CN116342170A - Monitoring processing terminal and monitoring method based on sales data - Google Patents
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Abstract
The invention relates to the technical field of sales data monitoring, and particularly discloses a monitoring processing terminal and a monitoring method based on sales data, wherein the method comprises the steps of monitoring order information in real time and establishing a sales database; acquiring production data in real time, and establishing a mapping relation between the production data and a sales database; inputting the sales database into a preset identification model to obtain an identification result, and determining an abnormal value of production data according to the identification result and the mapping relation; comparing the abnormal value with a preset abnormal threshold value, and generating a monitoring guide according to the comparison result. According to the method, the order information is acquired in real time, the order information is clustered according to the generation progress of the order information, the clustered order information is segmented, and compared with the segmented order information, the sales data which are obviously different are positioned, and then the production data corresponding to the sales data are inquired, so that the production data with more analysis value are acquired, and the cost utilization rate is greatly improved.
Description
Technical Field
The invention relates to the technical field of sales data monitoring, in particular to a monitoring processing terminal and a monitoring method based on sales data.
Background
The existing products are mostly distributed, and the same product can be sold through a plurality of sales terminals for serving clients in different areas.
Along with the development of society and technology, the existing sales terminals are mostly provided with intelligent equipment, the intelligent equipment is generally used for checkout, sales data can be continuously recorded in the checkout process, the value of the sales data is extremely high, the sales data is analyzed, and the production process can be reversely monitored.
However, in the prior art, for the monitoring process of the production process, the monitoring process is generally completed in the production stage, and the production party inputs a large amount of cost in the production stage, so that the quality inspection capability is continuously improved, and as the quality inspection capability is limited and the cost is improved, the improvement rate of the quality inspection capability is continuously reduced, that is, the cost reaches a certain degree, the cost is continuously improved, and the production monitoring capability is not improved too much; how to solve the problem of the technical proposal of the invention which improves the cost utilization rate by means of sales data on the basis of the prior art.
Disclosure of Invention
The invention aims to provide a monitoring processing terminal and a monitoring method based on sales data, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method of monitoring based on sales data, the method comprising:
monitoring order information in real time, and establishing a sales database according to the order information; wherein the sales database contains time nodes;
acquiring production data in real time, and establishing a mapping relation between the production data and a sales database;
inputting the sales database into a preset identification model to obtain an identification result, and determining an abnormal value of production data according to the identification result and the mapping relation;
comparing the abnormal value with a preset abnormal threshold value, and generating a monitoring guide according to the comparison result.
As a further scheme of the invention: the step of monitoring order information in real time and establishing a sales database according to the order information comprises the following steps:
monitoring order requests containing position information in real time, and clustering the order requests according to the position information;
monitoring the generation progress of each order request in real time, and carrying out numerical marking on the order requests according to the generation progress; wherein the numerical value is used to characterize the status of the order;
counting the numerical value marking information of various order requests, and inputting the numerical value marking information into a preset scoring model to obtain regional scores;
and establishing a sub-database according to the regional scores, inputting an order request containing a numerical value mark into the sub-database, and connecting the sub-database to obtain a sales database.
As a further scheme of the invention: the step of establishing a sub-database according to the regional scores, inputting an order request containing a numerical value mark into the sub-database, connecting the sub-database, and obtaining a sales database comprises the following steps:
determining a database scale of the sub-database according to the regional scores;
reading order requests containing numerical value marks, and determining data storage nodes according to the number relation of each order request; the data storage nodes are in a time format and correspond to the numerical values;
inputting an order request into a sub-database based on the data storage node;
and establishing connection relations of different sub-databases according to the values corresponding to the data storage nodes to obtain a sales database.
As a further scheme of the invention: the step of acquiring production data in real time and establishing a mapping relation between the production data and a sales database comprises the following steps:
reading the recorded production flow containing the production time; the production time is updated in real time;
acquiring production data in real time, and determining corresponding data storage nodes of the production data in a sales database according to time information in the production data and production time in a production flow;
and establishing a mapping relation between the production data and the data storage nodes corresponding to the production data.
