A communication network intelligent scheduling system based on big data integrated analysis

Abstract

The present invention discloses a communication network intelligent scheduling system based on big data integrated analysis, relates to the technical field of communication network scheduling, and comprises a collaborative center, wherein the collaborative center is communicatively connected with a network point construction module, a communication network monitoring module and a working machine adjustment module; the network point construction module is used to obtain a production data indicator set through production data, and generate a production weight set according to the production data indicator set; obtain the performance weight of the working machine according to the communication network data; generate a working machine group according to the production weight set and the performance weight, and generate a working network point according to the working machine group and the production data indicator set; the communication network monitoring module is used to obtain the network vacancy rate of the working network point; the working machine adjustment module is used to mark the working machine group of the working network point according to the network vacancy rate, and generate and implement corresponding working machine adjustment measures according to the marking.

Description

Technical Field

[0001] The present invention relates to the technical field of communication network scheduling, and in particular to a communication network intelligent scheduling system based on big data integrated analysis. Background Art

[0002] Communication networks play a vital role in the manufacturing industry. They provide key infrastructure and technical support for manufacturing companies, helping to achieve automation, integration and intelligence of the production process.

[0003] In traditional manufacturing processes, fixed computer equipment is usually used to control the production process. If production is reduced or stopped, it is easy to cause a waste of communication network resources. If production demand increases suddenly, but the communication network responsible for the production is not expanded or upgraded, it is easy to make production efficiency low. How to intelligently allocate communication network resources in the manufacturing process is something we urgently need to consider. Summary of the invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide a communication network intelligent scheduling system based on big data integrated analysis.

[0005] The object of the present invention can be achieved by the following technical solutions: A communication network intelligent scheduling system based on big data integrated analysis includes a collaboration center, the collaboration center is communicatively connected with a network construction module, a communication network monitoring module and a working machine adjustment module;

[0006] The network point construction module is used to obtain a production data indicator set through production data, and generate a production weight set according to the production data indicator set; obtain the performance weight of the working machine according to the communication network data; allocate the working machines according to the production weight set and the performance weight, generate a working machine group, and generate a working network point according to the working machine group and the production data indicator set, and then obtain an idle machine group according to the unallocated working machines;

[0007] The communication network monitoring module is used to obtain the network vacancy rate of the working network point;

[0008] The working machine adjustment module is used to mark the working machine groups of the working outlets according to the network vacancy rate, and the marks include warning groups to be added and warning groups to be reduced; if it is marked as a warning group to be added, a reserve machine group is generated, and the communication network capacity of the warning group to be added is expanded through the reserve machine group; if it is marked as a warning group to be reduced, a machine group to be eliminated is obtained, and the communication network utilization rate of a single working machine in the warning group to be reduced is improved through the machine group to be eliminated.

[0009] Furthermore, the process of generating working points includes:

[0010] The production data refers to various data information in the production process;

[0011] Generate a production data indicator set according to the production data = {production data segment 1, production data segment 2, ..., production data segment n}; the production data segment refers to various data information at different stages in the production and manufacturing process;

[0012] Obtain corresponding production weights according to the production data segments in the production data indicator set to generate a production weight set;

[0013] The communication network data refers to the performance data information of the communication network guiding the production process through transmission data;

[0014] Generate a communication data indicator set according to the communication network data = {communication data segment 1, communication data segment 2, ... communication data segment m}; wherein the communication data segment refers to various performance data information of the communication network;

[0015] Assign performance values ​​to several working machines in the communication network according to the communication data indicator set, obtain the performance weights of the working machines, and generate a performance weight set;

[0016] According to the production weights corresponding to the production data segments and the performance weights of the working machines, the production data segments are allocated to working machines, working machine groups are generated, and work points are generated according to the working machine groups and the production data segments.

