CN115512481B - Server data processing method networked with vending machine - Google Patents
Server data processing method networked with vending machine Download PDFInfo
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Abstract
The invention provides a server data processing method networked with a vending machine, which comprises the following steps: acquiring a first network port of the vending machine and a second network port of the server, and networking the vending machine and the server based on the first network port and the second network port; acquiring operation data of the vending machine, and uploading the operation data to a server based on a networking result; and performing data processing on the operation data based on the server, and generating a data processing report based on the processing result. Through carrying out the networking with vending machine and server, realize carrying out accurate effectual collection to vending machine's operation data, simultaneously, through handling the operation data who gathers to the realization is according to the result of processing to vending machine's running condition effectively holds, has improved data processing's rate of accuracy and efficiency, has also ensured carrying out effectual management to vending machine.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a server data processing method networked with automatic vending machines.
Background
At present, along with the pursuit of people on life quality, more and more automatic vending machines appear in the life of people, and beverage vending machines, box lunch vending machines, medicine vending machines and the like are continuously emerging;
however, with the advent of a large number of vending machines, management of the vending machines is carried out, and the conventional vending machines and the management terminal are independent, that is, the management terminal cannot timely know the current sales condition and the running condition of the vending machines, and needs to periodically patrol by manpower, meanwhile, the running data and the like of the vending machines need to be analyzed manually, so that the data analysis efficiency and the accuracy are greatly reduced, and the management effect of the vending machines is greatly disadvantageous;
accordingly, the present invention provides a server data processing method networked with a vending machine.
Disclosure of Invention
The invention provides a server data processing method networked with an automatic vending machine, which is used for realizing accurate and effective acquisition of operation data of the automatic vending machine by networking the automatic vending machine with a server, and realizing effective grasp of the operation condition of the automatic vending machine according to a processing result by processing the acquired operation data, thereby improving the accuracy and efficiency of data processing and guaranteeing effective management of the automatic vending machine.
The invention provides a server data processing method networked with a vending machine, which comprises the following steps:
step 1: acquiring a first network port of the vending machine and a second network port of the server, and networking the vending machine and the server based on the first network port and the second network port;
step 2: acquiring operation data of the vending machine, and uploading the operation data to a server based on a networking result;
step 3: and performing data processing on the operation data based on the server, and generating a data processing report based on the processing result.
Preferably, in step 1, a method for processing server data networked with a vending machine, acquiring a first network port of the vending machine and a second network port of the server, includes:
acquiring a terminal identifier of the vending machine, matching the terminal identifier with a preset identifier library, and determining a target model of the vending machine based on a matching result;
determining a first network port type of the vending machine based on the target model, determining a first network port parameter of the vending machine based on the first network port type, and determining a first network port of the vending machine based on the first network port parameter;
Acquiring a target message of a server, and determining a network port set in the server based on the target message;
determining an idle network port in the network port set, and determining a port parameter of the idle network port, wherein the idle network port is at least one;
and matching the first network port parameter with the port parameter of the idle network port, and determining a second network port of the server from the idle network ports based on the matching result.
Preferably, in step 1, a method for processing server data networked with a vending machine, wherein the vending machine and the server are networked based on a first network port and a second network port, includes:
acquiring a first network port of the vending machine and a second network port of the server, and controlling the vending machine to send a device networking request to the second network port of the server based on the first network port;
the server analyzes the received equipment networking request, determines the networking authority of the vending machine, matches the networking authority with a preset authority data table, and judges whether the server allows the vending machine to be networked;
if the network connection key is allowed, feeding back the network connection key to the automatic vending machine, and completing the network connection operation of the automatic vending machine and the server based on the network connection key;
Otherwise, rejecting the device networking request sent by the vending machine.
Preferably, a method for processing server data networked with a vending machine, completing networking operation of the vending machine and a server based on a networking key, includes:
acquiring networking results of the vending machine and the server, and determining a wireless transmission path between the vending machine and the server based on the networking results;
the automatic vending machine is controlled to send test data packets to the server based on the wireless transmission path, and receiving information of the server on the test data packets is obtained in real time;
determining transmission performance parameters of the wireless transmission path based on the received information, and comparing the transmission performance parameters with a preset threshold;
if the transmission performance parameter is greater than or equal to a preset threshold value, judging that the wireless transmission path between the vending machine and the server is qualified, and finishing verification of the networking result between the vending machine and the server;
otherwise, the wireless transmission path between the vending machine and the server is judged to be unqualified.
Preferably, a server data processing method networked with a vending machine determines that a wireless transmission path between the vending machine and a server is not qualified, including:
Acquiring target requirements on a wireless transmission path, acquiring receiving information of a server on a test data packet, and determining transmission delay and transmission rate of the wireless transmission path based on the receiving information, wherein the target requirements correspond to the transmission delay and the transmission rate;
determining a difference value between the target requirement and the transmission delay and between the target requirement and the transmission rate;
acquiring a network node in a wireless transmission path, and determining transmission configuration parameters of the network node to a test data packet;
and adjusting the configuration parameters of the network node based on the difference value, and re-determining the transmission performance parameters of the wireless transmission path after adjustment until the transmission performance parameters are greater than or equal to a preset threshold value.
Preferably, in step 2, operation data of the vending machine is obtained, and the operation data is uploaded to the server based on a networking result, including:
acquiring data uploading frequency, and determining a time interval for data acquisition of the vending machine based on the data uploading frequency;
acquiring operation data of the automatic vending machine based on the time interval, and splitting the operation data to obtain M sub-operation data blocks;
Determining the position information of each sub-operation data block in operation data, and respectively setting data block identifiers for M sub-operation data blocks based on the position information;
determining an uploading sequence of the M sub-operation data blocks based on the data block identification, and caching the M sub-operation data blocks in a preset cache queue based on the uploading sequence;
and determining a data transmission clock of a wireless transmission path between the vending machine and the server based on the data uploading frequency, and uploading M sub-operation data blocks cached in a preset cache queue to the server according to the uploading sequence based on the data transmission clock.
