OPTIMIZED METHOD FOR COLLECTING DATA FROM COMMUNICATING METERS VIA A CELLULAR NETWORK AND SYSTEM FOR EXECUTING THE METHOD.

Abstract

The invention relates to a method for collecting, via a cellular communication network (1000), data available in a set of communicating meters, the method being carried out in a data collection system (100) connected to said network (1000), and the method comprising: obtaining initial representative information (S1) on the behavior of said meters; establishing representative parameters (S2) of the behavior of each of the communicating meters from said initial information; establishing a data collection schedule (S3) for all or part of said meters from said representative parameters of the behavior of each of the meters; and transmitting data collection messages (S4) to said meters for which said schedule has been established. The invention also relates to a data collection system (100) configured to carry out the method. Figure to be published with the abstract: Fig. 3

Description

Title of the invention: OPTIMIZED METHOD FOR COLLECTING DATA FROM COMMUNICATING METERS VIA A CELLULAR NETWORK AND SYSTEM FOR EXECUTING THE PROCESS. technical field

[0001] The present invention relates to a method for collecting data available in communicating meters, performed by a data collection system and operated via a cellular network. At least one embodiment of the invention relates to a method for collecting data from electricity, water, or gas meters configured to communicate with a data concentrator via a mobile phone operator's IP network. STATE OF PRIOR ART

[0002] Modern electricity consumption meters are electronic devices, described as "smart" and / or "communicating," capable of generating and transmitting data to a server or remote system performing data collection functions, such as a consumption management server, via various communication networks, particularly for pricing and data collection services related to electricity, water, or gas consumption. Some of these meters are configured to transmit the data they generate via a cellular communication network, such as, for example, a mobile phone operator's network. Such a network consists of a set of communication cells, each cell of which is covered by a transmitting and receiving station called a base station.In other words, the area covered by the cellular communication network is divided into cells, each containing a base station. Each cell contains one or more communicating meters that generate information useful for managing one or more service or product deliveries. It is therefore necessary to collect data from these meters, taking into account the communication capacity of each cell in the communication network. Current techniques for collecting information from the meters are likely to cause congestion in the cellular communication network, especially since the communicating meters are sometimes in standby mode between two data processing or collection operations. This means that it is first necessary to control the meter to wake it up, and then to collect the necessary information. Others... Sometimes meters are temporarily out of network range due to disruptions. When data collection targeting a meter fails, it must be repeated until the data is collected, resulting in redundant use of network resources for the same information. Therefore, there is a need to optimize data collection methods to obtain large amounts of data from a set of meters, according to imposed scheduling constraints, while limiting the use of network resources that are already being used for other purposes. This situation can be improved. Description of the invention

[0003] The invention aims to provide a method for collecting data from communicating meters, according to calendar constraints, while limiting the use of network resources.

[0004] To this end, a method is proposed for collecting, via a cellular communication network, data available in a set of communicating meters, the method being carried out in a data collection system connected to said network and the method comprising:

[0005] - i) obtain initial information representative of the behavior of said counters,

[0006] - ii) establish parameters representative of the behavior of each of the counters communicating based on this initial information,

[0007] - iii) establish a data collection schedule for all or part of said counters based on said parameters representing the behavior of each of the counters, and,

[0008] - iv) transmit data collection messages to said meters according to said order established.

[0009] Advantageously, by learning the typical behavior of each communicating meter, it is thus possible to schedule data collection from a meter only when it has the highest probability of responding (and therefore of responding on the first attempt). The sharing of the cellular communication network is then carried out in a reasonable manner between the data collection system and the other applications that use the network. Furthermore, such a method advantageously allows for the detection of seasonality in the behavior of communicating meters connected to the cellular communication network and for taking this into account when scheduling data collection.

[0010] The method according to the invention may further comprise the optional features considered individually or in combination:

[0011] - Said initial information includes at least: • a cell identifier for said network with reference to a communicating meter identifier, • a state of obtaining a response from a counter to a message addressed to it with reference to a timestamp, • a response time of a counter to a message addressed to it, where applicable, with reference to a timestamp.

[0012] Thus, it is possible to determine behavior parameters for each of the counters, such as an average response time and an average response rate for each counter, depending on the day and time, and with reference to a cell through which it is accessible.