[0017] Furthermore, the corresponding production weights are obtained according to the production data segments in the production data indicator set, and the process of generating the production weight set includes:

[0018] Through artificial intelligence, machine learning and Internet of Things technologies, the production and manufacturing process is intelligently simulated according to the production data indicator set, and the production data in the production data indicator set is segmented and valued to generate a process weight set = {process weight 1, process weight 2, ..., process weight n};

[0019] It should be noted that process weight 1 ≠ process weight 2 ≠ process weight n, and process weight 1 + process weight 2 + ... + process weight n = 1; at the same time, the process weight and the production data segment are in a one-to-one correspondence;

[0020] Set the production impact coefficient k, and generate an increasing function INCREASE_F(k) based on the production impact coefficient k;

[0021] Generate a production weight set according to the process weight set = {production weight 1, production weight 2, ..., production weight n}; wherein the production weight i = process weight i × INCREASE_F (k);

[0022] It can be seen from the one-to-one correspondence between process weights and production data segments that production weights and production data segments also have a one-to-one correspondence.

[0023] Furthermore, the process of obtaining the performance weight of the working machine and generating a performance weight set includes:

[0024] Define a performance conversion assignment function E_TURN_M(mpd_i), where mpd_i refers to communication data segment i;

[0025] Acquire a communication data indicator set corresponding to the working machine, and assign values ​​to the communication data segments in the communication data indicator set through a performance conversion and assignment function, and calculate the performance weight of the working machine according to the communication data indicator set after conversion and assignment;

[0026] A performance weight set = {performance weight 1, performance weight 2, ..., performance weight p} is generated according to the performance weights of a number of working machines.

[0027] Furthermore, according to the production weights corresponding to the production data segments and the performance weights of the working machines, the production data segments are assigned working machines to generate working machine groups, and the process of generating working network points according to the working machine groups and the production data segments includes:

[0028] Set the expansion threshold excp and the limit expansion threshold EXCP;

[0029] Obtain the production weight corresponding to the production data segment, and simultaneously select several performance weights so that the production weight i×(1+excp)≤performance weight 1+…+performance weight q≤production weight i×(1+EXCP);

[0030] Obtain the MAC address of the working machine corresponding to the performance weight in the "performance weight 1+...+performance weight q", and generate a working machine group WORK_E (MAC address i-1,..., MAC address iq);

[0031] The production data segment corresponding to the production weight is obtained and matched with the working machine group, thereby generating a working point DOT (production data segment i) = {WORK_E (MAC address i-1, ..., MAC address iq)}.

[0032] Furthermore, the process of obtaining the idle machine group includes:

[0033] The working machines that are not allocated in the communication network are recorded as idle working machines, and an idle machine group is generated according to the MAC addresses of the idle working machines.

[0034] Furthermore, the process of obtaining the network vacancy rate of the working outlets includes:

[0035] Set the device activity and status determination value ke;

[0036] Regularly obtain the performance weights of the working machines in the working machine group and generate a status index;

[0037] If the state index is less than the device activity for ke consecutive times, the working machine is judged to be a normal working machine; otherwise, the working machine is judged to be an active working machine;

[0038] Count the number of active working machines, recorded as A_COUNT;

[0039] Count the number of common working machines, recorded as D_COUNT;

[0040]

[0041] Furthermore, the process of setting a mark for the working machine group includes:

[0042] Set the vacancy warning threshold and overload warning threshold, and the overload warning threshold is smaller than the vacancy warning threshold;

[0043] If the network vacancy rate is less than the overload warning threshold, the working machine group in the working network point will be marked as a warning group to be added; if the network vacancy rate is greater than or equal to the overload warning value and less than the vacancy warning threshold, no operation is required; if the network vacancy rate is greater than or equal to the vacancy warning threshold, the working machine group in the working network point will be marked as a warning group to be reduced.