Preferably, in step 3, the data processing method for the server networked with the vending machine, based on the server, performs data processing on the operation data, includes:
acquiring classification categories of the running data of the automatic vending machine, and acquiring a historical data set of each category based on the classification categories;
dividing the historical data set of each category into a first data set and a second data set, and processing the first data set based on a preset iterative algorithm to obtain a classification dictionary corresponding to the running data of the vending machine;
processing the second data set based on the classification dictionary to obtain training feature vectors corresponding to each category, and cascading the training feature vectors to obtain a training matrix, wherein the training matrix comprises at least one training feature vector, and one training feature vector corresponds to one category;
Acquiring operation data of the vending machine received by a server, normalizing the operation data, and extracting feature vectors of the operation data;
inputting the feature vector into a training matrix, determining the hamming distance between the feature vector and the training feature vector based on the training matrix, and judging that the operation data corresponding to the feature vector and the historical data set corresponding to the training feature vector are in the same category when the hamming distance is smaller than a preset distance threshold;
completing classification of the operation data of the automatic vending machine based on the judging result, clustering each type of operation data based on the classifying result, and determining an isolated sample in each type of operation data;
matching a target data cleaning rule from a preset data cleaning rule base based on the data category of each type of operation data, and performing first cleaning on isolated samples in each type of operation data based on the target data cleaning rule to obtain initial cleaning data corresponding to each type of operation data;
determining effective data length evaluation parameters of each type of operation data based on the data type of each type of operation data, processing each type of operation data based on the effective data length evaluation parameters, and determining effective load fields in each type of operation data;
Determining the effective data length in each type of operation data based on the effective load field, and performing second cleaning on each type of operation data based on the effective data length to obtain target operation data;
determining a target annotation information set based on the data category of the target operation data, and annotating the target operation data through the target annotation set based on a preset data annotation rule, wherein the target annotation information set comprises at least one piece of target annotation information, and the target operation data of each category corresponds to one piece of target annotation information;
and finishing the classification operation of the operation data of the vending machine based on the labeling result.
Preferably, a server data processing method networked with a vending machine, completing classification of operation data of the vending machine based on a determination result, includes:
acquiring commodity information and corresponding sales volume data of commodities in the automatic vending machine based on the classification result, and determining the types of the commodities stored in the automatic vending machine based on the commodity information;
determining commodity names of different commodity types based on commodity information, extracting data characteristics of sales volume data, and classifying the sales volume data based on the data characteristics to obtain sub sales volume data;
Determining corresponding relations between different commodity types and sub sales data based on the classification result, and constructing a commodity sales histogram based on the corresponding relations, wherein the abscissa of the commodity sales histogram is a commodity name, and the ordinate is the sub sales data;
determining sales volume conditions of different commodity types stored in the automatic vending machine in a target time period based on the commodity sales volume bar graph, and determining target hot-sell commodities in the different commodity types based on the sales volume conditions, wherein the target hot-sell commodities are at least one type;
acquiring historical sales volume data of the target hot-sold commodity in a historical prediction time period, and determining sales characteristic data of the target hot-sold commodity based on the historical sales volume data;
determining an influence factor on sales volume of the target hot-sold commodity based on the sales characteristic data, and determining an association relationship between a characteristic sample and a result sample of the target hot-sold commodity in a historical prediction time period based on the influence factor;
constructing a sales volume prediction model based on the association relation, and predicting sales volume of the target hot-sold commodity after the target period based on the sales volume prediction model to obtain a sales volume prediction value;
constructing commodity category trees of different commodity categories stored in the automatic vending machine, and determining association attributes between the target hot-sell commodity and other commodities based on the commodity category trees;
Correcting the sales volume predicted value based on the associated attribute to obtain a target sales volume predicted value, simultaneously obtaining the current stock quantity of the target hot-sell commodity in the vending machine, and performing difference operation on the target sales volume predicted value and the current stock quantity to obtain a predicted replenishment volume;
and transmitting the predicted replenishment quantity to the management terminal for replenishment reminding.
Preferably, a server data processing method networked with a vending machine, based on a determination result, completes classification of operation data of the vending machine, further includes:
acquiring working data of the vending machine based on the classification result, and determining the device type of the vending machine and the service characteristics of each device based on the working data;
determining performance evaluation indexes of the vending machine based on the device types and the service characteristics of each device, and constructing a performance evaluation system based on the performance evaluation indexes;
processing the working data of the automatic vending machine based on the performance evaluation system, and determining abnormal data in the working data based on a processing result;
determining the data characteristics of the abnormal data, and determining the operation characteristics of the device corresponding to the abnormal data based on the data characteristics;
and determining the target hidden danger device based on the operation characteristics, and recording the working data of the target hidden danger device. Obtaining a recording result of the target hidden danger device;
And transmitting the recorded result to the management terminal, and reminding the management terminal to maintain the target hidden danger device.