As a further scheme of the invention: the step of inputting the sales database into a preset recognition model to obtain a recognition result, and determining the abnormal value of the production data according to the recognition result and the mapping relation comprises the following steps:
splitting the sales database according to the data storage nodes to obtain sub-database clusters; the sub database cluster takes a numerical value as a label;
splitting all sub-databases in the sub-database cluster according to a preset data step length to obtain a data segment;
traversing the sub-database cluster according to the data segment, and calculating similarity to obtain a similarity matrix;
performing numerical analysis on the similarity matrix to determine abnormal values of the data segments;
and inquiring production data corresponding to the data segment based on the mapping relation, and taking the abnormal value of the data segment as the abnormal value of the production data.
As a further scheme of the invention: the step of calculating the similarity according to the data segment traversing the sub-database cluster to obtain a similarity matrix comprises the following steps:
acquiring the length of a data segment, and copying the reference data segment with the corresponding length in the sub-database in sequence;
inputting the data segment and the reference data segment into a preset numerical conversion model to obtain a first vector and a second vector which correspond to the data segment and the reference data segment respectively;
calculating the similarity of the first vector and the second vector according to a preset calculation formula;
recording the similarity corresponding to all the reference data segments, and arranging the similarity according to the positions of the reference data segments in the sub-databases and the positions of the sub-databases in the sub-database clusters to obtain a similarity matrix;
the calculation formula is as follows:
;
wherein S is similarity, and A and B are a first vector and a second vector respectively.
The technical scheme of the invention also provides a monitoring processing terminal based on sales data, which comprises:
the database establishing module is used for monitoring order information in real time and establishing a sales database according to the order information; wherein the sales database contains time nodes;
the mapping relation determining module is used for acquiring production data in real time and establishing a mapping relation between the production data and the sales database;
the abnormal value calculation module is used for inputting the sales database into a preset identification model to obtain an identification result, and determining abnormal values of production data according to the identification result and the mapping relation;
and the monitoring guide generation module is used for comparing the abnormal value with a preset abnormal threshold value and generating a monitoring guide according to the comparison result.
As a further scheme of the invention: the database establishment module comprises:
the clustering unit is used for monitoring the order requests containing the position information in real time and clustering the order requests according to the position information;
the numerical value marking unit is used for monitoring the generation progress of each order request in real time and marking the numerical value of the order request according to the generation progress; wherein the numerical value is used to characterize the status of the order;
the regional grading unit is used for counting the numerical value marking information of various order requests, inputting the numerical value marking information into a preset grading model and obtaining regional grading;
and the database connection unit is used for establishing a sub-database according to the regional scores, inputting the order request containing the numerical value marks into the sub-database, and connecting the sub-database to obtain the sales database.
As a further scheme of the invention: the database connection unit includes:
a scale determining subunit, configured to determine a database scale of the sub-database according to the area score;
the node determining subunit is used for reading the order requests containing the numerical value marks and determining a data storage node according to the quantity relation of each order request; the data storage nodes are in a time format and correspond to the numerical values;
a data input sub-unit for inputting an order request into a sub-database based on the data storage node;
and the relation establishing subunit is used for establishing the connection relation of different sub-databases according to the numerical values corresponding to the data storage nodes to obtain the sales database.
As a further scheme of the invention: the mapping relation determining module comprises:
the flow reading unit is used for reading the recorded production flow containing the production time; the production time is updated in real time;
the node query unit is used for acquiring production data in real time, and determining corresponding data storage nodes of the production data in the sales database according to time information in the production data and production time in a production flow;
and the establishing execution unit is used for establishing a mapping relation between the production data and the corresponding data storage nodes.
Compared with the prior art, the invention has the beneficial effects that: according to the method, order information is acquired in real time, the order information is clustered according to the generation progress of the order information, the clustered order information is segmented, and compared with the segmented order information, the sales data with obvious differences are located; on the basis that the production flow is standardized, the production data corresponding to the sales data is queried, so that the production data with more analysis value is obtained, and the cost utilization rate is greatly improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 is a flow chart diagram of a monitoring method based on sales data.
Fig. 2 is a first sub-flowchart block diagram of a sales data based monitoring method.
Fig. 3 is a second sub-flowchart block diagram of a sales data based monitoring method.