[0013] - Establishing a data collection schedule based on the established parameters is carried out with reference, for each of the communicating meters, to a cell identifier from which a communicating meter is accessible.

[0014] - Establish the parameters representative of the behavior of each of the counters includes a statistical analysis.

[0015] - The statistical analysis is carried out with reference to a maximum collection time.

[0016] - The statistical analysis is performed with reference to minimal use of the network of communication.

[0017] - Establish the parameters representative of the behavior of each of the counters includes learning through a classifier-type module.

[0018] - Learning by means of a classifier-type module is carried out with reference to a maximum collection time.

[0019] - Learning by means of a classifier-type module is carried out with reference to minimal use of the communication network.

[0020] The invention also relates to a system (or device) for collecting, via a cellular communication network, data available in a set of communicating meters, the collection system comprising electronic circuitry configured to:

[0021] - i) obtain initial information representative of the behavior of said counters,

[0022] - ii) establish parameters representative of the behavior of each of the counters communicating based on this initial information,

[0023] - iii) establish a data collection schedule for all or part of said counters based on said parameters representing the behavior of each of the counters, and,

[0024] - iv) transmit data collection messages to said meters according to said order established.

[0025] The collection system according to the invention may further include the characteristics- Optional risks considered individually or in combination:

[0026] - The data collection system further includes configured circuitry to process the aforementioned initial information, including: • a cell identifier for said network with reference to a communicating meter identifier, • a state of obtaining a response from a counter to a message addressed to it with reference to a timestamp, • a response time of a counter to a message addressed to it, where applicable, with reference to a timestamp.

[0027] - The data collection system includes electronic circuitry configured to establish a data collection schedule based on parameters established with reference, for each of the communicating meters, to a cell identifier from which a communicating meter is accessible.

[0028] - The data collection system further includes electronic circuitry configured to establish parameters representative of the behavior of each of the counters by performing a statistical analysis.

[0029] - The data collection system further includes electronic circuitry configured to perform statistical analysis with reference to a maximum collection time.

[0030] - The data collection system further comprises electronic circuitry configured to perform statistical analysis with minimal use of the communication network.

[0031] - The data collection system further comprises electrical circuitry tronic configured to establish the parameters representative of the behavior of each of the counters by learning using a classifier type module.

[0032] - The data collection system further comprises electronic circuitry configured to operate the learning by means of a classifier-type module, with reference to a maximum collection time.

[0033] Another object of the invention is a computer program product comprising program code instructions to execute the steps of a process as previously described, when this program is executed by a processor of a data collection system.

[0034] Finally, the invention also relates to an information storage medium comprising a computer program product as mentioned above. Brief description of the drawings

[0035] The features of the invention mentioned above, as well as others, will become clearer upon reading the following description of at least one example of implementation, the said description being made in relation to the attached drawings, among which:

[0036] [Fig. 1] schematically illustrates a communicating counter generating data to be collected, as already known;

[0037] [Fig.2] is a schematic representation of a communication network of the type cellular to which communicating meters are connected and comprising an improved system for collecting data generated by the meters, according to an embodiment;

[0038] [Fig.3] is a flowchart illustrating an improved data collection method executed in the communication network already shown in [Fig. 2], according to one embodiment; and,

[0039] [Fig.4] is a diagram illustrating an internal architecture of a system or device for collecting data generated by communicating meters, according to one embodiment.

[0040] DETAILED DESCRIPTION OF IMPROVEMENTS

[0041] Figure 1 schematically illustrates a communicating meter 10, already known in the prior art and configured to collect information on the supply of a physical quantity such as water, electricity, or gas, for example. Such a communicating meter is sometimes still called an intelligent meter or "smartmeter." The communicating meter 10 is arranged to operate a metering of a distributed physical quantity passing between an input 12 of the communicating meter 10, connected to a supply network for the physical quantity, and an output 13 of the communicating meter 10, connected to a domestic, industrial, or commercial installation, for example, that consumes the distributed physical quantity and whose consumption must be measured and / or controlled remotely.The communicating counter 10 includes a human-machine interface 14, also called a user interface, capable of inputting and displaying information related to the use of the counter. The communicating counter 10 further includes internal electronic circuitry comprising, in particular, one or more microcontrollers and a radio communication interface (not shown in the figure) connected to an antenna system 11. Thus, the communicating counter 10 is configured to communicate in a cellular communication network.

