CN115150430B - Vending machine operation data acquisition system based on internet of things - Google Patents
Vending machine operation data acquisition system based on internet of things Download PDFInfo
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Abstract
The invention provides an automatic vending machine operation data acquisition system based on the Internet of things, which comprises: the system comprises a node establishing module, a node acquiring module and a node acquiring module, wherein the node establishing module is used for establishing an acquisition structure diagram aiming at each automatic vending machine based on the position and the name of each automatic vending machine, and the acquisition structure diagram comprises a plurality of acquisition nodes; the data acquisition module is used for acquiring single operation data of a plurality of acquisition nodes in the acquisition structure diagram; the remote processing module is used for analyzing the single operation data based on the node relation among the plurality of acquisition nodes to obtain comprehensive operation data; by analyzing the single operation data according to the node relation among the plurality of acquisition nodes, comprehensive operation data is obtained, the quality of the acquired operation data is guaranteed, the operation data can better represent the operation condition of the single vending machine and the operation influence condition among the vending machines, and accurate operation data is provided for operation analysis of the vending machines.
Description
Technical Field
The invention relates to the technical field of data acquisition, in particular to an automatic vending machine operation data acquisition system based on the Internet of things.
Background
The vending machine is a machine capable of automatically paying according to the input coins or the code scanning. The vending machine is a common device for commercial automation, is not limited by time and place, and can save manpower and facilitate transaction. Is a brand new form of commercial retail, also known as a 24 hour open supermarket.
The conventional vending machine obtains operation data only including sales data and quantity data in the vending machine, and only aims at one vending machine, so that the provided operation data has great defects on maximizing the benefit of the vending machine, the operation analysis of the vending machine has limitations, and the operation condition of the vending machine cannot be accurately determined.
Disclosure of Invention
The invention provides an operation data acquisition system of an automatic vending machine based on the Internet of things, which ensures the quality of acquired operation data and provides accurate operation data for operation analysis of the automatic vending machine.
An internet of things-based vending machine operation data acquisition system, comprising:
the system comprises a node establishing module, a node acquiring module and a node acquiring module, wherein the node establishing module is used for establishing an acquisition structure diagram aiming at each automatic vending machine based on the position and the name of each automatic vending machine, and the acquisition structure diagram comprises a plurality of acquisition nodes;
The data acquisition module is used for acquiring single operation data of a plurality of acquisition nodes in the acquisition structure diagram;
and the remote processing module is used for analyzing the single operation data based on the node relation among the plurality of acquisition nodes to obtain comprehensive operation data.
Preferably, the method further comprises: and the communication module is connected with the data acquisition module and the remote processing module and is used for establishing communication connection between the data acquisition module and the remote processing module and remotely transmitting the single operation data acquired by the data acquisition module to the remote processing module.
Preferably, the node establishment module includes:
the identification building unit is used for building a first identification for the vending machine according to the positions and names of the vending machines in the target detection range;
the identification establishing unit is further used for determining the acquisition relation between the data acquisition module and the acquisition interface of the vending machine equipment based on the first identification, and establishing a second identification for the vending machine based on the acquisition relation;
and the node establishing unit is used for establishing acquisition nodes based on the first identifier and the second identifier, and finally obtaining an acquisition structure diagram.
Preferably, the data acquisition module includes:
the interface design unit is used for adding information of each sensor arranged on the vending machine to the acquisition node and arranging a data inlet corresponding to each sensor at the acquisition node;
the data acquisition unit is used for acquiring signals of each sensor and sending the signals to a data inlet of the acquisition node;
and the data analysis unit is used for analyzing based on the signals of the data inlet to obtain single operation data of the acquisition node.
Preferably, the data acquisition unit further includes:
a signal receiving unit for receiving signals from the signal output interfaces of the respective sensors;
the data sending unit is used for packaging the signals to obtain data packets and sending the data packets to the acquisition node;
and the data access unit is used for distributing signals in the data packet to the corresponding data entry based on the attribute of the data entry.