Fig. 4 is a third sub-flowchart block diagram of a sales data based monitoring method.
Fig. 5 is a block diagram showing the constitution of a monitoring processing terminal based on sales data.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Fig. 1 is a flow chart of a monitoring method based on sales data, and in an embodiment of the present invention, a monitoring method based on sales data includes:
step S100: monitoring order information in real time, and establishing a sales database according to the order information; wherein the sales database contains time nodes;
for a product, when a purchase request input by a customer is received, a corresponding order is generated; counting all order information, and establishing a database called a sales database; in addition, since there is a time span of order-payment in the generation of order information, the status of different order information at different times is different, and when order information is counted, it is necessary to count paid orders and unpaid orders centering on the current time.
Step S200: acquiring production data in real time, and establishing a mapping relation between the production data and a sales database;
the production data is acquired in real time, a time span exists between production and sales of the product, the time span is acquired, and sales data corresponding to the production data can be inquired through the time span, wherein the corresponding relationship is called as a mapping relationship.
Step S300: inputting the sales database into a preset identification model to obtain an identification result, and determining an abnormal value of production data according to the identification result and the mapping relation;
under the existing sales architecture, the number of sales terminals is large, and each sales terminal is provided with intelligent equipment (at least a cash register), so that sales data can be obtained through the intelligent equipment; and (3) analyzing the sales data, and when the sales data is abnormal, directly positioning the corresponding production data by means of the mapping relation, so as to control the production links.
Step S400: comparing the abnormal value with a preset abnormal threshold value, and generating a monitoring guide according to a comparison result;
when the sales data is analyzed, the degree of abnormality of the sales data at different moments is calculated and represented by an abnormal value; the calculated outliers can be synchronized as outliers of the production data; when the abnormal value reaches an abnormal threshold preset by a worker, generating a monitoring guide; the monitoring guidelines may be pre-created by a manager, analogous to a work task.
FIG. 2 is a first sub-flowchart of a method for monitoring sales data, wherein the step of monitoring order information in real time and establishing a sales database according to the order information comprises:
step S101: monitoring order requests containing position information in real time, and clustering the order requests according to the position information;
monitoring an order request in real time, and sending a position information acquisition request (for shipping) when the order request is received, wherein if a client refuses to share a position, a subsequent process cannot be carried out; if the client agrees to send the position information, clustering the order requests according to the position information.
Step S102: monitoring the generation progress of each order request in real time, and carrying out numerical marking on the order requests according to the generation progress; wherein the numerical value is used to characterize the status of the order;
the invention discloses a method for generating orders, which comprises the steps of sending an order request to a user, wherein a time span exists between the user sending the order request and the completion of the order, and the generation progress of the order is different at different moments.
Step S103: counting the numerical value marking information of various order requests, and inputting the numerical value marking information into a preset scoring model to obtain regional scores;
on the basis of clustering the order requests, acquiring numerical values of all order information, judging the conditions of various order information according to the numerical values, and representing by regional scores; the number of completed orders in some areas is large, the number of outstanding orders in some areas is large, the situation of different areas is different, the area score is reflected, and in general, the area score is proportional to the proportion of completed orders.
Step S104: establishing a sub-database according to the regional scores, inputting an order request containing a numerical value mark into the sub-database, and connecting the sub-database to obtain a sales database;
the databases established by the scores of different areas are different, and after the establishment of the databases is completed, the databases with different areas are connected, so that the sales database can be obtained.
Further, the step of establishing a sub-database according to the regional score, inputting an order request containing a numerical value mark into the sub-database, connecting the sub-database, and obtaining a sales database includes:
determining a database scale of the sub-database according to the regional scores;
reading order requests containing numerical value marks, and determining data storage nodes according to the number relation of each order request; the data storage nodes are in a time format and correspond to the numerical values;
inputting an order request into a sub-database based on the data storage node;
and establishing connection relations of different sub-databases according to the values corresponding to the data storage nodes to obtain a sales database.
The regional scores are used for determining sub-databases corresponding to various order requests, then the order numbers of different states are determined according to the numerical marks, and according to the proportion among the order numbers, data storage nodes which are in a time format (relative time) and used for representing the generation progress can be determined.