[0044] Furthermore, if the working machine group is marked as a warning machine group to be added, a warning processing cycle is set, and the warning processing cycle includes an early processing period, a middle processing period and a late processing period;

[0045] Obtain the performance growth rate of the units to be added for warning according to the warning processing cycle;

[0046] Said

[0047] Among them, count is the total number of working machines in the warning group to be added, and the performance weight i pre To obtain the performance weight of the working machine in the early stage of processing, the performance weight i current To obtain the performance weight of the working machine in the middle of processing, the performance weight i after Obtain the performance weight of the worker for later processing;

[0048] Obtain the performance increase requirement value according to the performance growth rate, and then select the standby working machines in the idle machine group according to the performance increase requirement value to generate a standby machine group;

[0049] The MAC address of the standby machine group is obtained, and the MAC address is added to the warning machine group to be added, so as to complete the expansion of the communication network of the warning machine group to be added.

[0050] Furthermore, if the working unit group is marked as a warning unit to be reduced, a warning processing cycle is set, and the performance reduction rate of the warning unit to be reduced is obtained according to the warning processing cycle;

[0051] Obtain a performance reduction requirement value according to the performance reduction rate, and then obtain working machines to be eliminated according to the performance reduction requirement value to generate a group of machines to be eliminated;

[0052] Obtain the MAC address of the group of machines to be eliminated, transfer the communication services of the group of machines to be eliminated to other working machines in the warning group to be reduced, and then remove the group of machines to be eliminated from the warning group to be reduced according to the MAC address, thereby improving the communication network utilization rate of a single working machine in the warning group to be reduced.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. By assigning values ​​to different stages in the production process and assigning performance values ​​to the working machines at the same time, and then allocating working machines with different performances according to the values ​​assigned at different stages, the communication resources of the working machines can be reasonably utilized.

[0055] 2. By regularly monitoring the utilization rate of the working machine, it is determined whether to expand or reduce the resources of the current communication network, which can effectively improve the communication utilization rate of a single working machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0058] like Figure 1 As shown, a communication network intelligent scheduling system based on big data integrated analysis includes a collaboration center, and the collaboration center is communicatively connected to a network construction module, a communication network monitoring module and a working machine adjustment module;

[0059] The network point construction module is used to obtain a production data indicator set through production data, and generate a production weight set according to the production data indicator set; obtain the performance weight of the working machine according to the communication network data; allocate the working machines according to the production weight set and the performance weight, generate a working machine group, and generate a working network point according to the working machine group and the production data indicator set, and then obtain an idle machine group according to the unallocated working machines;

[0060] The communication network monitoring module is used to obtain the network vacancy rate of the working network point;

[0061] The working machine adjustment module is used to mark the working machine group of the working network point according to the network vacancy rate, and the mark includes the warning group to be added and the warning group to be reduced; if it is marked as the warning group to be added, a reserve machine group is generated, and the communication network capacity of the warning group to be added is expanded through the reserve machine group; if it is marked as the warning group to be reduced, a machine group to be eliminated is obtained, and the communication network utilization rate of a single working machine in the warning group to be reduced is improved through the machine group to be eliminated;

[0062] It should be noted that the communication network is composed of several working units;

[0063] Specifically, the process of establishing a work outlet includes:

[0064] The production data refers to various data information in the production process, such as production volume data information, production efficiency data information, production quality data information, equipment status data information, etc.;

[0065] Generate a production data indicator set PD = {production data segment 1, production data segment 2, ..., production data segment n} according to the production data; wherein n is a positive integer;

[0066] The production data segment refers to various data information at different stages in the production process; for example, production data segment 1 refers to various data information at the initial stage, production data segment 2 refers to various data information at the second stage, ..., production data segment n refers to various data information at the final stage;

[0067] Obtain corresponding production weights according to the production data segments in the production data indicator set to generate a production weight set;

[0068] The process of obtaining the production weight set is:

[0069] Through artificial intelligence, machine learning and Internet of Things technologies, the production process is intelligently simulated according to the production data indicator set, and the production data in the production data indicator set is segmented and assigned values ​​to generate a process weight set FLD = {process weight 1, process weight 2, ..., process weight n}, where n is a positive integer;