Preferably, in step 3, a method for processing server data networked with a vending machine, generating a data processing report based on a processing result, includes:
obtaining a processing result of the server for carrying out data processing on the operation data, and determining data processing items contained in the processing result, wherein the data processing items are at least one type;
determining the item names of the data processing items and the item numbers of the data processing items, and constructing a data processing report template based on the item names and the item numbers;
and acquiring a target processing result corresponding to the data processing item, and recording the item name of the data processing item and the corresponding target processing result in a data processing report template to obtain a data processing report, wherein the item name of the data processing item corresponds to the target processing result one by one.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a method of processing server data networked with a vending machine in accordance with an embodiment of the present invention;
FIG. 2 is a flowchart of step 1 in a server data processing method networked with a vending machine according to an embodiment of the present invention;
fig. 3 is a flowchart of step 2 in a server data processing method networked with a vending machine according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
the embodiment provides a server data processing method networked with a vending machine, as shown in fig. 1, including:
step 1: acquiring a first network port of the vending machine and a second network port of the server, and networking the vending machine and the server based on the first network port and the second network port;
Step 2: acquiring operation data of the vending machine, and uploading the operation data to a server based on a networking result;
step 3: and performing data processing on the operation data based on the server, and generating a data processing report based on the processing result.
In this embodiment, the first network port is a connection interface used to characterize the vending machine and the server when networked, and is located on the vending machine.
In this embodiment, the second network port is a connection interface for characterizing the server to the vending machine when networked, and is located on the server.
In this embodiment, the operational data refers to the storage, sales, and change data of goods within the vending machine, the operational status data of the vending machine itself, and the like, including the malfunction data of the vending machine.
In this embodiment, performing data processing on the operation data based on the server refers to classifying the operation data of the vending machine acquired in the server, and determining the data characteristics of the operation data of different classes according to the classification result, so as to determine the working condition of the vending machine.
In this embodiment, the data processing report refers to a record file obtained by recording the processing result of the operation data of the vending machine.
The beneficial effects of the technical scheme are as follows: through carrying out the networking with vending machine and server, realize carrying out accurate effectual collection to vending machine's operation data, simultaneously, through handling the operation data who gathers to the realization is according to the result of processing to vending machine's running condition effectively holds, has improved data processing's rate of accuracy and efficiency, has also ensured carrying out effectual management to vending machine.
Example 2:
on the basis of embodiment 1, this embodiment provides a method for processing server data networked with a vending machine, as shown in fig. 2, in step 1, acquiring a first network port of the vending machine and a second network port of the server includes:
step 101: acquiring a terminal identifier of the vending machine, matching the terminal identifier with a preset identifier library, and determining a target model of the vending machine based on a matching result;
step 102: determining a first network port type of the vending machine based on the target model, determining a first network port parameter of the vending machine based on the first network port type, and determining a first network port of the vending machine based on the first network port parameter;
Step 103: acquiring a target message of a server, and determining a network port set in the server based on the target message;
step 104: determining an idle network port in the network port set, and determining a port parameter of the idle network port, wherein the idle network port is at least one;
step 105: and matching the first network port parameter with the port parameter of the idle network port, and determining a second network port of the server from the idle network ports based on the matching result.
In this embodiment, the terminal identifier is a label tag for marking different types and different vending machines, one vending machine corresponding to each terminal identifier.
In this embodiment, the preset identifier library is set in advance, and is used to store terminal identifiers corresponding to different vending machines.
In this embodiment, the target model refers to the device model of the current vending machine.
In this embodiment, the first network port type refers to the network port type of the vending machine.
In this embodiment, the first network port parameter refers to a transmission requirement of the network port of the vending machine for data and a networking requirement when networking with the server.
In this embodiment, the target message refers to ethernet data in the server, thereby facilitating determination of the port set of the server.
In this embodiment, the set of network ports refers to a set of all network ports existing in the server.
In this embodiment, the idle network port refers to port information in the server that is not currently connected to the device terminal.
The beneficial effects of the technical scheme are as follows: the network port parameters of the automatic vending machine are accurately analyzed by determining the equipment type of the automatic vending machine, so that the network port of the automatic vending machine is accurately confirmed, and secondly, all network interfaces in the server are confirmed by analyzing the message of the server, and finally, the network ports connected with the automatic vending machine are determined in a plurality of network ports, thereby providing convenience for networking the automatic vending machine and the server, and further improving the efficiency and accuracy of operation data processing of the automatic vending machine.
Example 3:
on the basis of embodiment 1, the present embodiment provides a method for processing server data networked with a vending machine, in step 1, networking the vending machine with the server based on a first network port and a second network port, including:
Acquiring a first network port of the vending machine and a second network port of the server, and controlling the vending machine to send a device networking request to the second network port of the server based on the first network port;
the server analyzes the received equipment networking request, determines the networking authority of the vending machine, matches the networking authority with a preset authority data table, and judges whether the server allows the vending machine to be networked;
if the network connection key is allowed, feeding back the network connection key to the automatic vending machine, and completing the network connection operation of the automatic vending machine and the server based on the network connection key;
otherwise, rejecting the device networking request sent by the vending machine.
In this embodiment, the device networking request is sent by the vending machine to the server for use in constructing a networking operation with the server.
In this embodiment, networking rights refer to conditions that the vending machine meets networking requirements.
In this embodiment, the preset permission data table is set in advance, and is used to store networking permissions of different devices.
In this embodiment, the networking key refers to a parameter that needs to be verified when the vending machine and the server are networked, and networking between the vending machine and the server can be completed through the key.
The beneficial effects of the technical scheme are as follows: through first network port and second network port, realize that vending machine sends equipment networking request to the server, and the server verifies equipment networking request after receiving equipment networking request, ensures that vending machine possesses the authority of networking to ensured the security to vending machine operation data processing, also be convenient for in time receive vending machine's operation data simultaneously, improved operation data processing efficiency.