Preferably, the data analysis unit includes:
the data conversion unit is used for determining the corresponding relation between the signal characteristics and the parameters based on the attribute of the data entry, and determining the parameter value corresponding to the signal based on the corresponding relation;
The data standardization unit is used for carrying out standardization of a numerical form and a numerical unit on the parameter values according to the format of standardized operation data to obtain standard parameter values;
the data classification unit is used for acquiring an operation index corresponding to the standard parameter value, dividing the standard parameter value which can directly represent the operation index into a first standard parameter value, and dividing the standard parameter value which cannot directly represent the operation index into a second standard parameter value;
the data determining unit is used for taking the first standard parameter value as first single operation data;
the data classifying unit is further configured to divide the second standard parameter value into a third standard parameter value based on the operation index, divide the operation data into a plurality of parameter values, comprehensively represent the operation data into a fourth standard parameter value, and obtain a fourth standard parameter data set corresponding to each operation data;
the relation determining unit is used for acquiring a relation template of the second standard parameters and the operation indexes, and inputting the second standard parameters into the relation template to obtain second single operation data;
The model building unit is used for building an operation data determining model based on the historical parameter values and the historical operation data;
the data processing unit is used for analyzing the fourth standard parameter data set, establishing the influence degree of the fourth standard parameter values in the fourth standard parameter data set on the corresponding operation indexes, and determining a comprehensive parameter value set based on the fourth standard parameter data set and the influence degree;
the model running unit is used for inputting the comprehensive parameter value set into the operation data determining model and outputting to obtain third single operation data;
the data acquisition unit is used for integrating the first single operation data, the second single operation data, the third single operation data and the corresponding operation indexes thereof to obtain single operation data.
Preferably, the remote processing module includes:
the matrix establishing unit is used for establishing a position information matrix of each acquisition node based on the position information of the plurality of acquisition nodes; based on the environmental information of the plurality of acquisition nodes, establishing an environmental information matrix of each acquisition node;
the matrix comparison unit is used for analyzing the position information matrix of the acquisition node to be analyzed and the position information matrix of other acquisition nodes to obtain a position difference matrix set; analyzing the environmental information matrix of the acquisition node to be analyzed and the environmental information matrix of other acquisition nodes to obtain an environmental difference matrix set;
The difference acquisition unit is used for acquiring the data difference between the single operation data of the acquisition node to be analyzed and other single operation data;
the matrix selection unit is used for acquiring a first target position difference matrix set with position difference within a first preset range from the position difference matrix and acquiring a first environment difference matrix set corresponding to the first target position difference matrix set;
the relation determining unit is used for analyzing the data difference based on the first target environment difference matrix set and determining a first influence relation of each environment factor on each operation index;
the matrix selection unit is further configured to obtain a second target environmental difference matrix set with environmental differences within a second preset range from the environmental difference matrices, and obtain a second position difference matrix set corresponding to the second target environmental difference matrix set;
the relationship determining unit is further configured to analyze the data difference based on the second target position difference matrix set, and determine a second influence relationship of each position factor on each operation index;
the relation adjusting unit is used for determining a first adjusting strategy according to the distribution of the position difference in a first preset range and adjusting the first influence relation to obtain a third influence relation; determining a second adjustment strategy according to the distribution of the environmental difference in a second preset range, and adjusting the second influence relation to obtain a fourth influence relation;
The data making unit is used for determining operation restriction data among a plurality of acquisition nodes based on the third influence relation; determining operational environment data between a plurality of collection nodes based on the fourth influence relationship;
and the data integration unit is used for obtaining comprehensive operation data based on the single operation data, the operation restriction data and the operation environment data.
Preferably, the relationship determination unit includes:
the characteristic acquisition unit is used for extracting the environmental elements at the same position in each first target environmental difference matrix in the first target environmental difference matrix set, comparing the environmental elements to obtain the environmental difference characteristics of the environmental elements, acquiring the data differences corresponding to the environmental difference characteristics of all the environmental elements, and determining the data difference characteristics;
the relation establishing unit is used for establishing a causal relation between the environmental difference characteristic and the data difference characteristic and acquiring a causal relation set of all first target environmental difference matrixes in the first target environmental difference matrix set;
and the relation processing unit is used for carrying out average processing on the causal relation set and determining a first influence relation of each environmental factor on each operation index according to an average processing result.
Preferably, the method further comprises: the data storage module is used for storing the comprehensive operation data;
the data storage module comprises:
the data dividing unit is used for dividing the operation index into a short-time storage type and a long-time storage type according to the attribute of the operation index, and dividing the comprehensive operation data into first comprehensive operation data and second comprehensive operation data based on the storage type;
a mode determining unit, configured to determine a data storage mode of the first comprehensive operation data and the second comprehensive operation data based on a data capacity and a storage duration of the first comprehensive operation data and the second comprehensive operation data, respectively;
and the data storage unit is used for respectively storing the first comprehensive operation data and the second comprehensive operation data according to the data storage mode.
In this embodiment, the operation index of the first comprehensive operation data is a short-time storage type, and the operation index of the second comprehensive operation data is a long-time storage type.