Finally, inputting the order requests into the corresponding positions to obtain sub databases corresponding to various order requests; and the different sub-databases share the same set of data storage nodes, and are connected according to the corresponding relation of the data storage nodes, so that the sales database can be obtained.
It is worth mentioning that different sub-databases are used to represent differences in the areas.
FIG. 3 is a block diagram of a second sub-process of a sales data based monitoring method, wherein the step of acquiring production data in real time and establishing a mapping relationship between the production data and a sales database includes:
step S201: reading the recorded production flow containing the production time; the production time is updated in real time;
step S202: acquiring production data in real time, and determining corresponding data storage nodes of the production data in a sales database according to time information in the production data and production time in a production flow;
step S203: and establishing a mapping relation between the production data and the data storage nodes corresponding to the production data.
The above-mentioned content defines the establishment process of the mapping relation, and the core of the method is that the time span (production time) of the production flow is monitored in real time, when the production data is obtained, the time information of the production data is recorded, the corresponding sales time can be calculated approximately according to the time information and the time span, the data storage node closest to the sales time is obtained, and the mapping relation is established.
Fig. 4 is a third sub-flowchart of a monitoring method based on sales data, wherein the step of inputting the sales database into a preset recognition model to obtain a recognition result, and determining an outlier of production data according to the recognition result and the mapping relationship includes:
step S301: splitting the sales database according to the data storage nodes to obtain sub-database clusters; the sub database cluster takes a numerical value as a label;
when analyzing the sub-database, the order requests in different states are analyzed respectively; and splitting each sub-database in the sales database according to the data storage node, and reserving the connection relation among the sub-databases to obtain a sub-database set called a sub-database cluster.
Step S302: splitting all sub-databases in the sub-database cluster according to a preset data step length to obtain a data segment;
for a specific analysis process, a worker selects a data step length, and segments data items in the sub-database to obtain the database; the smaller the data step length is, the higher the analysis duration is, and the more accurate the analysis result is.
Step S303: traversing the sub-database cluster according to the data segment, and calculating similarity to obtain a similarity matrix;
after the data segments are segmented, the data segments and all other databases are compared in turn, so that the similarity can be obtained, and when the similarity of the two data segments is calculated, the calculation is repeated differently because the similarity of the two data segments is the same.
Step S304: performing numerical analysis on the similarity matrix to determine abnormal values of the data segments;
the similarity matrix represents the repeated condition among sales data in the sub-databases, under the normal condition, the order requests in the same state are similar, and if the difference between one order request and other order requests is large, each numerical value in the corresponding similarity matrix is obviously in a low value, so that the data analysis is carried out on the similarity matrix, and the abnormal value of the data segment can be obtained.
For the order request, the generated order information includes price information, demand information, logistics information, after-sales information and the like of the product, and the price information, the demand information, the logistics information, the after-sales information and the like are unified in the order request, and compared with the information, a plurality of obviously different sales information such as obvious large customers and the like can be positioned, so that when abnormal sales data occurs, the related production data needs to be analyzed; as for whether there is really an abnormality between the abnormality of the sales data and the production data, it is not important, but the analytical value of the corresponding production data is higher; the technical scheme of the invention aims to select production data with higher analysis value.
Step S305: inquiring production data corresponding to the data segment based on the mapping relation, and taking the abnormal value of the data segment as the abnormal value of the production data;
the outliers of the data segments may be directly used as outliers of the production data.
Further, the step of calculating the similarity according to the data segment traversing the sub-database cluster to obtain a similarity matrix includes:
acquiring the length of a data segment, and copying the reference data segment with the corresponding length in the sub-database in sequence;
inputting the data segment and the reference data segment into a preset numerical conversion model to obtain a first vector and a second vector which correspond to the data segment and the reference data segment respectively;
calculating the similarity of the first vector and the second vector according to a preset calculation formula;
recording the similarity corresponding to all the reference data segments, and arranging the similarity according to the positions of the reference data segments in the sub-databases and the positions of the sub-databases in the sub-database clusters to obtain a similarity matrix;
the calculation formula is as follows:
;
wherein S is similarity, and A and B are a first vector and a second vector respectively.