Example 4:
on the basis of embodiment 3, this embodiment provides a server data processing method for networking with a vending machine, completing networking operation of the vending machine and a server based on a networking key, including:
acquiring networking results of the vending machine and the server, and determining a wireless transmission path between the vending machine and the server based on the networking results;
the automatic vending machine is controlled to send test data packets to the server based on the wireless transmission path, and receiving information of the server on the test data packets is obtained in real time;
determining transmission performance parameters of the wireless transmission path based on the received information, and comparing the transmission performance parameters with a preset threshold;
If the transmission performance parameter is greater than or equal to a preset threshold value, judging that the wireless transmission path between the vending machine and the server is qualified, and finishing verification of the networking result between the vending machine and the server;
otherwise, the wireless transmission path between the vending machine and the server is judged to be unqualified.
In this embodiment, the wireless transmission path refers to a data transmission link for data transmission between the vending machine and the server.
In this embodiment, the test packet is set in advance and is sent by the vending machine to the server for verifying whether the transmission link between the vending machine and the server is a path.
In this embodiment, the received information includes the time of reception of the test packet by the server, the data volume of the received test packet, the time delay of reception of the data, and the like
In this embodiment, the transmission performance parameter is used to characterize the transmission condition of the data by the wireless transmission path, and the larger the value is, the better the data transmission performance of the wireless transmission path is.
In this embodiment, the preset threshold is set in advance, and is used to measure whether the performance of the wireless transmission path for transmitting data meets the expected requirement.
The beneficial effects of the technical scheme are as follows: the automatic vending machine sends the test data packet to the server, so that the reliability of a wireless transmission path between the automatic vending machine and the server is realized, the server is ensured to be capable of effectively receiving the operation data in the automatic vending machine, and the automatic vending machine data is rapidly and accurately processed.
Example 5:
on the basis of embodiment 4, this embodiment provides a server data processing method networked with a vending machine, and determines that a wireless transmission path between the vending machine and a server is not qualified, including:
acquiring target requirements on a wireless transmission path, acquiring receiving information of a server on a test data packet, and determining transmission delay and transmission rate of the wireless transmission path based on the receiving information, wherein the target requirements correspond to the transmission delay and the transmission rate;
determining a difference value between the target requirement and the transmission delay and between the target requirement and the transmission rate;
acquiring a network node in a wireless transmission path, and determining transmission configuration parameters of the network node to a test data packet;
and adjusting the configuration parameters of the network node based on the difference value, and re-determining the transmission performance parameters of the wireless transmission path after adjustment until the transmission performance parameters are greater than or equal to a preset threshold value.
In this embodiment, the target requirement is set in advance, and is used to characterize the theoretical transmission performance of the wireless transmission path on the data.
In this embodiment, the difference value refers to a transmission delay of the wireless transmission path and a difference between the transmission rate and the target requirement.
In this embodiment, the network node is a node in the wireless transmission path that processes the transmission data, and different configurations result in different transmission performance of the data in the wireless transmission path.
In this embodiment, the transmission configuration parameter refers to a transmission condition of the network node when transmitting data, specifically, a rate of data processing, a bandwidth of transmission, and the like.
The beneficial effects of the technical scheme are as follows: when the transmission performance of the wireless transmission path is judged to be unqualified, the configuration parameters of the wireless transmission path are adjusted, so that the wireless transmission path between the vending machine and the server can accurately and efficiently transmit the operation data of the vending machine, and the effect of the server on the processing of the vending operation data is guaranteed.
Example 6:
on the basis of embodiment 1, the present embodiment provides a method for processing server data networked with a vending machine, as shown in fig. 3, in step 2, operation data of the vending machine is obtained, and the operation data is uploaded to a server based on a networking result, including:
Step 201: acquiring data uploading frequency, and determining a time interval for data acquisition of the vending machine based on the data uploading frequency;
step 202: acquiring operation data of the automatic vending machine based on the time interval, and splitting the operation data to obtain M sub-operation data blocks;
step 203: determining the position information of each sub-operation data block in operation data, and respectively setting data block identifiers for M sub-operation data blocks based on the position information;
step 204: determining an uploading sequence of the M sub-operation data blocks based on the data block identification, and caching the M sub-operation data blocks in a preset cache queue based on the uploading sequence;
step 205: and determining a data transmission clock of a wireless transmission path between the vending machine and the server based on the data uploading frequency, and uploading M sub-operation data blocks cached in a preset cache queue to the server according to the uploading sequence based on the data transmission clock.
In this embodiment, the data upload frequency refers to the frequency value at which the vending machine transmits data to the server.
In this embodiment, the sub operation data block refers to a data segment of operation data obtained by splitting operation data of the vending machine.
In this embodiment, the location information refers to the location of the different sub-operational data blocks in the operational data before the operational data is split.
In this embodiment, the data block identifier is a tag label for marking different sub-data blocks, and by using the tag label, information such as a position of a corresponding sub-data block can be quickly and accurately determined.
In this embodiment, the uploading sequence is determined according to the position of the sub-operation data block in the operation data, and may be determining the sequence of uploading the data according to the time acquisition sequence of the operation data.
In this embodiment, the preset buffer queue is set in advance, and is used for temporarily storing data and performing corresponding processing on a storage area when the data is transmitted to the data receiving end.
In this embodiment, the data transmission clock is used to characterize the time difference between two adjacent data when the data is transmitted by the wireless transmission path between the vending machine and the server.
The beneficial effects of the technical scheme are as follows: through confirming the uploading frequency to vending machine operation data to realize effectively confirming vending machine data acquisition's time interval, secondly, gather vending machine's operation data through time interval, and thereby carry out the blocking with the operation data who gathers and realize the accurate efficient transmission to the server with vending machine's operation data, ensured the receiving effect of server to vending machine operation data, improved processing efficiency and the processing accuracy to vending machine operation data.