[0042] Figure 2 schematically illustrates a cellular communication network 1000, comprising a communication subnetwork 1001 to which three base stations STA-A, STA-B, and STA-C are connected. These base stations are configured to transmit and receive signals from and to third-party devices compatible with and connected to the communication network. In the example described, base station STA-A is connected to the communication subnetwork 1001 via a wired communication link. In communication subnetwork 1000a, base station STA-B is connected to subnetwork 1001 via a wired communication link 1000b, and base station STA-C is connected to subnetwork 1001 via a wired communication link 1000c. Of course, each of base stations STA-A, STA-B, or STA-C could also be connected to subnetwork 1001 via a wireless link. Each of base stations STA-A, STA-B, and STA-C covers a geographical area called a cell. According to the example described, base station STA-A covers cell A, base station STA-B covers cell B and base station STA-C covers cell C. Communicating counters 10a, 10b, 10c, lOd, 10e, lOf, 10g, lOh and lOi, similar to the communicating counter 10 previously described in relation to the [Fig.[l] are installed within the coverage area of ​​the 1000 communication network, established by combining the STA-A, STA-B, and STA-C base stations, which operate radio transmissions in cells A, B, and C, respectively. In the example described, the number of cells in the 1000 communication network, as well as the number of communicating meters, are intentionally reduced to facilitate reading and understanding this description. In reality, the number of cells for a data collection system can be in the tens, hundreds, or thousands, and the same applies to the number of meters involved in data collection.Although a communicating meter can be geographically located in several cells of the 1000 communication network, it is considered here that such a communicating meter, connected to a fixed installation, is fixed and is therefore connected to only one cell of the 1000 communication network at any given time. According to one embodiment, a communicating meter located in several cells is connected to the 1000 communication network via the cell whose base station offers it the best communication performance. Thus, according to the embodiment described in [Fig.[2] The communicating meters 10a, 10b and 10c are connected to the communication network 1000 via the base station STA-A operating in cell A, the communicating meters lOd, 10e and lOf are connected to the communication network 1000 via the base station STA-B operating in cell B and the communicating meters 10g, lOh and lOi are connected to the communication network 1000 via the base station STA-C operating in cell C. The communication network 1000 further includes a data collection system 100 configured to collect data generated by the communicating meters 10a, 10b, 10c, lOd, 10e, lOf, 10g, lOh and lOi. According to the example described, the data collection system 100 is connected to the communication subnetwork 1001 via a wired communication link 101. According to a variant, the data collection system 100 is connected to the communication subnetwork 1001 via a wireless link.The data collection system 100 is still referred to here as a data collection device or . a service provider's management server. Advantageously, the data collection system 100 is configured to implement a data collection process aimed at optimizing data collection by reducing the use of resources on the communication network 1000, which, being cellular, is intended to be shared with other uses or applications, including common mobile telephony or telecommunications applications over a mobile network (audio and / or video transmission, for example). Thus, the data collection system 100 is configured to execute an improved collection process described in relation to [Fig. 3].

[0043] Figure 3 illustrates steps of the improved data collection process carried out in the 1000 communication network or in a similar communication network.

[0044] A step S0 corresponds to an initialization step at the end of which the data collection system 100, as well as all the systems of the communication network 1000, are normally configured to perform data transmissions across the communication network 1000. Thus, at the end of step S0, the data collection system 100 is able to send messages to the various communicating meters 10a, 10b, 10c, lOd, 10e, lOf, 10g, lOh, and lOi via, depending on the communicating meter concerned by a message transmission, the base station to which it is connected. The data collection system 100 is further configured to receive messages sent by the communicating meters in response to messages addressed to them, provided that the latter have the capacity to respond.Indeed, some smart meters may be temporarily disrupted by obstacles to electromagnetic waves, poor operating conditions of the communication network, or due to potential standby cycles aimed at saving energy.

[0045] Cleverly, the improved data collection process executed by the system 100 for collecting data generated by the various communicating meters 10a, 10b, 10c, lOd, 10e, lOf, 10g, lOh and lOi operates four main stages SI, S2, S3 and S4, hereinafter referred to respectively as "labeling phase", "learning and planning phase", "scheduling phase" and "data collection phase".

