares the information, and an information receiving module receives the data from the information extraction system and transmits the data to an information classification module;

and (3) data comparison: the data receiving is to receive and process information in the information receiving module, the data analysis module analyzes the type of data in the data receiving, the data type presetting is to store user information and push the data according to the requirements of users, the comparison model building is to match the data in the data type presetting with the data analysis module to build a data model and simplify the data needed by the users, meanwhile, the network information reference module makes reference for the information classification module, the data analysis module in the information classification module firstly simplifies the data through the comparison model, meanwhile, the network information reference module is used as a secondary reference, the simplified content is reduced in the data analysis module if the coincidence degree of the information content and the network is too high in the reference process, the information data arrangement module is classified according to the user and the data type, and classifies and arranges the users of the same data type in sequence;

data transmission: after data processing is carried out through the information analysis module, data encryption is carried out through the encryption terminal, the encrypted data are transmitted to the sending module, and the encrypted data are transmitted to the first server through the sending module;

data processing: the information receiving end receives information from the sending module, redundant frequencies are removed through the filter, the filter can effectively filter frequency points of specific frequencies in a power line or frequencies except the frequency points to obtain a power signal of the specific frequencies or eliminate an electric 0 source signal after the specific frequencies, the working content of the information receiving end is reduced, the decoding terminal is used for analyzing encrypted data, the information arrangement module is used for comparing the decrypted data to prevent data cross in the data analysis process, the information arrangement module is used for determining, if the cross condition occurs, the information arrangement encryption module conducts encryption according to data of a single user, and the information arrangement encryption module conducts encryption:

and (3) subscription information encryption: the information arrangement encryption module is set according to a password added by a user, the password is voice encryption, a spectrogram is generated through an import script, io and wavfile library at first, then voice is sampled, the sampling frequency is 8000, 2048-point FFT conversion is carried out on a voice signal so as to convert the voice signal into a regional graph of an original voice signal, then the voice signal is converted into a binary system from a decimal system, iteration is carried out after the conversion is finished, the number of times is within the range of 80-90, and the voice encryption is carried out through the method;

and (3) subscription information sending: sending all the subscription information to a mobile terminal or a computer terminal through a batch sending terminal after encryption, and then unlocking the subscription content in a voice decryption mode;

and (3) error feedback: error reporting content and opinion feedback content in a computer terminal and a mobile terminal are led into an error feedback port, then the error feedback port sends data to a feedback information analysis terminal, the feedback information analysis terminal analyzes feedback of each user, and the feedback information analysis terminal firstly performs priority arrangement on the feedback of the user.

In the embodiment, data is analyzed and then arranged, specific distinguishing is carried out according to data types and user groups, then the distinguished data are directly packaged and encrypted in the distinguishing process, the sending modules are sequentially used for transmission, then the server receives the data, the frequency except the data is filtered through the filter, the working efficiency of an information receiving end is increased, namely, a part of data is directly transmitted after the classification is finished, and the data which are not classified can be transmitted after the classification is finished, so that the transmission efficiency is effectively increased;

and the user subscription data is encrypted through the information arrangement encryption module, then is sent to the computer end and the mobile end through the information batch sending module, and then is unlocked through the voice wavelength, so that the privacy of the user is ensured.

Feedback is processed in a grading mode through the error feedback port and the feedback information analysis end, and therefore the error processing efficiency is improved.

The present application further provides an apparatus for intelligently recommending apparatus for information on a device, the apparatus for intelligently recommending apparatus for information on a device includes:

Optionally, the device information intelligent recommendation apparatus further includes:

the information acquisition module is used for acquiring user information and equipment information in the Internet of things platform;

the calibration module is used for calibrating the user information to a corresponding user label;

the recommendation module is used for determining recommendation equipment information corresponding to the user tag based on the equipment information;

and the establishing module is used for establishing a mapping relation between each user tag and the corresponding recommendation equipment information to obtain a tag mapping relation.

Optionally, the recommendation module includes:

a vector determination module for determining a first feature vector of the device information and a second feature vector of the user information;

a calculating module, configured to calculate a feature distance between the first feature vector and the second feature vector;

and the recommended equipment information determining module is used for determining the equipment information of which the characteristic distance is smaller than a preset distance threshold as the recommended equipment information of the corresponding user tag.

Optionally, the transmission module includes:

the classification module is used for classifying the recommendation equipment information based on a preset class label of the equipment information to obtain recommendation equipment information of a plurality of class sets;

the priority determining module is used for determining the transmission priority of the recommendation equipment information of each category set;

the arrangement module is used for carrying out data arrangement on the recommendation equipment information of each category set based on the transmission priority to obtain arranged recommendation equipment information;

and the batch transmission module is used for transmitting the arranged recommended equipment information batches to the user terminal based on a preset batch transmission rule.

Optionally, the apparatus for intelligently recommending device information further includes:

the analysis module is used for analyzing the recommendation equipment information into a target file with a preset format;

and the matrix determining module is used for determining an information matrix corresponding to the target file.

Optionally, the matrix determining module includes:

the partitioning module is used for partitioning the target file based on a preset information partitioning rule to obtain a block of the target file;

the information unit determining module is used for determining the information units in the blocks and the coordinate information of the information units;

and the information matrix composition module is used for vectorizing the information unit and the coordinate information of the information unit and forming an information matrix corresponding to the target file by the vectorized information unit and the coordinate information of the information unit.

the training data acquisition module is used for acquiring an equipment information set, demand information training data and an equipment information label of the demand information training data;

and the training module is used for carrying out iterative training on a preset model to be trained on the basis of the equipment information set, the demand information training data and the equipment information label of the demand information training data to obtain an equipment information recommendation model meeting precision conditions.