Example 7:
on the basis of embodiment 1, the present embodiment provides a server data processing method networked with a vending machine, in step 3, performing data processing on operation data based on a server, including:
acquiring classification categories of the running data of the automatic vending machine, and acquiring a historical data set of each category based on the classification categories;
dividing the historical data set of each category into a first data set and a second data set, and processing the first data set based on a preset iterative algorithm to obtain a classification dictionary corresponding to the running data of the vending machine;
processing the second data set based on the classification dictionary to obtain training feature vectors corresponding to each category, and cascading the training feature vectors to obtain a training matrix, wherein the training matrix comprises at least one training feature vector, and one training feature vector corresponds to one category;
acquiring operation data of the vending machine received by a server, normalizing the operation data, and extracting feature vectors of the operation data;
inputting the feature vector into a training matrix, determining the hamming distance between the feature vector and the training feature vector based on the training matrix, and judging that the operation data corresponding to the feature vector and the historical data set corresponding to the training feature vector are in the same category when the hamming distance is smaller than a preset distance threshold;
Completing classification of the operation data of the automatic vending machine based on the judging result, clustering each type of operation data based on the classifying result, and determining an isolated sample in each type of operation data;
matching a target data cleaning rule from a preset data cleaning rule base based on the data category of each type of operation data, and performing first cleaning on isolated samples in each type of operation data based on the target data cleaning rule to obtain initial cleaning data corresponding to each type of operation data;
determining effective data length evaluation parameters of each type of operation data based on the data type of each type of operation data, processing each type of operation data based on the effective data length evaluation parameters, and determining effective load fields in each type of operation data;
determining the effective data length in each type of operation data based on the effective load field, and performing second cleaning on each type of operation data based on the effective data length to obtain target operation data;
determining a target annotation information set based on the data category of the target operation data, and annotating the target operation data through the target annotation set based on a preset data annotation rule, wherein the target annotation information set comprises at least one piece of target annotation information, and the target operation data of each category corresponds to one piece of target annotation information;
And finishing the classification operation of the operation data of the vending machine based on the labeling result.
In this embodiment, the classification categories are the number of categories that are known in advance to be required to classify the collected operation data of the vending machine, and the data category that each category contains.
In this embodiment, the historical data set is obtained in advance and is the operational data of the vending machine over a period of time, including different categories of data information.
In this embodiment, the first data set and the second data set refer to dividing the historical data set of the same category into two blocks, so as to construct a training matrix, wherein the first data set is used for determining the classification dictionary, and the second data set is used for determining the corresponding feature vector.
In this embodiment, the preset iterative algorithm is set in advance.
In this embodiment, the classification dictionary is used for recording data characteristics of different types of data, including association relationships among the data, data structure characteristics and data value conditions.
In this embodiment, the training feature vector refers to a data feature corresponding to the second data set, including a data value and the like.
In this embodiment, cascading refers to summarizing training feature vectors corresponding to different types of data sets, so as to achieve acquisition of a training matrix.
In this embodiment, the training matrix is a classification tool for classifying the vending machine's operational data, and is trained.
In this embodiment, the normalization processing refers to performing the same specification on the value of the operation data, so as to facilitate accurate classification of the operation data.
In this embodiment, the feature vector refers to a value condition corresponding to a data feature of the operation data of the vending machine, so as to match with the training feature vector in the training matrix, thereby achieving the purpose of classification.
In this embodiment, hamming distance is used to characterize how far and how near different data are, the closer the distance is, the more similar the two are, and sharing the same type center.
In this embodiment, the preset distance threshold is set in advance, and is used to measure whether the classification criterion is satisfied.
In this embodiment, the isolated sample refers to data whose data value existing in the same class of data deviates significantly from the data average value.
In this embodiment, the preset data cleansing rule base is set in advance, and is used for storing data cleansing rules corresponding to different types of data.
In this embodiment, the target data cleansing rule refers to a cleansing rule suitable for cleansing data of a current data category, and is one or a combination of several kinds of preset data cleansing rule bases.
In this embodiment, the first cleaning refers to cleaning of isolated samples in each type of operational data.
In this embodiment, the initial cleaning data refers to cleaning data obtained after cleaning the isolated sample data in the operation data of each category.
In this embodiment, the valid data length evaluation parameter is used to measure an evaluation criterion or a reference criterion of valid data bytes in the running data, specifically, a degree of association between core content of a field and the whole data, and the like.
In this embodiment, the payload field refers to a valid data field in each type of operation data.
In this embodiment, the effective data length refers to the number of key bytes in each type of operation data that can indicate the core content of the type of operation data.
In this embodiment, only the second cleaning refers to the extraction of the valid data length in each class of operational data.
In this embodiment, the target operation data refers to final data obtained by performing isolated sample cleaning on each type of operation data and extracting the effective data length.
In this embodiment, the target annotation information set refers to a set of marking data for category marking each category of operation data.
In this embodiment, the preset data labeling rules are set in advance, and are used for labeling data of different types of operation data.
The beneficial effects of the technical scheme are as follows: through confirming the categorised class to vending machine operation data, and realize carrying out accurate effectual construction to training matrix according to categorised class, thereby ensured the categorised rate of accuracy to vending machine operation data, secondly, through carrying out first washing and the second washing to each class operation data, ensure that the operation data that obtains is accurate effective, provide convenience for the guarantee to vending operation data's processing effect, finally, carry out different mark to each class operation data, thereby be convenient for directly perceivedly efficient distinguish different operation data types, ensured the processing effect to vending machine operation data.