[0070] It should be noted that process weight 1 ≠ process weight 2 ≠ process weight n, and process weight 1 + process weight 2 + ... + process weight n = 1; at the same time, the process weight and the production data segment are in a one-to-one correspondence, that is, the process weight i corresponds to the production data segment i, where i is greater than 0 and i is less than n+1;

[0071] Generate a production weight set MD = {production weight 1, production weight 2, ..., production weight n} according to the process weight set FLD, where n is a positive integer;

[0072] The process of obtaining the production weight in the production weight set is as follows:

[0073] Set the production impact coefficient k, and generate an increasing function INCREASE_F(k) based on the production impact coefficient k;

[0074] The production weight i=process weight i×INCREASE_F(k);

[0075] It can be seen from the one-to-one correspondence between process weight and production data segment that production weight and production data segment also have a one-to-one correspondence, that is, production weight i corresponds to production data segment i;

[0076] The communication network data refers to the various performance data information in the production process guided by the communication network through transmission data, such as network flow data information, communication record data information, network performance data information, network equipment data information, etc.;

[0077] Generate a communication data indicator set MPD={communication data segment 1, communication data segment 2, ... communication data segment m} according to the communication network data, where n is a positive integer;

[0078] The communication data segment refers to various performance data information of the communication network, for example, communication data segment 1 refers to network flow data information, communication data segment 2 refers to communication record data information, ... communication data segment m refers to network equipment data information;

[0079] Assign performance values ​​to several working machines in the communication network according to the communication data indicator set, obtain the performance weights of the working machines, and generate a performance weight set;

[0080] The process of obtaining the performance weight of the working machine and generating the performance weight set is as follows:

[0081] Define a performance conversion assignment function E_TURN_M(mpd_i), where mpd_i refers to communication data segment i;

[0082] Obtain the communication data indicator set corresponding to the working machine, and assign values ​​to the communication data segments in the communication data indicator set through the performance conversion assignment function, and calculate the performance weight of the working machine according to the converted and assigned communication data indicator set = E_TURN_M(mpd_1)+E_TURN_M(mpd_2)+…+E_TURN_M(mpd_p);

[0083] Generate a performance weight set EP={performance weight 1, performance weight 2, ..., performance weight p} according to the performance weights of several working machines, where n is a positive integer;

[0084] According to the production weights corresponding to the production data segments and the performance weights of the working machines, the production data segments are assigned working machines to generate working machine groups, and working points are generated according to the working machine groups and the production data segments;

[0085] Set the expansion threshold excp and the limit expansion threshold EXCP;

[0086] Obtain the production weight corresponding to the production data segment, and simultaneously select several performance weights so that the production weight i×(1+excp)≤performance weight 1+…+performance weight q≤production weight i×(1+EXCP);

[0087] Obtain the MAC address of the working machine corresponding to the performance weight in the "performance weight 1+...+performance weight q", and generate a working machine group WORK_E (MAC address i-1,..., MAC address iq);

[0088] Obtain the production data segment corresponding to the production weight, match it with the working machine group, and thus generate the working point DOT (production data segment i) = {WORK_E (MAC address i-1, ..., MAC address iq)};

[0089] At the same time, the unassigned work machines in the communication network are recorded as idle work machines, and an idle machine group WORK_NULL (MAC address 1, ..., MAC address 2) is generated according to the MAC addresses of the idle work machines;

[0090] Specifically, the process of obtaining the network vacancy rate of work outlets includes:

[0091] Set the device activity ALive and status determination value ke;

[0092] Regularly obtain the performance weights of the working machines in the working machine group and generate a status index;

[0093] Said

[0094] Among them, CURRENT_EM is the currently obtained performance weight, and PRE_EM is the performance weight obtained last time;

[0095] If the state index is less than the device activity ALive for consecutive ke times, the working machine is judged to be a normal working machine; otherwise, the working machine is judged to be an active working machine;