Preferably, the mode determining unit includes:
the sorting unit is used for determining the matching degree of each storage node and the second comprehensive data according to the storage capacity and the storage node attribute of the storage node in the storage space and the data capacity and the storage time length of the second comprehensive operation data, and sorting the storage nodes according to the numerical value of the matching degree;
The node selection unit is used for selecting a second storage node of the second comprehensive operation data according to the sequencing result of the storage nodes;
the node selection unit is further configured to select a first storage node for the first comprehensive operation data from the remaining storage nodes in the same manner as the second comprehensive operation data is selected as the storage node;
the data updating unit is used for judging whether the actual storage duration of the comprehensive operation data stored in the first storage node and the second storage node under the operation index attribute is longer than the standard storage duration;
if yes, updating the first storage node or the second storage node;
otherwise, no update is performed.
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 block diagram of an operation data acquisition system of a vending machine based on the internet of things in an embodiment of the invention;
FIG. 2 is a block diagram of a data acquisition module according to an embodiment of the present invention;
fig. 3 is a block diagram of a remote processing module 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 invention provides an automatic vending machine operation data acquisition system based on the Internet of things, as shown in fig. 1, comprising:
the system comprises a node establishing module, a node acquiring module and a node acquiring module, wherein the node establishing module is used for establishing an acquisition structure diagram aiming at each automatic vending machine based on the position and the name of each automatic vending machine, and the acquisition structure diagram comprises a plurality of acquisition nodes;
the data acquisition module is used for acquiring single operation data of a plurality of acquisition nodes in the acquisition structure diagram;
And the remote processing module is used for analyzing the single operation data based on the node relation among the plurality of acquisition nodes to obtain comprehensive operation data.
In this embodiment, one vending machine corresponds to one collection node.
In this embodiment, the single operation data is operation data of one collection node.
In this embodiment, the node relationship is used to represent a relationship between features affecting operational data between individual vending machines.
In this embodiment, the integrated operation data is used to represent integrated operation data of all vending machines, such as sales data, screen click time length, user stay time length, and the like.
The beneficial effects of above-mentioned design scheme are: through establishing the collection structure diagram of each vending machine, the classification of data collection is definitely improved, data collection efficiency, through according to the node relation between a plurality of collection nodes, analysis is carried out to single operation data, obtain comprehensive operation data, the quality of the operation data of having guaranteed to obtain for operation data can represent the operation condition of single vending machine and the operation influence condition between vending machine better, provides accurate operation data for the operation analysis of vending machine.
Example 2
Based on embodiment 1, the embodiment of the invention provides an operation data acquisition system of a vending machine based on the internet of things, which further comprises: and the communication module is connected with the data acquisition module and the remote processing module and is used for establishing communication connection between the data acquisition module and the remote processing module and remotely transmitting the single operation data acquired by the data acquisition module to the remote processing module.
The beneficial effects of above-mentioned design scheme are: by establishing the communication module, data transmission between the data acquisition module and the remote processing module is realized, and the acquisition of operation data is ensured.
Example 3
Based on embodiment 1, the embodiment of the invention provides an operation data acquisition system of a vending machine based on the internet of things, wherein the node establishment module comprises:
the identification building unit is used for building a first identification for the vending machine according to the positions and names of the vending machines in the target detection range;
the identification establishing unit is further used for determining the acquisition relation between the data acquisition module and the acquisition interface of the vending machine equipment based on the first identification, and establishing a second identification for the vending machine based on the acquisition relation;
And the node establishing unit is used for establishing acquisition nodes based on the first identifier and the second identifier, and finally obtaining an acquisition structure diagram.
In this embodiment, the first identifier is used to identify the location and name of the vending machine, and the first identifier is unique and the first identifiers of different vending machines are different.
In this embodiment, the second identifier is used to determine a relationship between the vending machine equipment acquisition interface and the acquired data, determining a source of the acquired data.
The beneficial effects of above-mentioned design scheme are: the acquisition structure diagram is built according to the information of each vending machine, so that the source of the acquired data is clarified, the acquired data is clearly managed, and a basis is provided for acquiring operation data.
Example 4
Based on embodiment 1, an embodiment of the present invention provides an operation data acquisition system of a vending machine based on the internet of things, as shown in fig. 2, where the data acquisition module includes:
the interface design unit is used for adding information of each sensor arranged on the vending machine to the acquisition node and arranging a data inlet corresponding to each sensor at the acquisition node;
The data acquisition unit is used for acquiring signals of each sensor and sending the signals to a data inlet of the acquisition node;
and the data analysis unit is used for analyzing based on the signals of the data inlet to obtain single operation data of the acquisition node.