Example 8:
on the basis of embodiment 7, the present embodiment provides a server data processing method networked with a vending machine, and based on a determination result, the classification of operation data of the vending machine is completed, including:
acquiring commodity information and corresponding sales volume data of commodities in the automatic vending machine based on the classification result, and determining the types of the commodities stored in the automatic vending machine based on the commodity information;
Determining commodity names of different commodity types based on commodity information, extracting data characteristics of sales volume data, and classifying the sales volume data based on the data characteristics to obtain sub sales volume data;
determining corresponding relations between different commodity types and sub sales data based on the classification result, and constructing a commodity sales histogram based on the corresponding relations, wherein the abscissa of the commodity sales histogram is a commodity name, and the ordinate is the sub sales data;
determining sales volume conditions of different commodity types stored in the automatic vending machine in a target time period based on the commodity sales volume bar graph, and determining target hot-sell commodities in the different commodity types based on the sales volume conditions, wherein the target hot-sell commodities are at least one type;
acquiring historical sales volume data of the target hot-sold commodity in a historical prediction time period, and determining sales characteristic data of the target hot-sold commodity based on the historical sales volume data;
determining an influence factor on sales volume of the target hot-sold commodity based on the sales characteristic data, and determining an association relationship between a characteristic sample and a result sample of the target hot-sold commodity in a historical prediction time period based on the influence factor;
constructing a sales volume prediction model based on the association relation, and predicting sales volume of the target hot-sold commodity after the target period based on the sales volume prediction model to obtain a sales volume prediction value;
Constructing commodity category trees of different commodity categories stored in the automatic vending machine, and determining association attributes between the target hot-sell commodity and other commodities based on the commodity category trees;
correcting the sales volume predicted value based on the associated attribute to obtain a target sales volume predicted value, simultaneously obtaining the current stock quantity of the target hot-sell commodity in the vending machine, and performing difference operation on the target sales volume predicted value and the current stock quantity to obtain a predicted replenishment volume;
and transmitting the predicted replenishment quantity to the management terminal for replenishment reminding.
In this embodiment, the commodity information refers to the type of commodity, the corresponding commodity name, and the like.
In this embodiment, the commodity name refers to the commodity name of the commodity stored in the vending machine.
In this embodiment, the data features refer to category features contained in sales data and corresponding specific value cases.
In this embodiment, the sub sales data refers to sales data included in each category after classifying the sales data.
In this embodiment, the commodity sales histogram is used to characterize sales of different classes of commodities over a period of time.
In this embodiment, the target period is set in advance, and may be, specifically, one month or half year.
In this embodiment, the target hot commodity refers to a commodity type in which the amount of sales reaches a preset threshold value in a target period of time, the preset threshold value being modifiable.
In this embodiment, the historical forecast time period is set in advance and is sales data of the vending machine before a certain time period.
In this embodiment, the sales characteristic data refers to sales speeds of the target hot commodity at different points in time and sales volume at different points in time within the historical forecast period.
In this embodiment, the historical sales data refers to sales of the vending machine to the targeted hot merchandise over a historical forecast period of time.
In this embodiment, the influencing factors include weather, amount of customer browsing, and the like.
In this embodiment, the characteristic sample refers to the corresponding stock quantity of the target hot commodity at the beginning of the historical prediction period.
In this embodiment, the result sample refers to the remaining inventory corresponding to the target hot commodity after the end of the historical forecast period.
In this embodiment, the target period is set in advance, specifically, one week, one month, two months, or the like.
In this embodiment, the sales prediction value refers to the number of target hot products that may be sold at the end of the target period.
In this embodiment, the commodity category tree is used to represent the association relationship between commodity attributes of different types of commodities in the vending machine, so as to facilitate judging whether other commodities will affect sales of the target hot-sell commodity.
In this embodiment, the associated attribute refers to a case where other commodity stores are consistent with the target hot commodity type, or the like.
In this embodiment, the target sales predicted value refers to a final sales predicted result obtained by correcting the value of the sales predicted value.
In this embodiment, the predicted restocking amount refers to the restocking amount required for the target hot commodity.
The beneficial effects of the technical scheme are as follows: according to the method, commodity sales data in the automatic vending machine are acquired according to the classification result, so that sales heat of different commodity types in the automatic vending machine is effectively analyzed, accurate locking of target hot-sell commodities is achieved, and secondly, a sales prediction model is built by processing and analyzing historical sales data of the target hot-sell commodities, so that sales of the target hot-sell commodities in a target period is accurately predicted, and the goods supplementing quantity is timely determined according to the prediction result, the practicability of data processing of the automatic vending machine is improved, and meanwhile accurate and effective analysis of sales data in operation data of the automatic vending machine is guaranteed.
Example 9:
on the basis of embodiment 7, this embodiment provides a server data processing method networked with a vending machine, and based on a determination result, classifies operation data of the vending machine, and further includes:
acquiring working data of the vending machine based on the classification result, and determining the device type of the vending machine and the service characteristics of each device based on the working data;
determining performance evaluation indexes of the vending machine based on the device types and the service characteristics of each device, and constructing a performance evaluation system based on the performance evaluation indexes;
processing the working data of the automatic vending machine based on the performance evaluation system, and determining abnormal data in the working data based on a processing result;
determining the data characteristics of the abnormal data, and determining the operation characteristics of the device corresponding to the abnormal data based on the data characteristics;
and determining the target hidden danger device based on the operation characteristics, and recording the working data of the target hidden danger device. Obtaining a recording result of the target hidden danger device;
and transmitting the recorded result to the management terminal, and reminding the management terminal to maintain the target hidden danger device.
In this embodiment, the operational data refers to the operation status data of the vending machine itself, including data in normal operation and data in abnormal operation.
In this embodiment, the service features refer to the operation types of different devices and the corresponding operation features.
In this embodiment, the performance evaluation index is used to evaluate whether each device meets the requirements for normal performance.
In this embodiment, the abnormal data refers to a data segment with abnormal value in the working data of the vending machine.