[0096] Count the number of active working machines, recorded as A_COUNT;

[0097] Count the number of common working machines, recorded as D_COUNT;

[0098]

[0099] Specifically, the process of marking the working machine groups of the working network points according to the network vacancy rate includes:

[0100] Set the vacancy warning threshold D_LINE and the overload warning threshold A_LINE, and the overload warning threshold A_LINE is smaller than the vacancy warning threshold D_LINE;

[0101] If the network vacancy rate is less than the overload warning threshold A_LINE, the working machine group in the working network point is marked as a warning group to be added;

[0102] If the network vacancy rate is greater than or equal to the overload warning value A_LINE and less than the vacancy warning threshold D_LINE, no action is required;

[0103] If the network vacancy rate is greater than or equal to the vacancy warning threshold D_LINE, the working machine group in the working network point is marked as a warning group to be reduced;

[0104] Specifically, if the unit is marked as a warning unit to be added, a reserve unit group is selected from the idle unit group, and the process of expanding the communication network of the warning unit to be added through the reserve unit group includes:

[0105] Set an early warning processing cycle ROUND_TIME, which includes a pre-processing period ROUND_TIME_PRE, a mid-processing period ROUND_TIME_CURRENT, and a post-processing period ROUND_TIME_AFTER;

[0106] Obtain the performance growth rate of the units to be added for warning according to the warning processing cycle ROUND_TIME;

[0107] Said

[0108] Among them, count is the total number of working machines in the warning group to be added, and the performance weight i preTo process the previous ROUND_TIME_PRE, obtain the performance weight of the worker. The performance weight i current To process the mid-term ROUND_TIME_CURRENT, obtain the performance weight of the worker. The performance weight i after To process the later ROUND_TIME_AFTER, obtain the performance weight of the worker;

[0109] According to the performance growth rate, the performance increase requirement value is obtained, and then according to the performance increase requirement value, suitable idle working machines are selected from the idle machine group and recorded as standby working machines to generate a standby machine group;

[0110] Said Among them, the performance weight i refers to the performance weight of the working machine in the warning unit to be added in the later ROUND_TIME_AFTER processing period, then The performance weight i is the sum of the performance weights of several working machines in the warning unit to be added in the later ROUND_TIME_AFTER processing period;

[0111] Select a number of idle working machines from the idle machine group, obtain the performance weights of the idle working machines, and make Performance weight i = performance increase requirement value" holds true; wherein, "performance weight i" is the performance weight of the idle working machine; the idle working machine is recorded as a standby working machine;

[0112] Obtain the MAC address of the standby machine group, add the MAC address to the warning machine group to be added, and complete the expansion of the communication network of the warning machine group to be added;

[0113] Specifically, if it is marked as a warning unit to be reduced, a group of units to be eliminated is obtained, and the process of improving the communication network utilization rate of a single working unit in the warning unit to be reduced by the group of units to be eliminated includes:

[0114] According to the warning processing cycle ROUND_TIME, the performance reduction rate of the warning unit to be reduced is obtained;

[0115] Said

[0116] According to the performance reduction rate, the performance reduction demand value is obtained, and then according to the performance reduction demand value, suitable working machines are selected from the warning groups to be reduced, recorded as the working machines to be eliminated, and a group of machines to be eliminated is generated;

[0117] Said

[0118] Select several working machines from the waiting warning group, obtain the performance weights of the working machines, and make the Performance weight i = performance reduction requirement value" is established; wherein, "performance weight i" is the performance weight of several working machines selected from the warning group to be reduced; the working machine is marked as a working machine to be eliminated;

[0119] Obtain the MAC address of the group of machines to be eliminated, transfer the communication services of the group of machines to be eliminated to other working machines in the warning group to be reduced, and then remove the group of machines to be eliminated from the warning group to be reduced according to the MAC address, thereby improving the communication network utilization rate of a single working machine in the warning group to be reduced.