In this embodiment, one sensor corresponds to one data entry.
In this embodiment, the sensor includes, for example, a camera for monitoring the number of people in front of the vending machine, etc.; the touch sensor is used for monitoring the clicking condition of the vending machine, the screen use time length and the like; the goods monitoring sensor is used for monitoring the vending conditions of the vending machine, and the consumption monitoring sensor is used for acquiring the sales of the vending machine.
The beneficial effects of above-mentioned design scheme are: the data acquired by each sensor is sent to the acquisition node, the acquisition and analysis of the sensing signals of each vending machine are concentrated, single operation data are obtained, the efficiency of the sensing signal analysis is guaranteed, the standard of the operation data is unified, and the quality of the single operation data is guaranteed.
Example 5
Based on embodiment 4, the embodiment of the invention provides an operation data acquisition system of a vending machine based on the internet of things, wherein the data acquisition unit further comprises:
A signal receiving unit for receiving signals from the signal output interfaces of the respective sensors;
the data sending unit is used for packaging the signals to obtain data packets and sending the data packets to the acquisition node;
and the data access unit is used for distributing signals in the data packet to the corresponding data entry based on the attribute of the data entry.
The beneficial effects of above-mentioned design scheme are: and after the signals of the signal output interfaces of the sensors are packaged and sent to a system comprising the acquisition node, the signals of each data inlet are determined according to the attribute of the data inlet, so that the accuracy of the signals acquired by the data inlet is ensured, and a data basis is provided for acquiring single operation data.
Example 6
Based on embodiment 4, the embodiment of the invention provides an operation data acquisition system of a vending machine based on the internet of things, wherein the data analysis unit comprises:
the data conversion unit is used for determining the corresponding relation between the signal characteristics and the parameters based on the attribute of the data entry, and determining the parameter value corresponding to the signal based on the corresponding relation;
the data standardization unit is used for carrying out standardization of a numerical form and a numerical unit on the parameter values according to the format of standardized operation data to obtain standard parameter values;
The data classification unit is used for acquiring an operation index corresponding to the standard parameter value, dividing the standard parameter value which can directly represent the operation index into a first standard parameter value, and dividing the standard parameter value which cannot directly represent the operation index into a second standard parameter value;
the data determining unit is used for taking the first standard parameter value as first single operation data;
the data classifying unit is further configured to divide the second standard parameter value into a third standard parameter value based on the operation index, divide the operation data into a plurality of parameter values, comprehensively represent the operation data into a fourth standard parameter value, and obtain a fourth standard parameter data set corresponding to each operation data;
the relation determining unit is used for acquiring a relation template of the second standard parameters and the operation indexes, and inputting the second standard parameters into the relation template to obtain second single operation data;
the model building unit is used for building an operation data determining model based on the historical parameter values and the historical operation data;
the data processing unit is used for analyzing the fourth standard parameter data set, establishing the influence degree of the fourth standard parameter values in the fourth standard parameter data set on the corresponding operation indexes, and determining a comprehensive parameter value set based on the fourth standard parameter data set and the influence degree;
The model running unit is used for inputting the comprehensive parameter value set into the operation data determining model and outputting to obtain third single operation data;
the data acquisition unit is used for integrating the first single operation data, the second single operation data, the third single operation data and the corresponding operation indexes thereof to obtain single operation data.
In this embodiment, the attribute of the data entry is consistent with the attribute of the access signal, if the attribute of the data entry is the screen use duration, the corresponding signal feature is screen brightness, the screen brightness signal feature is extracted as the in-use screen within the preset signal range, and the screen use duration is determined according to the length of the screen brightness signal feature within the preset signal range.
In this embodiment, the parameter values are standardized, so that the quality of the operation data is ensured, and visual comparison is facilitated.
In this embodiment, the first standard parameter value may be, for example, a parameter from which the sales number, the visitor number, and the like of each item can be directly used as an operation index.
In this embodiment, the third standard parameter value may be, for example, a user code parameter, which may be converted into a specific operation index by a relationship template: user identity.
In this embodiment, one operation index corresponds to a plurality of standard parameters, for example, the operation index is an actual browsing duration of a screen page, the corresponding fourth standard parameter data set is a screen brightness duration parameter and a stay time period parameter, and a stay time period parameter before a vending machine for a user, and the determined influence degree is an influence degree of the screen brightness duration parameter and the stay time period parameter before the vending machine for the user, the stay time period parameter of the screen brightness and the stay time period parameter before the vending machine for the user, the comprehensive numerical value set is a common stay time period of the screen brightness and the stay time period before the vending machine for the user, and the comprehensive parameter numerical value set can represent operation data more accurately than the fourth standard parameter data set.