[0046] During the SI step, and throughout a predefined period T (for example, a full week), the data collection system 100 sends protocol messages to the various communicating meters 10a, 10b, 10c, lOd, 10e, lOf, 10g, lOh, and lOi, and records a great deal of information relating to the behavior of the different meters. This involves determining whether or not a meter responds to a protocol message addressed to it and, if so, how long it takes. This information is recorded in correlation with an identifier of the The communicating meter in question. During this step SI, information relating to the cell of the communication network 1000 through which a communicating meter has responded or not responded is obtained and stored, so that all information can also be stored with reference to a cell identifier of the communication network 1000. The term "protocol message" here refers to any message in a predefined format that a meter is expected to be able to receive and interpret, so as to respond to it, and that the data collection system 100 also understands, or at the very least that it is capable of generating, and for which the data collection system 100 can identify a response or a lack of response. The format of the exchanged protocol messages is not described here insofar as it does not contribute to understanding the invention.The messages exchanged between the data collection system 100 and the smart meters can be exchanged as part of service provision transmissions, or simply as part of the search for information representative of the smart meters' operation, or through a combination of these two approaches. Thus, at the end of the period T for collecting information representative of the meters' operation, the data collection system 100 has information relating to each of the smart meters, due to the numerous communication attempts made via the message exchanges described.

[0047] According to one embodiment, the data collection system 100 identifies and records at least some interrelated information in the form of: - a cell identifier of the communication network 1000 with reference to a communicating meter identifier, - a state of receiving a response from a counter to a message addressed to it with reference to a timestamp, for example "response" or "no response" at the current time, - a response time of a counter to a message addressed to it, if applicable, and with reference to a timestamp, for example a given counter responded at such time, after a delay of 10 seconds.

[0048] According to one embodiment, the data collection system 100 has information concerning the meters for which data collection is to be carried out and therefore the meters to which the improved data collection method should be applied. Thus, the data collection system 100 has, for example, a list of identifiers of the relevant communicating meters and does not need to carry out any phase of discovering communicating meters present or potentially present in a geographical area covered by the communication network. communication 1000. For example, the data collection system 100 can send one or more messages to each of the meters in a list and then use information relating to the communication protocol of the cellular communication network 1000 to identify through which base station a meter could have been reached and, in fact, in which cell of the communication network it is located.

[0049] According to one embodiment, the information collected and stored during the SI step is grouped by cells of the communication network 1000, that is, with reference to a cell identifier of the communication network 1000.

[0050] For example, a set of information obtained during the so-called labeling phase, during the SI step, can be organized as in the table below:

[0051] [Table 1] Cell Identifier Date Time Cell Counter (d) (h) Response Time (s) (Ceid) (Coid) Response Time (tr) (r) A 10a 29 / 09 / 2023 00:00 response 0.1 A 10b 29 / 09 / 2023 00:00 response 0.2 A 10c 30 / 09 / 2023 00 :00 No response B lOd 09 / 30 / 2023 00:00 Response 0.1 B 10e 10 / 01 / 2023 00:15 Response 0.2 B lOf 10 / 01 / 2023 00:30 No response C 10g 10 / 01 / 2023 00:15 Response 0.3 C lOh 10 / 01 / 2023 00:45 Response 0.2 C lOi 10 / 02 / 2023 00:45 No response

[0052] Thus, cell identifier information, counter identifier, timestamp, response status and response time are correlated with each other in a memory of the data collection system 100.

[0053] During step S2, the data collection system 100 establishes operating parameters specific to each of the communicating meters involved in the collection, or in other words, the data collection system 100 establishes a profile behavior for each of the communicating meters, based on the information collected during the SI step over a period of time T and stored. This learning phase is carried out by proceeding by meter and by time slots, for example by meter and by hour or by meter and by quarter-hour, or even by meter and by five-minute slots, these examples being non-exhaustive. In a first embodiment, the data collection system 100 performs this learning phase by conducting a statistical analysis to determine when each communicating meter has the highest probability of responding to a message addressed to it, particularly a data collection message. In a second embodiment, the data collection system 100 performs this learning phase by using machine learning to provide a probability of successful communication for each meter involved in an upcoming data collection.