[0120] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A communication network intelligent scheduling system based on big data integrated analysis, including a coordination center, characterized in that: The collaborative center is connected to the network construction module, the communication network monitoring module and the working machine adjustment module by communication; The network construction module is used to obtain a production data indicator set through production data, and generate a production weight set according to the production data indicator set; obtain the performance weight of the working machine according to the communication network data; According to the production weight set and performance weight, the working machines are allocated to generate working machine groups, and working points are generated according to the working machine groups and production data indicator sets, and then the idle machine groups are obtained according to the unallocated working machines; Generate a production data indicator set = {production data segment 1, production data segment 2, ..., production data segment n} according to the production data; the production data segment refers to various data information at different stages in the production and manufacturing process; assign values ​​to the production data segments in the production data indicator set to generate a production weight set = {production weight 1, production weight 2, ..., production weight n}; Assign performance values ​​to several working machines in the communication network, obtain the performance weight of each working machine, and generate a performance weight set = {performance weight 1, performance weight 2, ..., performance weight p}; According to the production weights corresponding to the production data segments and the performance weights of the working machines, the production data segments are assigned working machines, working machine groups are generated, and the process of generating working network points according to the working machine groups and the production data segments includes: Set the expansion threshold excp and the limit expansion threshold EXCP; Obtain the production weight corresponding to the production data segment, and simultaneously select several performance weights so that the production weight i×(1+excp)≤performance weight 1+…+performance weight q≤production weight i×(1+EXCP); Obtain the MAC address of the work machine corresponding to the performance weight in the "performance weight 1+...+performance weight q", and generate a work machine group WORK_E (MAC address i-1,..., MAC address iq); Obtain the production data segment corresponding to the production weight, match it with the working machine group, and thus generate the working point DOT (production data segment i) = {WORK_E (MAC address i-1, ..., MAC address iq)}; The communication network monitoring module is used to obtain the network vacancy rate of the working network point; The working machine adjustment module is used to mark the working machine groups of the working outlets according to the network vacancy rate, and the marks include warning groups to be added and warning groups to be reduced; if it is marked as a warning group to be added, a reserve machine group is screened out from the idle machine group, and the communication network capacity of the warning group to be added is expanded through the reserve machine group; if it is marked as a warning group to be reduced, a machine group to be eliminated is obtained, and the communication network utilization rate of a single working machine in the warning group to be reduced is improved through the machine group to be eliminated.

2. A communication network intelligent scheduling system based on big data integrated analysis according to claim 1, characterized in that: The process of generating working points includes: The production data refers to various data information in the production process; Generate a production data indicator set according to the production data = {production data segment 1, production data segment 2, ..., production data segment n}; the production data segment refers to various data information at different stages in the production and manufacturing process; Obtain corresponding production weights according to the production data segments in the production data indicator set to generate a production weight set; The communication network data refers to the performance data information of the communication network guiding the production process through transmission data; Generate a communication data indicator set according to the communication network data = {communication data segment 1, communication data segment 2, ... communication data segment m}; wherein the communication data segment refers to various performance data information of the communication network; Assign performance values ​​to several working machines in the communication network according to the communication data indicator set, obtain the performance weights of the working machines, and generate a performance weight set; According to the production weights corresponding to the production data segments and the performance weights of the working machines, the production data segments are allocated to working machines, working machine groups are generated, and work points are generated according to the working machine groups and the production data segments.