The beneficial effects of above-mentioned design scheme are: according to the method, the device and the system, the parameter values corresponding to the signals are obtained according to the attributes of the data entry, and according to the relation between the parameter values and the operation indexes, different modes are adopted to analyze the parameter values of different types, so that single operation data representing the operation indexes are finally obtained, and the accuracy of the finally obtained single operation data is ensured by carrying out different analyses on the parameter values, so that the single operation data can intuitively and clearly represent the operation conditions of corresponding unmanned sellers.
Example 7
Based on embodiment 1, an embodiment of the present invention provides an operation data acquisition system of a vending machine based on the internet of things, as shown in fig. 3, where the remote processing module includes:
the matrix establishing unit is used for establishing a position information matrix of each acquisition node based on the position information of the plurality of acquisition nodes; based on the environmental information of the plurality of acquisition nodes, establishing an environmental information matrix of each acquisition node;
the matrix comparison unit is used for analyzing the position information matrix of the acquisition node to be analyzed and the position information matrix of other acquisition nodes to obtain a position difference matrix set; analyzing the environmental information matrix of the acquisition node to be analyzed and the environmental information matrix of other acquisition nodes to obtain an environmental difference matrix set;
the difference acquisition unit is used for acquiring the data difference between the single operation data of the acquisition node to be analyzed and other single operation data;
the matrix selection unit is used for acquiring a first target position difference matrix set with position difference within a first preset range from the position difference matrix and acquiring a first environment difference matrix set corresponding to the first target position difference matrix set;
The relation determining unit is used for analyzing the data difference based on the first target environment difference matrix set and determining a first influence relation of each environment factor on each operation index;
the matrix selection unit is further configured to obtain a second target environmental difference matrix set with environmental differences within a second preset range from the environmental difference matrices, and obtain a second position difference matrix set corresponding to the second target environmental difference matrix set;
the relationship determining unit is further configured to analyze the data difference based on the second target position difference matrix set, and determine a second influence relationship of each position factor on each operation index;
the relation adjusting unit is used for determining a first adjusting strategy according to the distribution of the position difference in a first preset range and adjusting the first influence relation to obtain a third influence relation; determining a second adjustment strategy according to the distribution of the environmental difference in a second preset range, and adjusting the second influence relation to obtain a fourth influence relation;
the data making unit is used for determining operation restriction data among a plurality of acquisition nodes based on the third influence relation; determining operational environment data between a plurality of collection nodes based on the fourth influence relationship;
And the data integration unit is used for obtaining comprehensive operation data based on the single operation data, the operation restriction data and the operation environment data.
In this embodiment, the location element included in the location information matrix is a distance from other vending machines.
In this embodiment, the environmental elements contained in the environmental difference matrix are, for example, traffic flow, building concentration, and the like.
In this embodiment, the first influence relationship is an influence on an operation index, such as a vending machine access number and a sales number, caused by a difference of each environmental factor.
In this embodiment, the second influence relationship is an influence on an operation index, such as the number of accesses to the vending machine, the number of sales, caused by the difference of each location factor.
In this embodiment, the more uniform the position difference is distributed within the first preset range, the smaller the amplitude of the corresponding first adjustment policy, and the second adjustment policy adjusts the first influence relationship and the second influence relationship, so that the obtained third influence relationship avoids errors caused by the position relationship, and the obtained fourth influence relationship avoids errors caused by the environmental information.
The beneficial effects of above-mentioned design scheme are: the position distribution distance of the automatic vending machine, operation restriction data for operation, operation environment data of surrounding environments of the automatic vending machine and single operation data are determined according to the node position relation among all the acquisition nodes, the node environment relation and the difference of the single operation data, and comprehensive operation data are formed together, so that the obtained operation data are considered from the angle of the single automatic vending machine and the comparison angle among a plurality of automatic vending machines in a target area range, the obtained comprehensive operation data are more comprehensive and accurate, the operation data can better represent the operation condition of the single automatic vending machine and the operation influence condition among the automatic vending machines, and accurate operation data are provided for operation analysis of the automatic vending machines.
Example 8
Based on embodiment 7, the embodiment of the invention provides an operation data acquisition system of a vending machine based on the internet of things, wherein the relationship determining unit comprises:
the characteristic acquisition unit is used for extracting the environmental elements at the same position in each first target environmental difference matrix in the first target environmental difference matrix set, comparing the environmental elements to obtain the environmental difference characteristics of the environmental elements, acquiring the data differences corresponding to the environmental difference characteristics of all the environmental elements, and determining the data difference characteristics;
The relation establishing unit is used for establishing a causal relation between the environmental difference characteristic and the data difference characteristic and acquiring a causal relation set of all first target environmental difference matrixes in the first target environmental difference matrix set;
and the relation processing unit is used for carrying out average processing on the causal relation set and determining a first influence relation of each environmental factor on each operation index according to an average processing result.