[0054] Each of these embodiments can be carried out according to variants having respectively the following objectives: - to comply with a time constraint for the end of data collection, such as, for example, aiming to have collected data from all meters before eight o'clock the following morning, or - to significantly reduce the use of resources of the communication network used (here the communication network 1000) in order to avoid any redundancy in sending messages to the counter until the data to be collected is obtained.

[0055] According to one embodiment, when the analysis is of a statistical type and when the objective is to respect a time constraint, the data collection system 100 determines, for each of the counters, a time slot for which this counter has the fastest collection time while having a minimum threshold of 50% in response rate to messages addressed to it.

[0056] According to one embodiment, when the analysis is of a statistical type and when the objective is to reduce the use of the resources of the communication network used, the data collection system 100 determines a time slot for which a counter has a maximum response rate to messages addressed to it.

[0057] In one embodiment, when the learning phase performs machine learning, the objective of which is to determine a probability of successful communication based on the circumstances of sending a message to a communicating counter (for example, based on the day of the week, the date, the time, whether a day is a holiday, etc.), a decision tree algorithm is used. In one embodiment, the algorithm used is a classifier (also called a classifier) ​​for each time slot according to two Classes are respectively associated with a communicating counter and a non-communicating counter. Thus, each time slot will have a probability of belonging to a class for a given counter. In one embodiment, if a time slot has a probability of belonging to a class greater than 0.6, then that time slot belongs to that class. Such an algorithm aims to maximize the success rate of data collection while respecting a collection deadline or to minimize the number of communications required to collect data available in a given communicating counter.

[0058] According to one embodiment, the training of the decision tree-type classifier is based on two sets of information, the first of which includes all the first information previously collected in the SI step, time-stamped (recorded in association with the current date and time), enriched with second pieces of information such as the day of the week, the month of the year, whether the day concerned is a holiday or not, the urban or rural nature of the meter's position identified by the network cell through which it communicates, and the other, second, includes the class to which each piece of information belongs (for example, a class 0 according to which the meter communicates and a class 1 according to which the meter does not communicate).

[0059] Cleverly, the algorithm thus trained is used during step S2, where appropriate (a variant of a "machine learning" type learning mode) to determine the best time slot for each of the communicating meters concerned by a planned and future collection.

[0060] At the end of a learning process thus carried out in step S2, the data collection system 100 has the capacity to determine, in step S3, a collection schedule according to predefined constraints, for example according to calendar constraints of a data collection (which day, which duration, etc.).

[0061] According to one embodiment, the collection scheduling is carried out by proceeding cell by cell and with the aim of subsequently operating a collection in parallel (simultaneously) in each of the relevant cells of the communication network.

[0062] According to one embodiment, and depending on collection organization constraints, such as, for example, scheduling constraints (e.g., during the night from Tuesday to Wednesday, between 9 p.m. and 9 a.m., in an urban area), the scheduling is established so as to collect data from all meters identified as being able to respond at the scheduled collection time and, moreover, as being unable to respond in the moments following the collection. The collection then covers the maximum number of meters in each cell meeting this criterion, using the maximum amount of communication network resources allocated (or dedicated) to the collection for the duration of the collection.

[0063] According to one embodiment, data collection tables are generated so as to establish a collection order (a sequence), with reference to each of the cells A, B and C, of ​​the communication network 1000.

[0064] When a collection schedule has been established by the data collection system 100 by surveying information on the observed behavior of meters concerned by the collection subject to the scheduling, then by determining operating parameters (profile) for each of the meters, and taking into account a defined objective in terms of calendar constraints and / or level of network resources used, the data collection system 100 can execute the data collection during the S4 step, proceeding according to the established sequence and operating in parallel (simultaneously) in the different cells A, B and C of the communication network.

[0065] According to one embodiment and when the collection is completed at the end of step S4, i.e. when the time allowed for collecting data has expired, an optional step S5 is executed by the data collection system 100, aimed at determining the rate of communicating meters that could be read successfully, i.e. the rate of communicating meters that were able to send the data to be collected in response to a collection message from the data collection system 100.