The above-mentioned contents define the comparison process of data segments, and the principle is that text data (data segments) are converted into numerical information by means of the existing data conversion model, the numerical information is represented by vectors, and then the similarity between two data segments can be obtained by adopting vector calculation rules.
Fig. 5 is a block diagram of the composition of a monitoring and processing terminal based on sales data, in which in an embodiment of the present invention, a monitoring and processing terminal based on sales data, the processing terminal 10 includes:
the database establishing module 11 is used for monitoring order information in real time and establishing a sales database according to the order information; wherein the sales database contains time nodes;
the mapping relation determining module 12 is used for acquiring production data in real time and establishing a mapping relation between the production data and a sales database;
the abnormal value calculation module 13 is configured to input the sales database into a preset recognition model, obtain a recognition result, and determine an abnormal value of production data according to the recognition result and the mapping relationship;
and the monitoring guide generation module 14 is used for comparing the abnormal value with a preset abnormal threshold value and generating a monitoring guide according to the comparison result.
Further, the database creation module 11 includes:
the clustering unit is used for monitoring the order requests containing the position information in real time and clustering the order requests according to the position information;
the numerical value marking unit is used for monitoring the generation progress of each order request in real time and marking the numerical value of the order request according to the generation progress; wherein the numerical value is used to characterize the status of the order;
the regional grading unit is used for counting the numerical value marking information of various order requests, inputting the numerical value marking information into a preset grading model and obtaining regional grading;
and the database connection unit is used for establishing a sub-database according to the regional scores, inputting the order request containing the numerical value marks into the sub-database, and connecting the sub-database to obtain the sales database.
Specifically, the database connection unit includes:
a scale determining subunit, configured to determine a database scale of the sub-database according to the area score;
the node determining subunit is used for reading the order requests containing the numerical value marks and determining a data storage node according to the quantity relation of each order request; the data storage nodes are in a time format and correspond to the numerical values;
a data input sub-unit for inputting an order request into a sub-database based on the data storage node;
and the relation establishing subunit is used for establishing the connection relation of different sub-databases according to the numerical values corresponding to the data storage nodes to obtain the sales database.
Still further, the mapping determining module 12 includes:
the flow reading unit is used for reading the recorded production flow containing the production time; the production time is updated in real time;
the node query unit is used for acquiring production data in real time, and determining corresponding data storage nodes of the production data in the sales database according to time information in the production data and production time in a production flow;
and the establishing execution unit is used for establishing a mapping relation between the production data and the corresponding data storage nodes.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. A method of monitoring based on sales data, the method comprising:
monitoring order information in real time, and establishing a sales database according to the order information; wherein the sales database contains time nodes;
acquiring production data in real time, and establishing a mapping relation between the production data and a sales database;
inputting the sales database into a preset identification model to obtain an identification result, and determining an abnormal value of production data according to the identification result and the mapping relation;
comparing the abnormal value with a preset abnormal threshold value, and generating a monitoring guide according to the comparison result.
2. The sales data based monitoring method of claim 1, wherein the step of monitoring order information in real time and creating a sales database based on the order information comprises:
monitoring order requests containing position information in real time, and clustering the order requests according to the position information;
monitoring the generation progress of each order request in real time, and carrying out numerical marking on the order requests according to the generation progress; wherein the numerical value is used to characterize the status of the order;
counting the numerical value marking information of various order requests, and inputting the numerical value marking information into a preset scoring model to obtain regional scores;
and establishing a sub-database according to the regional scores, inputting an order request containing a numerical value mark into the sub-database, and connecting the sub-database to obtain a sales database.
3. The sales data based monitoring method of claim 2, wherein the step of creating a sub-database based on the regional scores, inputting an order request containing a numerical label into the sub-database, connecting the sub-databases, and obtaining a sales database comprises:
determining a database scale of the sub-database according to the regional scores;
reading order requests containing numerical value marks, and determining data storage nodes according to the number relation of each order request; the data storage nodes are in a time format and correspond to the numerical values;
inputting an order request into a sub-database based on the data storage node;
and establishing connection relations of different sub-databases according to the values corresponding to the data storage nodes to obtain a sales database.