In this embodiment, the data characteristics refer to the data type of the abnormal data and the corresponding specific value condition.
In this embodiment, the operation features refer to the operation features of the device, the operation functions implemented, and the like.
In this embodiment, the target hidden trouble device refers to a device having a hidden trouble in the vending machine.
The beneficial effects of the technical scheme are as follows: the automatic vending machine has the advantages that the working data of the automatic vending machine are acquired according to the classification result, the working data are analyzed and processed, the fault hidden danger devices in the automatic vending machine are accurately and effectively analyzed according to the working data, the accuracy of evaluating the running performance of the automatic vending machine according to the data is improved, and in addition, when faults exist, workers are conveniently and timely reminded of running and maintenance.
Example 10:
on the basis of embodiment 1, the present embodiment provides a server data processing method networked with a vending machine, in step 3, generating a data processing report based on a processing result, including:
Obtaining a processing result of the server for carrying out data processing on the operation data, and determining data processing items contained in the processing result, wherein the data processing items are at least one type;
determining the item names of the data processing items and the item numbers of the data processing items, and constructing a data processing report template based on the item names and the item numbers;
and acquiring a target processing result corresponding to the data processing item, and recording the item name of the data processing item and the corresponding target processing result in a data processing report template to obtain a data processing report, wherein the item name of the data processing item corresponds to the target processing result one by one.
In this embodiment, the data processing items refer to the types of processing of the operation data of the vending machine, and specifically include data classification, data cleansing, sales prediction, troubleshooting, and the like.
In this embodiment, the target processing result refers to a data processing result obtained after the server processes the different data processing items.
The beneficial effects of the technical scheme are as follows: the server is determined to sell the data processing items of the operation data, and processing results of different data processing items are obtained at the same time, so that accurate and effective recording of the data processing items and corresponding processing results is realized, and the management terminal can check the processing results in real time.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. A method of processing server data networked to a vending machine, comprising:
step 1: acquiring a first network port of the vending machine and a second network port of the server, and networking the vending machine and the server based on the first network port and the second network port;
step 2: acquiring operation data of the vending machine, and uploading the operation data to a server based on a networking result;
step 3: performing data processing on the operation data based on the server, and generating a data processing report based on a processing result;
in step 3, performing data processing on the operation data based on the server, including:
acquiring classification categories of the running data of the automatic vending machine, and acquiring a historical data set of each category based on the classification categories;
dividing the historical data set of each category into a first data set and a second data set, and processing the first data set based on a preset iterative algorithm to obtain a classification dictionary corresponding to the running data of the vending machine;
Processing the second data set based on the classification dictionary to obtain training feature vectors corresponding to each category, and cascading the training feature vectors to obtain a training matrix, wherein the training matrix comprises at least one training feature vector, and one training feature vector corresponds to one category;
acquiring operation data of the vending machine received by a server, normalizing the operation data, and extracting feature vectors of the operation data;
inputting the feature vector into a training matrix, determining the hamming distance between the feature vector and the training feature vector based on the training matrix, and judging that the operation data corresponding to the feature vector and the historical data set corresponding to the training feature vector are in the same category when the hamming distance is smaller than a preset distance threshold;
completing classification of the operation data of the automatic vending machine based on the judging result, clustering each type of operation data based on the classifying result, and determining an isolated sample in each type of operation data;
matching a target data cleaning rule from a preset data cleaning rule base based on the data category of each type of operation data, and performing first cleaning on isolated samples in each type of operation data based on the target data cleaning rule to obtain initial cleaning data corresponding to each type of operation data;
Determining effective data length evaluation parameters of each type of operation data based on the data type of each type of operation data, processing each type of operation data based on the effective data length evaluation parameters, and determining effective load fields in each type of operation data;
determining the effective data length in each type of operation data based on the effective load field, and performing second cleaning on each type of operation data based on the effective data length to obtain target operation data;
determining a target annotation information set based on the data category of the target operation data, and annotating the target operation data through the target annotation set based on a preset data annotation rule, wherein the target annotation information set comprises at least one piece of target annotation information, and the target operation data of each category corresponds to one piece of target annotation information;
and finishing the classification operation of the operation data of the vending machine based on the labeling result.
2. The method of claim 1, wherein in step 1, obtaining a first network port of the vending machine and a second network port of the server comprises:
acquiring a terminal identifier of the vending machine, matching the terminal identifier with a preset identifier library, and determining a target model of the vending machine based on a matching result;
Determining a first network port type of the vending machine based on the target model, determining a first network port parameter of the vending machine based on the first network port type, and determining a first network port of the vending machine based on the first network port parameter;
acquiring a target message of a server, and determining a network port set in the server based on the target message;
determining an idle network port in the network port set, and determining a port parameter of the idle network port, wherein the idle network port is at least one;
and matching the first network port parameter with the port parameter of the idle network port, and determining a second network port of the server from the idle network ports based on the matching result.
3. The method of claim 1, wherein in step 1, networking the vending machine with the server based on the first network port and the second network port, comprises:
acquiring a first network port of the vending machine and a second network port of the server, and controlling the vending machine to send a device networking request to the second network port of the server based on the first network port;
The server analyzes the received equipment networking request, determines the networking authority of the vending machine, matches the networking authority with a preset authority data table, and judges whether the server allows the vending machine to be networked;
if the network connection key is allowed, feeding back the network connection key to the automatic vending machine, and completing the network connection operation of the automatic vending machine and the server based on the network connection key;
otherwise, rejecting the device networking request sent by the vending machine.