[0066] If the collection rate determined in step S5 is higher than a predefined collection target (step S5, output "yes"), then the process is completed and no further collection operations will be carried out until a new collection phase is scheduled according to the defined collection policy. Conversely, if the collection rate determined in step S5 is insufficient (step S5, output "no"), then the process loops back to step SI, modifying, for example, the parameter T representing the duration of the labeling phase aimed at obtaining new information representative of the behavior of the communicating meters present in the communication network 1000.Each new iteration of the SI step, performed when the data collection completeness rate is deemed insufficient, is considered a learning reinforcement phase aimed in particular at identifying the communicating meters involved in the fact that it was not possible to achieve the target data collection rate. In one embodiment, a new iteration of the SI step is performed with a data collection period for information representative of the meter behavior that is longer than initially defined (for example, longer than seven days).

[0067] According to one embodiment, and considering that certain meters can be identified as being unwilling to respond, for example due to disturbances in electromagnetic transmission communications, a communication strategy with these meters can be advantageously established, for example by not The system does not systematically attempt to communicate for all elementary time periods considered by the data collection system 100, but rather only attempts to collect data from a predefined number of communicating meters to be read during a given elementary time period. In other words, if the system reiterates communication attempts with "difficult-to-reach" meters every five minutes, it will only attempt to reach 70% or 60% of the remaining meters, for example, per five-minute period, to avoid overloading the communication network 1000. Naturally, the percentage of meters targeted by a new attempt can be any number of times, from 0% to 100%, depending on the results previously obtained.

[0068] According to one embodiment, when a new communicating meter is inserted into the communication network 1000, the data collection system 100 plans to operate a data collection schedule identical in terms of behavior to that planned for a communicating meter geographically close to a communicating meter already known to the collection system 100. If such a strategy does not allow data to be collected from this meter without difficulty, then a learning phase must take place according to the described procedure.

[0069] It should be noted that in [Fig.3], the optional nature of step S5 is illustrated by dotted lines.

[0070] Figure 4 schematically illustrates an example of the internal architecture of the data collection system 100, also referred to here as the data collection device or management server of a service provider(s). According to the hardware architecture example shown in Figure 4,[4], the collection system 100 then comprises, connected by a communication bus 120: a processor or CPU (Central Processing Unit) 121; a RAM (Random Access Memory) 122; a ROM (Read Only Memory) 123; a storage unit such as a hard disk drive (or a storage media reader, such as an SD card reader (Secure Digital) 124); at least one communication interface 125 enabling the data collection system 100 to communicate with devices present in the communication network comprising the data collection system 100, including in particular the communicating meters 10a, 10b, 10c, 1Od, 10e, 10f and 1Oh, via the base stations STA-A, STA-B and STA-C.

[0071] The processor 121 is capable of executing instructions loaded into RAM 122 from ROM 123, external memory (not shown), storage media (such as an SD card), or a communication network. When the data collection system 100 is powered on, the processor 121 is capable of reading instructions from RAM 122 and executing them. These instructions form a computer program causing the processor 121 to implement the process described in relation to [Fig.3] or one of its variants.

[0072] All or part of the process implemented by the data collection system 100, or its described variants, can be implemented in software form by executing a set of instructions by a programmable machine, for example a DSP (Digital Signal Processor) or a microcontroller, or implemented in hardware form by a dedicated machine or component, for example a FPGA (Field-Programmable Gate Array) or an ASIC (Application-Specific Integrated Circuit). In general, the data collection system 100 comprises electronic circuitry configured to implement the described process in relation to itself and to devices connected to the communication network 1000.Of course, the 100 data collection system also includes all the elements usually present in a system comprising a control unit and its peripherals, such as a power supply circuit, a power supply monitoring circuit, one or more clock circuits, a reset circuit, input / output ports, interrupt inputs, bus drivers, this list being non-exhaustive.

[0073] The invention is not limited to the embodiments and examples described but relates more broadly to any collection method, operating via a cellular communication network, adapted to the collection of data available in a set of communicating meters and executed in a data collection device connected to this network, and comprising: obtaining initial information representative of the behavior of the meters, then establishing representative parameters (or profiles) of the behavior of each of the communicating meters from this information, in order to establish a data collection schedule of all or part of the meters from the parameters (or profiles) representative of the behavior of each of the meters, and then transmitting data collection messages to the communicating meters according to the schedule which has been established.In particular, a classifier of a different type than a decision tree can be used for the learning phase carried out during step S2.