In this embodiment, averaging the set of causal relationships determines a target causal relationship capable of representing all causal relationships in the set of causal relationships, the target causal relationship obtained using an averaging process.
The beneficial effects of above-mentioned design scheme are: the method comprises the steps of determining the difference of operation data caused by the environmental difference, establishing a relation between the environmental difference and the data difference, and processing the relation to obtain a final first influence relation, so that the suitability of the first influence relation and vending machines under different environments is ensured, and a basis is provided for determining accurate comprehensive operation data.
Example 9
Based on embodiment 1, the embodiment of the invention provides an operation data acquisition system of a vending machine based on the internet of things, which is characterized by further comprising: the data storage module is used for storing the comprehensive operation data;
The data storage module comprises:
the data dividing unit is used for dividing the operation index into a short-time storage type and a long-time storage type according to the attribute of the operation index, and dividing the comprehensive operation data into first comprehensive operation data and second comprehensive operation data based on the storage type;
a mode determining unit, configured to determine a data storage mode of the first comprehensive operation data and the second comprehensive operation data based on a data capacity and a storage duration of the first comprehensive operation data and the second comprehensive operation data, respectively;
and the data storage unit is used for respectively storing the first comprehensive operation data and the second comprehensive operation data according to the data storage mode.
In this embodiment, the operation index of the first comprehensive operation data is a short-time storage type, and the operation index of the second comprehensive operation data is a long-time storage type.
In this embodiment, the data storage means includes a selection of data from the storage node and an automatic update duration interval of data.
The beneficial effects of above-mentioned design scheme are: by selecting a proper storage mode for the comprehensive operation data according to different storage requirements for different comprehensive operation data, timeliness of the comprehensive operation data is guaranteed, searching of the comprehensive operation data by a user is facilitated, and quality of the stored comprehensive operation data is guaranteed.
Example 10
Based on embodiment 9, the embodiment of the invention provides an operation data acquisition system of a vending machine based on the internet of things, wherein the mode determining unit comprises:
the sorting unit is used for determining the matching degree of each storage node and the second comprehensive data according to the storage capacity and the storage node attribute of the storage node in the storage space and the data capacity and the storage time length of the second comprehensive operation data, and sorting the storage nodes according to the numerical value of the matching degree;
matching degree P of the storage node and the second comprehensive data r The calculation formula of (2) is as follows:
wherein K is t The characteristic value corresponding to the storage time length of the second comprehensive data is (0, 1), and gamma is taken as the value r Node attribute values representing current storage nodes are (0, 1), τ represents storage rate of the current storage nodes, E (t) represents data capacity average values of the second comprehensive data under different operation indexes, E (r) represents historical storage average values of the current storage nodes, E (0) represents standard storage average values, and E (r) represents standard storage average values>E(0)>E(t)；
The node selection unit is used for selecting a second storage node of the second comprehensive operation data according to the sequencing result of the storage nodes;
The node selection unit is further configured to select a first storage node for the first comprehensive operation data from the remaining storage nodes in the same manner as the second comprehensive operation data is selected as the storage node;
the data updating unit is used for judging whether the actual storage duration of the comprehensive operation data stored in the first storage node and the second storage node under the operation index attribute is longer than the standard storage duration;
the calculation formula of the standard storage duration T is as follows:
wherein T is y Expressed in the operation index attributeThe history storage duration T min Representing a minimum preset storage duration of the integrated operation data stored under the first storage node or the second storage node,
an average preset storage duration representing the comprehensive operation data stored under the first storage node or the second storage node, n represents a data amount of the comprehensive operation data stored under the first storage node or the second storage node, and T i Representing a preset storage duration of the ith operation data stored under the first storage node or the second storage node;
if yes, updating the first storage node or the second storage node;
Otherwise, no update is performed.
In this embodiment, the longer the storage period, the larger the corresponding feature value.
In this embodiment, the node attribute value is related to a storage node attribute of a storage node, where the storage node attribute is related to a retention time of the storage node on the storage data, and the longer the retention time, the larger the corresponding node attribute value.
In this embodiment, since the second comprehensive operation data is data that needs to be stored for a long period of time as compared with the first comprehensive operation data, in order to secure the storage quality of the second comprehensive operation data, a storage node is first selected for the second comprehensive operation data.