4. A method of processing server data networked to a vending machine as claimed in claim 3, wherein the networking operation of the vending machine to the server is accomplished based on a networking key, comprising:
acquiring networking results of the vending machine and the server, and determining a wireless transmission path between the vending machine and the server based on the networking results;
the automatic vending machine is controlled to send test data packets to the server based on the wireless transmission path, and receiving information of the server on the test data packets is obtained in real time;
determining transmission performance parameters of the wireless transmission path based on the received information, and comparing the transmission performance parameters with a preset threshold;
if the transmission performance parameter is greater than or equal to a preset threshold value, judging that the wireless transmission path between the vending machine and the server is qualified, and finishing verification of the networking result between the vending machine and the server;
Otherwise, the wireless transmission path between the vending machine and the server is judged to be unqualified.
5. The method of claim 4, wherein determining that the wireless transmission path between the vending machine and the server is unacceptable comprises:
acquiring target requirements on a wireless transmission path, acquiring receiving information of a server on a test data packet, and determining transmission delay and transmission rate of the wireless transmission path based on the receiving information, wherein the target requirements correspond to the transmission delay and the transmission rate;
determining a difference value between the target requirement and the transmission delay and between the target requirement and the transmission rate;
acquiring a network node in a wireless transmission path, and determining transmission configuration parameters of the network node to a test data packet;
and adjusting the configuration parameters of the network node based on the difference value, and re-determining the transmission performance parameters of the wireless transmission path after adjustment until the transmission performance parameters are greater than or equal to a preset threshold value.
6. The method for processing server data networked to a vending machine according to claim 1, wherein in step 2, operation data of the vending machine is acquired and uploaded to the server based on the networking result, comprising:
Acquiring data uploading frequency, and determining a time interval for data acquisition of the vending machine based on the data uploading frequency;
acquiring operation data of the automatic vending machine based on the time interval, and splitting the operation data to obtain M sub-operation data blocks;
determining the position information of each sub-operation data block in operation data, and respectively setting data block identifiers for M sub-operation data blocks based on the position information;
determining an uploading sequence of the M sub-operation data blocks based on the data block identification, and caching the M sub-operation data blocks in a preset cache queue based on the uploading sequence;
and determining a data transmission clock of a wireless transmission path between the vending machine and the server based on the data uploading frequency, and uploading M sub-operation data blocks cached in a preset cache queue to the server according to the uploading sequence based on the data transmission clock.
7. A server data processing method networked with a vending machine according to claim 1, wherein sorting of the vending machine operational data is completed based on the determination result, comprising:
acquiring commodity information and corresponding sales volume data of commodities in the automatic vending machine based on the classification result, and determining the types of the commodities stored in the automatic vending machine based on the commodity information;
Determining commodity names of different commodity types based on commodity information, extracting data characteristics of sales volume data, and classifying the sales volume data based on the data characteristics to obtain sub sales volume data;
determining corresponding relations between different commodity types and sub sales data based on the classification result, and constructing a commodity sales histogram based on the corresponding relations, wherein the abscissa of the commodity sales histogram is a commodity name, and the ordinate is the sub sales data;
determining sales volume conditions of different commodity types stored in the automatic vending machine in a target time period based on the commodity sales volume bar graph, and determining target hot-sell commodities in the different commodity types based on the sales volume conditions, wherein the target hot-sell commodities are at least one type;
acquiring historical sales volume data of the target hot-sold commodity in a historical prediction time period, and determining sales characteristic data of the target hot-sold commodity based on the historical sales volume data;
determining an influence factor on sales volume of the target hot-sold commodity based on the sales characteristic data, and determining an association relationship between a characteristic sample and a result sample of the target hot-sold commodity in a historical prediction time period based on the influence factor;
constructing a sales volume prediction model based on the association relation, and predicting sales volume of the target hot-sold commodity after the target period based on the sales volume prediction model to obtain a sales volume prediction value;
Constructing commodity category trees of different commodity categories stored in the automatic vending machine, and determining association attributes between the target hot-sell commodity and other commodities based on the commodity category trees;
correcting the sales volume predicted value based on the associated attribute to obtain a target sales volume predicted value, simultaneously obtaining the current stock quantity of the target hot-sell commodity in the vending machine, and performing difference operation on the target sales volume predicted value and the current stock quantity to obtain a predicted replenishment volume;
and transmitting the predicted replenishment quantity to the management terminal for replenishment reminding.
8. The method of claim 1, wherein sorting the vending machine operational data is accomplished based on the determination, further comprising:
acquiring working data of the vending machine based on the classification result, and determining the device type of the vending machine and the service characteristics of each device based on the working data;
determining performance evaluation indexes of the vending machine based on the device types and the service characteristics of each device, and constructing a performance evaluation system based on the performance evaluation indexes;
processing the working data of the automatic vending machine based on the performance evaluation system, and determining abnormal data in the working data based on a processing result;
Determining the data characteristics of the abnormal data, and determining the operation characteristics of the device corresponding to the abnormal data based on the data characteristics;
determining a target hidden danger device based on the operation characteristics, and recording working data of the target hidden danger device to obtain a recording result of the target hidden danger device;
and transmitting the recorded result to the management terminal, and reminding the management terminal to maintain the target hidden danger device.
9. A server data processing method networked with a vending machine according to claim 1, wherein in step 3, generating a data processing report based on the processing result comprises:
obtaining a processing result of the server for carrying out data processing on the operation data, and determining data processing items contained in the processing result, wherein the data processing items are at least one type;
determining the item names of the data processing items and the item numbers of the data processing items, and constructing a data processing report template based on the item names and the item numbers;
and acquiring a target processing result corresponding to the data processing item, and recording the item name of the data processing item and the corresponding target processing result in a data processing report template to obtain a data processing report, wherein the item name of the data processing item corresponds to the target processing result one by one.
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