Claims

Demands

1. A method for collecting, via a cellular communication network (1000), data available in a set of communicating meters (10a, 10b, 10c, lOd, 10e, lOf, 10g, lOh, lOi), the method being carried out in a data collection system (100) connected to said network (1000) and the method comprising: - i) obtain initial information (SI) representative of the behavior of said counters (10a, 10b, 10c, lOd, 10e, lOf, 10g, lOh, lOi), - ii) establish parameters (S2) representative of the behavior of each of the communicating counters (10a, 10b, 10c, lOd, 10e, lOf, 10g, lOh, lOi) from said initial information, - iii) establish a data collection schedule (S3) for all or part of said counters (10a, 10b, 10c, lOd, 10e, lOf, 10g, lOh, lOi) based on said parameters representative of the behavior of each of the counters, and, - iv) transmit data collection messages (S4) to said counters (10a, 10b, 10c, lOd, 10e, lOf, 10g, lOh, lOi) according to said established scheduling.

2. Data collection method according to claim 1, wherein said first information comprises at least: - a cell identifier (Ceid) of said network with reference to a communicating counter identifier (Coid), - a state of obtaining (r) a response from a counter to a message addressed to it with reference to a timestamp (d, h), - a response time (tr) of a counter to a message addressed to it, if applicable, and with reference to a timestamp (d, h).

3. Data collection method according to claim 2, wherein establishing a data collection schedule (S3) from said parameters is carried out with reference, for each of the communicating meters, to a cell identifier (Ceid) from which a communicating meter is accessible.

4. A data collection method according to any one of claims 1 to 3, wherein establishing said parameters representative of the behavior of each of the counters includes a statistical analysis.

5. Data collection method according to claim 4, wherein the statistical analysis is carried out with reference to a maximum collection time.

6. Data collection method according to claim 4, wherein the statistical analysis is carried out with reference to minimal use of said communication network.

7. A data collection method according to any one of claims 1 to 3, wherein establishing said parameters representative of the behavior of each of the counters includes learning by means of a classifier-type module.

8. Data collection method according to claim 7, in learning by means of a classifier-type module is operated with reference to a maximum collection time.

9. Data collection method according to claim 7, in learning by means of a classifier-type module is operated with reference to minimal use of said communication network.

10. Data collection system (100), via a cellular-type communication network (1000), of data available in a set of communicating meters, the collection system comprising electronic circuitry configured to: - i) obtain first information (SI) representative of the behavior of said meters, - ii) establish parameters representative of the behavior (S2) of each of the communicating meters from said first information, - iii) establish a data collection schedule (S3) of all or part of said meters from said parameters representative of the behavior of each of the meters, and, - iv) transmit data collection messages (S4) to said meters according to said established schedule.

11. Data collection system (100) according to claim 10, further comprising circuitry configured to process said first information comprising: - a cell identifier of said network with reference to a communicating meter identifier, - a status of obtaining a response from a meter to a message addressed to it with reference to a timestamp, - a response time of a meter to a message addressed to it, where applicable, and with reference to a timestamp.

12. Data collection system according to any one of claims 10 and 11, comprising electronic circuitry configured to establish a data collection schedule from said parameters with reference, for each of the communicating counters, to a cell identifier from which a communicating counter is accessible.

13. Data collection system according to any one of claims 10 to 12, further comprising electronic circuitry configured to establish said parameters representative of the behavior of each of the counters by performing a statistical analysis.

14. Data collection system according to claim 13, further comprising electronic circuitry configured to perform statistical analysis with reference to a maximum collection time.

15. Data collection system according to claim 13, further comprising electronic circuitry configured to perform statistical analysis with reference to minimal use of said communication network.

16. A data collection system according to any one of claims 10 to 12, further comprising electronic circuitry configured to establish said parameters representative of the behavior of each of the counters by means of a classifier-type module.

17. Data collection system according to claim 16, further comprising electronic circuitry configured to perform said learning by means of a classifier-type module, with reference to a maximum collection time.

18. Data collection system according to claim 16, further comprising electronic circuitry configured to perform said learning by means of a classifier-type module, with reference to minimal use of said communication network.

19. Computer program product characterized in that it comprises program code instructions to execute the steps of the method according to any one of claims 1 to 9, when said program is executed by a processor of said data collection system

20. Information storage medium comprising a computer program product according to claim 19.

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