In this embodiment, the storage rate of the current storage node is the ratio of the storage data capacity to the storage space capacity of the current storage node during the history storage.
In this embodiment, the standard storage duration is determined by the nature of the specific comprehensive operational data stored by the storage node.
In this embodiment, for the formula
It may for example be that,
in this embodiment, for the formula
For example, T may be y ＝100，T min ＝80，/>
T=85, in days.
The beneficial effects of above-mentioned design scheme are: by selecting a proper storage node for the neutral operation data according to the characteristics of the comprehensive operation data and the characteristics of the storage node, and setting reasonable updating time according to the storage time of the comprehensive operation data, the comprehensive operation data is updated in time, the timeliness of the comprehensive operation data is ensured, and an accurate data basis is provided for the decision of a user on the vending machine.
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. Automatic vending machine operation data acquisition system based on thing networking, its characterized in that includes:
the system comprises a node establishing module, a node acquiring module and a node acquiring module, wherein the node establishing module is used for establishing an acquisition structure diagram aiming at each automatic vending machine based on the position and the name of each automatic vending machine, and the acquisition structure diagram comprises a plurality of acquisition nodes;
the data acquisition module is used for acquiring single operation data of a plurality of acquisition nodes in the acquisition structure diagram;
the remote processing module is used for analyzing the single operation data based on the node relation among the plurality of acquisition nodes to obtain comprehensive operation data;
the remote processing module comprises:
the matrix establishing unit is used for establishing a position information matrix of each acquisition node based on the position information of the plurality of acquisition nodes; based on the environmental information of the plurality of acquisition nodes, establishing an environmental information matrix of each acquisition node;
The matrix comparison unit is used for analyzing the position information matrix of the acquisition node to be analyzed and the position information matrix of other acquisition nodes to obtain a position difference matrix set; analyzing the environmental information matrix of the acquisition node to be analyzed and the environmental information matrix of other acquisition nodes to obtain an environmental difference matrix set;
the difference acquisition unit is used for acquiring the data difference between the single operation data of the acquisition node to be analyzed and other single operation data;
the matrix selection unit is used for acquiring a first target position difference matrix set with position difference within a first preset range from the position difference matrix and acquiring a first environment difference matrix set corresponding to the first target position difference matrix set;
the relation determining unit is used for analyzing the data difference based on the first target environment difference matrix set and determining a first influence relation of each environment factor on each operation index;
the matrix selection unit is further configured to obtain a second target environmental difference matrix set with environmental differences within a second preset range from the environmental difference matrices, and obtain a second position difference matrix set corresponding to the second target environmental difference matrix set;
The relationship determining unit is further configured to analyze the data difference based on the second target position difference matrix set, and determine a second influence relationship of each position factor on each operation index;
the relation adjusting unit is used for determining a first adjusting strategy according to the distribution of the position difference in a first preset range and adjusting the first influence relation to obtain a third influence relation; determining a second adjustment strategy according to the distribution of the environmental difference in a second preset range, and adjusting the second influence relation to obtain a fourth influence relation;
the data making unit is used for determining operation restriction data among a plurality of acquisition nodes based on the third influence relation; determining operational environment data between a plurality of collection nodes based on the fourth influence relationship;
and the data integration unit is used for obtaining comprehensive operation data based on the single operation data, the operation restriction data and the operation environment data.
2. The internet of things-based vending machine operation data collection system of claim 1, further comprising: and the communication module is connected with the data acquisition module and the remote processing module and is used for establishing communication connection between the data acquisition module and the remote processing module and remotely transmitting the single operation data acquired by the data acquisition module to the remote processing module.
3. The vending machine operation data collection system based on the internet of things of claim 1, wherein the node establishment module comprises:
the identification building unit is used for building a first identification for the vending machine according to the positions and names of the vending machines in the target detection range;
the identification establishing unit is further used for determining the acquisition relation between the data acquisition module and the acquisition interface of the vending machine equipment based on the first identification, and establishing a second identification for the vending machine based on the acquisition relation;
and the node establishing unit is used for establishing acquisition nodes based on the first identifier and the second identifier, and finally obtaining an acquisition structure diagram.
4. The vending machine operation data collection system based on the internet of things of claim 1, wherein the data collection module comprises:
the interface design unit is used for adding information of each sensor arranged on the vending machine to the acquisition node and arranging a data inlet corresponding to each sensor at the acquisition node;
the data acquisition unit is used for acquiring signals of each sensor and sending the signals to a data inlet of the acquisition node;
And the data analysis unit is used for analyzing based on the signals of the data inlet to obtain single operation data of the acquisition node.