3. A communication network intelligent scheduling system based on big data integrated analysis according to claim 2, characterized in that: The process of obtaining corresponding production weights according to the production data segments in the production data indicator set and generating the production weight set includes: Through artificial intelligence, machine learning and Internet of Things technologies, the production and manufacturing process is intelligently simulated according to the production data indicator set, and the production data in the production data indicator set is segmented and valued to generate a process weight set = {process weight 1, process weight 2, ..., process weight n}; It should be noted that process weight 1 ≠ process weight 2 ≠ process weight n, and process weight 1 + process weight 2 + ... + process weight n = 1; at the same time, the process weight and the production data segment are in a one-to-one correspondence; Set the production impact coefficient k, and generate an increasing function INCREASE_F(k) based on the production impact coefficient k; Generate a production weight set according to the process weight set = {production weight 1, production weight 2, ..., production weight n}; wherein the production weight i = process weight i × INCREASE_F (k); It can be seen from the one-to-one correspondence between process weights and production data segments that production weights and production data segments also have a one-to-one correspondence.

4. A communication network intelligent scheduling system based on big data integrated analysis according to claim 3, characterized in that: The process of obtaining the performance weight of the working machine and generating the performance weight set includes: Define a performance conversion assignment function E_TURN_M(mpd_i), where mpd_i refers to communication data segment i; Acquire a communication data indicator set corresponding to the working machine, and assign values ​​to the communication data segments in the communication data indicator set through a performance conversion and assignment function, and calculate the performance weight of the working machine according to the communication data indicator set after conversion and assignment; A performance weight set = {performance weight 1, performance weight 2, ..., performance weight p} is generated according to the performance weights of a number of working machines.

5. A communication network intelligent scheduling system based on big data integrated analysis according to claim 4, characterized in that: The process of obtaining an idle machine group includes: The working machines that are not allocated in the communication network are recorded as idle working machines, and an idle machine group is generated according to the MAC addresses of the idle working machines.

6. A communication network intelligent scheduling system based on big data integrated analysis according to claim 5, characterized in that: The process of obtaining the network vacancy rate of working outlets includes: Set the device activity and status determination value ke; Regularly obtain the performance weights of the working machines in the working machine group and generate a status index; If the state index is less than the device activity for ke consecutive times, the working machine is judged to be a normal working machine; otherwise, the working machine is judged to be an active working machine; Count the number of active working machines, recorded as A_COUNT; Count the number of common working machines, recorded as D_COUNT; 7. A communication network intelligent scheduling system based on big data integrated analysis according to claim 6, characterized in that: The process of setting a mark on the working machine group includes: Set the vacancy warning threshold and overload warning threshold, and the overload warning threshold is smaller than the vacancy warning threshold; If the network vacancy rate is less than the overload warning threshold, the working machine group in the working network point will be marked as a warning group to be added; if the network vacancy rate is greater than or equal to the overload warning value and less than the vacancy warning threshold, no operation is required; if the network vacancy rate is greater than or equal to the vacancy warning threshold, the working machine group in the working network point will be marked as a warning group to be reduced.

8. The communication network intelligent scheduling system based on big data integrated analysis according to claim 7 is characterized in that: If the working unit group is marked as a unit to be added for warning, a warning processing cycle is set, and the performance growth rate of the unit to be added for warning is obtained according to the warning processing cycle; Obtain the performance increase requirement value according to the performance growth rate, and then select the standby working machines in the idle machine group according to the performance increase requirement value to generate a standby machine group; The MAC address of the standby machine group is obtained, and the MAC address is added to the warning machine group to be added, so as to complete the expansion of the communication network of the warning machine group to be added.

9. A communication network intelligent scheduling system based on big data integrated analysis according to claim 8, characterized in that: If the working unit group is marked as a warning unit to be reduced, a warning processing cycle is set, and the performance reduction rate of the warning unit to be reduced is obtained according to the warning processing cycle; Obtain a performance reduction requirement value according to the performance reduction rate, and then obtain working machines to be eliminated according to the performance reduction requirement value to generate a group of machines to be eliminated; Obtain the MAC address of the group of machines to be eliminated, transfer the communication services of the group of machines to be eliminated to other working machines in the warning group to be reduced, and then remove the group of machines to be eliminated from the warning group to be reduced according to the MAC address, thereby improving the communication network utilization rate of a single working machine in the warning group to be reduced.

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