The specific implementation manner of the device information intelligent recommendation apparatus of the application is basically the same as that of each embodiment of the device information intelligent recommendation method, and is not described herein again.

Referring to fig. 1, fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present application.

As shown in fig. 1, the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. The communication bus 1002 is used to implement connection communication among these components. The user interface 1003 may include a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001 described previously.

Optionally, the device information intelligent recommendation device may further include a rectangular user interface, a network interface, a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and the like. The rectangular user interface may comprise a Display screen (Display), an input sub-module such as a Keyboard (Keyboard), and the optional rectangular user interface may also comprise a standard wired interface, a wireless interface. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface).

Those skilled in the art will appreciate that the device information intelligent recommendation device structure shown in fig. 1 does not constitute a limitation of the device information intelligent recommendation device, and may include more or less components than those shown, or combine some components, or arrange different components.

As shown in fig. 1, a memory 1005, which is a storage medium, may include an operating system, a network communication module, and a device information intelligent recommendation program. The operating system is a program for managing and controlling hardware and software resources of the device information intelligent recommendation device and supports the running of the device information intelligent recommendation program and other software and/or programs. The network communication module is used for realizing communication among the components in the memory 1005 and with other hardware and software in the intelligent recommendation system for device information.

In the device information intelligent recommendation device shown in fig. 1, the processor 1001 is configured to execute the device information intelligent recommendation program stored in the memory 1005, and implement the steps of the device information intelligent recommendation method described in any one of the above.

The specific implementation of the device information intelligent recommendation device of the present application is basically the same as that of the above device information intelligent recommendation method, and is not described herein again.

The present application also provides a storage medium having a program stored thereon for implementing an intelligent recommendation method for device information, the program being executed by a processor to implement the intelligent recommendation method for device information as follows:

acquiring a user tag of a user with information to be recommended;

classifying the recommended equipment information, and transmitting the classified recommended equipment information to a user terminal in batches;

acquiring demand information of a user;

calibrating the user information to corresponding user tags;

Optionally, the step of classifying the pieces of recommended equipment information and transmitting the classified pieces of recommended equipment information to the user terminal includes:

based on the transmission priority, performing data arrangement on the recommendation equipment information of each category set to obtain arranged recommendation equipment information;

and determining the corresponding information matrix of the target file.

Optionally, before the step of obtaining the requirement information of the user, the method includes:

The specific implementation of the storage medium of the present application is substantially the same as the embodiments of the above-mentioned device information intelligent recommendation method, and is not described herein again.

The application also provides a computer program product comprising a computer program, and the computer program realizes the steps of the above intelligent recommendation method for device information when being executed by a processor.

The specific implementation of the computer program product of the present application is substantially the same as the embodiments of the device information intelligent recommendation method, and is not described herein again.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a component of' 8230; \8230;" does not exclude the presence of another like element in a process, method, article, or apparatus that comprises the element.

The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.

Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method of the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solutions of the present application or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method described in the embodiments of the present application.

The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.

Claims

1. An intelligent equipment information recommendation method is characterized by comprising the following steps:

acquiring a user tag of a user with information to be recommended;

acquiring demand information of a user;

2. The intelligent device information recommendation method according to claim 1, wherein before the step of obtaining the user tag of the information user to be recommended, the method comprises:

calibrating the user information to corresponding user tags;

and establishing a mapping relation between each user label and the corresponding recommendation equipment information to obtain a label mapping relation.

3. The method for intelligently recommending device information according to claim 2, wherein the step of determining recommended device information corresponding to the user information based on the device information comprises:

4. The intelligent equipment information recommendation method of claim 1, wherein the step of classifying the recommended equipment information and transmitting the classified recommended equipment information batch to the user terminal comprises:

5. The method for intelligently recommending device information according to claim 1, wherein before the step of inputting the demand information and the recommended device information into a preset device information recommendation model and performing demand analysis processing on the demand information and the recommended device information based on the device information recommendation model to obtain target device information, the method comprises:

and determining the corresponding information matrix of the target file.

6. The method for intelligently recommending device information according to claim 5, wherein said step of determining the corresponding information matrix of said target file comprises:

7. The intelligent device information recommendation method according to claim 1, wherein before the step of obtaining the requirement information of the user, the method comprises:

and performing iterative training on a preset model to be trained based on the equipment information set, the demand information training data and the equipment information label of the demand information training data to obtain an equipment information recommendation model meeting precision conditions.

8. The device information intelligent recommendation device is characterized by comprising:

9. An apparatus information intelligent recommendation apparatus, characterized in that the apparatus information intelligent recommendation apparatus comprises: a memory, a processor, and a program stored on the memory for implementing the device information intelligent recommendation method,

the processor is used for executing a program for implementing the intelligent recommendation method for device information to implement the steps of the intelligent recommendation method for device information according to any one of claims 1 to 7.

10. A storage medium having stored thereon a program for implementing an intelligent device information recommendation method, the program being executed by a processor to implement the steps of the intelligent device information recommendation method according to any one of claims 1 to 7.