5. The internet of things-based vending machine operation data acquisition system of claim 4, wherein the data acquisition unit further comprises:
a signal receiving unit for receiving signals from the signal output interfaces of the respective sensors;
the data sending unit is used for packaging the signals to obtain data packets and sending the data packets to the acquisition node;
and the data access unit is used for distributing signals in the data packet to the corresponding data entry based on the attribute of the data entry.
6. The internet of things-based vending machine operation data collection system of claim 4, wherein the data analysis unit comprises:
the data conversion unit is used for determining the corresponding relation between the signal characteristics and the parameters based on the attribute of the data entry, and determining the parameter value corresponding to the signal based on the corresponding relation;
the data standardization unit is used for carrying out standardization of a numerical form and a numerical unit on the parameter values according to the format of standardized operation data to obtain standard parameter values;
The data classification unit is used for acquiring an operation index corresponding to the standard parameter value, dividing the standard parameter value which can directly represent the operation index into a first standard parameter value, and dividing the standard parameter value which cannot directly represent the operation index into a second standard parameter value;
the data determining unit is used for taking the first standard parameter value as first single operation data;
the data classifying unit is further configured to divide the second standard parameter value into a third standard parameter value based on the operation index, divide the operation data into a plurality of parameter values, comprehensively represent the operation data into a fourth standard parameter value, and obtain a fourth standard parameter data set corresponding to each operation data;
the relation determining unit is used for acquiring a relation template of the second standard parameters and the operation indexes, and inputting the second standard parameters into the relation template to obtain second single operation data;
the model building unit is used for building an operation data determining model based on the historical parameter values and the historical operation data;
the data processing unit is used for analyzing the fourth standard parameter data set, establishing the influence degree of the fourth standard parameter values in the fourth standard parameter data set on the corresponding operation indexes, and determining a comprehensive parameter value set based on the fourth standard parameter data set and the influence degree;
The model running unit is used for inputting the comprehensive parameter value set into the operation data determining model and outputting to obtain third single operation data;
the data acquisition unit is used for integrating the first single operation data, the second single operation data, the third single operation data and the corresponding operation indexes thereof to obtain single operation data.
7. The vending machine operation data collection system based on the internet of things according to claim 1, wherein the relationship determination unit comprises:
the characteristic acquisition unit is used for extracting the environmental elements at the same position in each first target environmental difference matrix in the first target environmental difference matrix set, comparing the environmental elements to obtain the environmental difference characteristics of the environmental elements, acquiring the data differences corresponding to the environmental difference characteristics of all the environmental elements, and determining the data difference characteristics;
the relation establishing unit is used for establishing a causal relation between the environmental difference characteristic and the data difference characteristic and acquiring a causal relation set of all first target environmental difference matrixes in the first target environmental difference matrix set;
and the relation processing unit is used for carrying out average processing on the causal relation set and determining a first influence relation of each environmental factor on each operation index according to an average processing result.
8. The internet of things-based vending machine operation data collection system of claim 1, further comprising: the data storage module is used for storing the comprehensive operation data;
the data storage module comprises:
the data dividing unit is used for dividing the operation index into a short-time storage type and a long-time storage type according to the attribute of the operation index, and dividing the comprehensive operation data into first comprehensive operation data and second comprehensive operation data based on the storage type;
a mode determining unit, configured to determine a data storage mode of the first comprehensive operation data and the second comprehensive operation data based on a data capacity and a storage duration of the first comprehensive operation data and the second comprehensive operation data, respectively;
and the data storage unit is used for respectively storing the first comprehensive operation data and the second comprehensive operation data according to the data storage mode.
9. The vending machine operation data collection system based on the internet of things of claim 8, wherein the mode determining unit comprises:
the sorting unit is used for determining the matching degree of each storage node and the second comprehensive data according to the storage capacity and the storage node attribute of the storage node in the storage space and the data capacity and the storage time length of the second comprehensive operation data, and sorting the storage nodes according to the numerical value of the matching degree;
The node selection unit is used for selecting a second storage node of the second comprehensive operation data according to the sequencing result of the storage nodes;
the node selection unit is further configured to select a first storage node for the first comprehensive operation data from the remaining storage nodes in the same manner as the second comprehensive operation data is selected as the storage node;
the data updating unit is used for judging whether the actual storage duration of the comprehensive operation data stored in the first storage node and the second storage node under the operation index attribute is longer than the standard storage duration;
if yes, updating the first storage node or the second storage node;
otherwise, no update is performed.
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