4. The sales data based monitoring method of claim 3, wherein the step of acquiring production data in real time and establishing a mapping relationship between the production data and a sales database comprises:
reading the recorded production flow containing the production time; the production time is updated in real time;
acquiring production data in real time, and determining corresponding data storage nodes of the production data in a sales database according to time information in the production data and production time in a production flow;
and establishing a mapping relation between the production data and the data storage nodes corresponding to the production data.
5. The sales data-based monitoring method of claim 3, wherein the step of inputting the sales database into a preset recognition model to obtain a recognition result, and determining the abnormal value of the production data according to the recognition result and the mapping relationship comprises:
splitting the sales database according to the data storage nodes to obtain sub-database clusters; the sub database cluster takes a numerical value as a label;
splitting all sub-databases in the sub-database cluster according to a preset data step length to obtain a data segment;
traversing the sub-database cluster according to the data segment, and calculating similarity to obtain a similarity matrix;
performing numerical analysis on the similarity matrix to determine abnormal values of the data segments;
and inquiring production data corresponding to the data segment based on the mapping relation, and taking the abnormal value of the data segment as the abnormal value of the production data.
6. The sales data based monitoring method of claim 5, wherein the step of traversing the sub-database clusters from the data segment, calculating a similarity, and obtaining a similarity matrix comprises:
acquiring the length of a data segment, and copying the reference data segment with the corresponding length in the sub-database in sequence;
inputting the data segment and the reference data segment into a preset numerical conversion model to obtain a first vector and a second vector which correspond to the data segment and the reference data segment respectively;
calculating the similarity of the first vector and the second vector according to a preset calculation formula;
recording the similarity corresponding to all the reference data segments, and arranging the similarity according to the positions of the reference data segments in the sub-databases and the positions of the sub-databases in the sub-database clusters to obtain a similarity matrix;
the calculation formula is as follows:
;
wherein S is similarity, and A and B are a first vector and a second vector respectively.
7. A sales data based monitoring processing terminal, the processing terminal comprising:
the database establishing module is used for monitoring order information in real time and establishing a sales database according to the order information; wherein the sales database contains time nodes;
the mapping relation determining module is used for acquiring production data in real time and establishing a mapping relation between the production data and the sales database;
the abnormal value calculation module is used for inputting the sales database into a preset identification model to obtain an identification result, and determining abnormal values of production data according to the identification result and the mapping relation;
and the monitoring guide generation module is used for comparing the abnormal value with a preset abnormal threshold value and generating a monitoring guide according to the comparison result.
8. The sales data based monitoring processing terminal of claim 7, wherein the database creation module comprises:
the clustering unit is used for monitoring the order requests containing the position information in real time and clustering the order requests according to the position information;
the numerical value marking unit is used for monitoring the generation progress of each order request in real time and marking the numerical value of the order request according to the generation progress; wherein the numerical value is used to characterize the status of the order;
the regional grading unit is used for counting the numerical value marking information of various order requests, inputting the numerical value marking information into a preset grading model and obtaining regional grading;
and the database connection unit is used for establishing a sub-database according to the regional scores, inputting the order request containing the numerical value marks into the sub-database, and connecting the sub-database to obtain the sales database.
9. The sales data based monitoring processing terminal according to claim 8, wherein the database connection unit includes:
a scale determining subunit, configured to determine a database scale of the sub-database according to the area score;
the node determining subunit is used for reading the order requests containing the numerical value marks and determining a data storage node according to the quantity relation of each order request; the data storage nodes are in a time format and correspond to the numerical values;
a data input sub-unit for inputting an order request into a sub-database based on the data storage node;
and the relation establishing subunit is used for establishing the connection relation of different sub-databases according to the numerical values corresponding to the data storage nodes to obtain the sales database.
10. The sales data-based monitoring processing terminal of claim 9, wherein the mapping relation determination module includes:
the flow reading unit is used for reading the recorded production flow containing the production time; the production time is updated in real time;
the node query unit is used for acquiring production data in real time, and determining corresponding data storage nodes of the production data in the sales database according to time information in the production data and production time in a production flow;
and the establishing execution unit is used for establishing a mapping relation between the production data and the corresponding data storage nodes.
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